This document summarizes research on exploiting weather conditions, specifically wind, to minimize transportation energy usage. The research found that:
1) Tailwinds can significantly reduce energy usage compared to headwinds or no wind.
2) For a trip from Phoenix to Chicago, the most direct route was found to use the least energy despite not utilizing winds as much as alternative routes.
3) Comparing energy usage of the same route on different days found large variations depending on wind conditions, showing potential for energy savings by choosing travel days strategically.
Determination of Kite forces for ship propulsionGeorge Dadd
This document presents a method for predicting kite forces for use in ship propulsion by kites. It describes modeling kite trajectories as figure-eight shapes and using these to calculate kite velocity, force and performance over time. Simulation results show a 300m^2 kite could provide an average propulsive force of 16.7 tonnes in 6.18 m/s wind. The model is used to study how parameters like aspect ratio, elevation angle and trajectory shape impact propulsive performance. Validation with experimental data shows favorable agreement.
This document discusses different types of climate models and their components and uses. It begins by defining climate models as mathematical representations of the climate system based on physical principles. It then describes four main types of climate models: (1) energy balance models which use simplified equations to model global or regional energy budgets, (2) Earth system models of intermediate complexity which have more complex representations than EBMs but less than GCMs, (3) general circulation models which use 3D grids to model interactions between components at a regional scale, and (4) emulators which use statistical techniques to link climate drivers to impacts. The document also discusses key components of models, their development over time, grid size considerations, and how models are used
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
1) CGCMs are coupled general circulation models that combine atmospheric and oceanic GCMs to allow the lower boundary conditions of the atmosphere to be determined interactively by ocean processes.
2) CGCM3.1 is a third generation CGCM developed by CCCMA that runs at T47 and T63 resolutions with ocean grids of roughly 1.4x0.9 degrees and 1.85 degrees respectively.
3) A study assessed CGCM3.1's wind fields in the Persian Gulf, finding it generally underestimated wind speeds compared to ECMWF reanalysis data.
Glider Advancements in Efficiency: Enhancing Factors Necessary for Ocean-Wide...LiamRamsay
Research Paper written for the Autonomous Underwater Vehicle (AUV) Teledyne Marine Slocum Glider. Paper was published for the MTS/IEEE OCEANS Conference 2015 Washington D.C.
This document summarizes a presentation on climate data and projections focusing on limiting global warming to less than 2 degrees Celsius. It discusses the work of GERICS (the Climate Service Center Germany) in developing solutions for regional climate modeling, impacts analysis, and climate adaptation toolkits. Key points covered include:
- GERICS' interdisciplinary approach to regional climate modeling, impacts assessment, and stakeholder engagement.
- The development of adaptation toolkits for cities, companies, and other sectors to facilitate climate risk assessment and planning.
- An overview of the presentation, covering topics like climate modeling techniques, accessing climate projections data, and visualizing and analyzing climate information.
Climate models are tools used in climate research that range in complexity from simple zero-dimensional energy balance models to complex three-dimensional general circulation models. They work by solving equations that conserve mass, momentum, energy and other quantities in grid boxes. Climate models are evaluated by comparing their results to observations. They are used for applications such as detecting and attributing causes of climate change, making projections of future climate change, and studying past climates.
Determination of Kite forces for ship propulsionGeorge Dadd
This document presents a method for predicting kite forces for use in ship propulsion by kites. It describes modeling kite trajectories as figure-eight shapes and using these to calculate kite velocity, force and performance over time. Simulation results show a 300m^2 kite could provide an average propulsive force of 16.7 tonnes in 6.18 m/s wind. The model is used to study how parameters like aspect ratio, elevation angle and trajectory shape impact propulsive performance. Validation with experimental data shows favorable agreement.
This document discusses different types of climate models and their components and uses. It begins by defining climate models as mathematical representations of the climate system based on physical principles. It then describes four main types of climate models: (1) energy balance models which use simplified equations to model global or regional energy budgets, (2) Earth system models of intermediate complexity which have more complex representations than EBMs but less than GCMs, (3) general circulation models which use 3D grids to model interactions between components at a regional scale, and (4) emulators which use statistical techniques to link climate drivers to impacts. The document also discusses key components of models, their development over time, grid size considerations, and how models are used
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
1) CGCMs are coupled general circulation models that combine atmospheric and oceanic GCMs to allow the lower boundary conditions of the atmosphere to be determined interactively by ocean processes.
2) CGCM3.1 is a third generation CGCM developed by CCCMA that runs at T47 and T63 resolutions with ocean grids of roughly 1.4x0.9 degrees and 1.85 degrees respectively.
3) A study assessed CGCM3.1's wind fields in the Persian Gulf, finding it generally underestimated wind speeds compared to ECMWF reanalysis data.
Glider Advancements in Efficiency: Enhancing Factors Necessary for Ocean-Wide...LiamRamsay
Research Paper written for the Autonomous Underwater Vehicle (AUV) Teledyne Marine Slocum Glider. Paper was published for the MTS/IEEE OCEANS Conference 2015 Washington D.C.
This document summarizes a presentation on climate data and projections focusing on limiting global warming to less than 2 degrees Celsius. It discusses the work of GERICS (the Climate Service Center Germany) in developing solutions for regional climate modeling, impacts analysis, and climate adaptation toolkits. Key points covered include:
- GERICS' interdisciplinary approach to regional climate modeling, impacts assessment, and stakeholder engagement.
- The development of adaptation toolkits for cities, companies, and other sectors to facilitate climate risk assessment and planning.
- An overview of the presentation, covering topics like climate modeling techniques, accessing climate projections data, and visualizing and analyzing climate information.
Climate models are tools used in climate research that range in complexity from simple zero-dimensional energy balance models to complex three-dimensional general circulation models. They work by solving equations that conserve mass, momentum, energy and other quantities in grid boxes. Climate models are evaluated by comparing their results to observations. They are used for applications such as detecting and attributing causes of climate change, making projections of future climate change, and studying past climates.
Comparison of Latent Heat Flux Using Aerodynamic Methods and Using the Penman...Ramesh Dhungel
This document compares methods for calculating latent heat flux using aerodynamic and Penman-Monteith methods with satellite data. It finds that using surface temperature instead of air temperature in the Penman-Monteith method, as well as fully parameterizing the method, results in more accurate calculations of latent heat flux, especially in sparsely vegetated areas where surface and air temperatures differ more. The maximum error found when simplifying the Penman-Monteith method was 56 W/m^2. The study emphasizes the advantage of using separate aerodynamic equations over the combined Penman-Monteith equation when surface temperature is significantly warmer than air temperature.
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...IOSR Journals
This document summarizes a study that simulated wind power dynamics for electricity production in Nassiriyah District, Iraq. The study measured wind speed data from 2010-2013 at 10m altitude and used this to mathematically model and predict important wind energy parameters. It found that the minimum altitude for feasible wind speed (≥5m/s) for power production was 44m for a friction coefficient of 3.0 and 32m for a friction coefficient of 4.0. Weibull distribution analysis showed that the percentage of days with mean wind speeds ≥5m/s increased with higher altitudes and friction coefficients, making wind energy more viable at greater heights.
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
This document provides a summary of Brian Medeiros' career and qualifications. It includes his educational background, earning a PhD in Atmospheric & Oceanic Sciences from UCLA in 2007. It lists his professional experience as a Project Scientist at NCAR since 2009. It also provides details of his research focus on cloud-climate interactions in general circulation models, awards, teaching experience, research grants, publications, and professional service and involvement.
1) eMAST aims to integrate data from TERN and other sources using data assimilation tools to enable ecosystem modeling, optimization, and analysis of questions in carbon, water, climate change, fire, and biodiversity.
2) eMAST is delivering high-resolution climate, canopy, water use, and productivity data products as well as tools for data interpolation, modeling, and assimilation.
3) ePiSaT is an R package that uses OzFlux tower data, climate data from eMAST, and satellite data to model and map gross primary productivity across Australia.
This document summarizes wind data collected over one year at Sitakundu, Bangladesh. The data was analyzed to determine monthly average wind speeds, daily variations, frequency distributions, and instantaneous wind velocities. Irregular wind behavior was observed in August and September, with higher gusts and more variable speeds. Weibull analysis was used to calculate shape and scale factors each month. Factors were outside normal ranges for August and September, likely due to high magnitude wind gusts during this period. Modifications were suggested to better model local wind conditions, including using mean hourly rather than 10-minute wind speeds.
Hurricanes and Global Warming- Dr. Kerry EmanuelJohn Atkeison
Dr. Kerry Emanuel explains how Global Warming increased the power of hurricanes. Hurricane Katrina is discussed, with the conclusion that Katrina probably would not have had the power to break the New Orleans levees in a pre-Global Warming world. April 2009 webinar presented by the Southern Allicance for Clean Energy (http://www.cleanenergy.org/) and the Gulf Restoration Network (http://healthygulf.org/) SlideCast by John Atkeison of the Alliance for Affordable Energy. There is a very small amount of phone noise.
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
This document discusses a study analyzing the impact of climate change on precipitation characteristics in Guwahati, India using an Earth System Model. It summarizes the use of statistical downscaling with multiple linear regression to project future precipitation data. Predictors with the highest correlation to total monthly precipitation, maximum monthly precipitation, and number of dry days were selected from the ESM dataset. The downscaled results will be used for flood frequency analysis to project precipitation levels and dry days under different return periods.
The study aimed to calculate the percent energy recovery of a runner on flat ground, uphill at a 4 degree slope, and downhill at a -4 degree slope using a 3D accelerometer. The researchers found no statistically significant difference in mean percent recovery between the conditions. Specifically, the mean recovery was 27.16% for flat ground, 28.6% for uphill, and 27.8% for downhill. While the method showed potential for measuring energy exchange, the current data did not provide definitive conclusions about recovery differences on varying slopes due to high p-values.
This study developed a method to represent 2D seismic velocity fields using 2D Haar wavelet series. The method simplifies previous approaches using polynomials or 1D wavelet series. Seismic velocity fields from 5 geological models were approximated using the 2D Haar wavelet algorithm. While the number of coefficients was initially high, generating coefficient averages over subintervals reduced the total coefficients by up to 90%, simplifying the representations. The findings support expanding this work to seismic reflection travel time modeling, ray tracing, tomography, and migration using the wavelet parameterization.
Descriptive modeling is a type of mathematical modeling that describes major historical events and relationships between elements that created those events. Descriptive climate models typically represent significant components of the climate system like the atmosphere, oceans, land, and their interactions. One strength is they can isolate factors contributing to climate change, like how changes in precipitation and temperature affect agricultural yields. Current examples include using descriptive models to simulate 20th century climate trends and the decrease in Arctic sea ice cover since 1960.
Climate change is projected to impact drastically in southern African during the 21st century
under low mitigation futures (Niang et al., 2014). African temperatures are projected to rise
rapidly, in the subtropics at least at 1.5 times the global rate of temperature increase (James
and Washington, 2013; Engelbrecht et al., 2015). Moreover, the southern African region is
projected to become generally drier under enhanced anthropogenic forcing (Christensen et
al., 2007; Engelbrecht et al., 2009; James and Washington, 2013; Niang et al., 2014). These
changes in temperature and rainfall patterns will plausibly have a range of impacts in South
Africa, including impacts on energy demand (in terms of achieving human comfort within
buildings and factories), agriculture (e.g. reductions of yield in the maize crop under higher
temperatures and reduced soil moisture), livestock production (e.g. higher cattle mortality as
a result of oppressive temperatures) and water security (through reduced rainfall and
enhanced evapotranspiration) (Engelbrecht et al., 2015).
This document compares the performance of three horizontal axis wind turbines with varying blade sizes (2m, 1.5m, and 1m) suitable for low wind regimes. Data was collected over three months on the power output of the three turbines. The results showed that while larger blades produced more power, the efficiency and percentage of rated power produced increased with decreasing blade size. Specifically, the 1m blade turbine performed at 94.89% of its rated capacity on average, compared to 95.81% for the 1.5m turbine and 83.96% for the 2m turbine. Thus, the smaller turbine was more effective at extracting power from the low wind speeds.
Climate Modelling, Predictions and Projectionsipcc-media
This document discusses climate modeling, predictions, and projections. It summarizes that global surface temperature change is likely to exceed 1.5°C by the end of the century for all scenarios. It also notes that ocean acidification is a clear signal of human-caused climate change and that global sea levels will continue rising through 2100 even with reductions in greenhouse gas emissions. Initialized climate simulations can reproduce temperature trends and internal variability to provide near-term climate predictions.
Ten most popular software for prediction of cyclonic stormsMrinmoy Majumder
In recent years the frequency and intensity of cyclones and hurricanes have been increased manifold compared to the last decade. As a result, the necessity for the development of computer models to predict the track, intensity, and time of occurrence of cyclonic storms has increased to avoid loss of life and prevention of damages to public properties. In this presentaion I had tried to highlight the ten most used models in this aspect which are responsible for saving millions of life and their livelihood.
This document summarizes research on modeling extreme hurricane winds and insured losses in the United States. The researchers use extreme value theory and Bayesian statistical methods to estimate return levels of hurricane winds for different regions and time periods. They find higher return levels are associated with warmer climate conditions. The same methods are applied to model insured hurricane losses and estimate extreme potential losses under different climate scenarios.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
The document proposes a wind power prediction method based on Bayesian fusion of multiple numerical weather predictions. It first establishes a relationship between wind speed and power using neural networks. It then analyzes the characteristics of wind speed forecasts from three independent weather sources. A Bayesian method is designed to fuse the wind speed forecasts, yielding a more accurate prediction than any single source. The fused wind speed is input to the neural network model to predict wind power at 15-minute intervals. Experimental results show the method improves accuracy of wind speed and power forecasting compared to using a single source.
1) This document summarizes a student's study on optimizing the shape and dimensions of a point absorber wave energy converter to improve power extraction.
2) The study evaluated three shapes - a cylinder, cylinder with hemispherical bottom (bullet), and cylinder with conical bottom (cone) - and determined the bullet shape with a 10m radius and 15m total vertical length was most efficient.
3) Accounting for viscous effects through computational fluid dynamics simulations reduced predicted power extraction by over 10% and shifted the optimal damper configuration for certain sea states.
Amtelco June 2010 webinar on Infinity Contact-Based Architecture NewAMTELCO
This webinar discussed Contact Based Architecture, which leverages Intelligent Series applications like IS Directory, IS Directory Contacts, IS Directory On-Call Scheduling, IS Appointment Scheduling, and IS Web Services. It shifts from account-based to contact-based messaging, dispatching, and scheduling. The webinar covered how to build an IS Directory, automate updates, link scripts to retrieve contacts, update scripts for contact dispatching, use on-call and appointment scheduling, take advantage of new web scripting features, and provide mobile scheduling apps. Contact Based Architecture forms the foundation for these applications.
This document summarizes Oman's history as a trading nation and its current efforts to diversify its economy and create jobs through investments in infrastructure, industries, and developing its oil and gas resources. It discusses how national oil company Petroleum Development Oman is increasing production through enhanced oil recovery techniques and creating local jobs. It also profiles independent operator Occidental Oman and its use of technology to increase production at fields in Oman.
Comparison of Latent Heat Flux Using Aerodynamic Methods and Using the Penman...Ramesh Dhungel
This document compares methods for calculating latent heat flux using aerodynamic and Penman-Monteith methods with satellite data. It finds that using surface temperature instead of air temperature in the Penman-Monteith method, as well as fully parameterizing the method, results in more accurate calculations of latent heat flux, especially in sparsely vegetated areas where surface and air temperatures differ more. The maximum error found when simplifying the Penman-Monteith method was 56 W/m^2. The study emphasizes the advantage of using separate aerodynamic equations over the combined Penman-Monteith equation when surface temperature is significantly warmer than air temperature.
Simulation of Wind Power Dynamic for Electricity Production in Nassiriyah Dis...IOSR Journals
This document summarizes a study that simulated wind power dynamics for electricity production in Nassiriyah District, Iraq. The study measured wind speed data from 2010-2013 at 10m altitude and used this to mathematically model and predict important wind energy parameters. It found that the minimum altitude for feasible wind speed (≥5m/s) for power production was 44m for a friction coefficient of 3.0 and 32m for a friction coefficient of 4.0. Weibull distribution analysis showed that the percentage of days with mean wind speeds ≥5m/s increased with higher altitudes and friction coefficients, making wind energy more viable at greater heights.
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
This document provides a summary of Brian Medeiros' career and qualifications. It includes his educational background, earning a PhD in Atmospheric & Oceanic Sciences from UCLA in 2007. It lists his professional experience as a Project Scientist at NCAR since 2009. It also provides details of his research focus on cloud-climate interactions in general circulation models, awards, teaching experience, research grants, publications, and professional service and involvement.
1) eMAST aims to integrate data from TERN and other sources using data assimilation tools to enable ecosystem modeling, optimization, and analysis of questions in carbon, water, climate change, fire, and biodiversity.
2) eMAST is delivering high-resolution climate, canopy, water use, and productivity data products as well as tools for data interpolation, modeling, and assimilation.
3) ePiSaT is an R package that uses OzFlux tower data, climate data from eMAST, and satellite data to model and map gross primary productivity across Australia.
This document summarizes wind data collected over one year at Sitakundu, Bangladesh. The data was analyzed to determine monthly average wind speeds, daily variations, frequency distributions, and instantaneous wind velocities. Irregular wind behavior was observed in August and September, with higher gusts and more variable speeds. Weibull analysis was used to calculate shape and scale factors each month. Factors were outside normal ranges for August and September, likely due to high magnitude wind gusts during this period. Modifications were suggested to better model local wind conditions, including using mean hourly rather than 10-minute wind speeds.
Hurricanes and Global Warming- Dr. Kerry EmanuelJohn Atkeison
Dr. Kerry Emanuel explains how Global Warming increased the power of hurricanes. Hurricane Katrina is discussed, with the conclusion that Katrina probably would not have had the power to break the New Orleans levees in a pre-Global Warming world. April 2009 webinar presented by the Southern Allicance for Clean Energy (http://www.cleanenergy.org/) and the Gulf Restoration Network (http://healthygulf.org/) SlideCast by John Atkeison of the Alliance for Affordable Energy. There is a very small amount of phone noise.
Delineation of Mahanadi River Basin by Using GIS and ArcSWATinventionjournals
Precipitation is the significant segment of hydrologic cycle and this essential wellspring of overflow. In hydrological models precipitation as information, release is mimicked at the outlet of a watershed. Exactness of release re-enactment relies on drainage zone of the watershed. Therefore in the present work Mahanadi river basin lying within Odisha (drainage area approximately 65000 sq. km.) has been delineated in to five subbasins based on the five CWC operated discharge sites in Odisha. In the present work Arc-Swat has been used to delineate the watershed with the help of the (digital elevation model) DEM. At last as indicated by area of release locales, the aggregate study range was isolated into five sub-basins in particular Kesinga, Kantamal, Salebhata, Sundergarh and Tikarpada. It was observed that number of sub-watersheds into which the study area is being depicted relies on number of outlets and density of drainage. For a specific number of outlets, the thick is the density of drainage the more is the quantity of sub-watershed and the other way around.
This document discusses a study analyzing the impact of climate change on precipitation characteristics in Guwahati, India using an Earth System Model. It summarizes the use of statistical downscaling with multiple linear regression to project future precipitation data. Predictors with the highest correlation to total monthly precipitation, maximum monthly precipitation, and number of dry days were selected from the ESM dataset. The downscaled results will be used for flood frequency analysis to project precipitation levels and dry days under different return periods.
The study aimed to calculate the percent energy recovery of a runner on flat ground, uphill at a 4 degree slope, and downhill at a -4 degree slope using a 3D accelerometer. The researchers found no statistically significant difference in mean percent recovery between the conditions. Specifically, the mean recovery was 27.16% for flat ground, 28.6% for uphill, and 27.8% for downhill. While the method showed potential for measuring energy exchange, the current data did not provide definitive conclusions about recovery differences on varying slopes due to high p-values.
This study developed a method to represent 2D seismic velocity fields using 2D Haar wavelet series. The method simplifies previous approaches using polynomials or 1D wavelet series. Seismic velocity fields from 5 geological models were approximated using the 2D Haar wavelet algorithm. While the number of coefficients was initially high, generating coefficient averages over subintervals reduced the total coefficients by up to 90%, simplifying the representations. The findings support expanding this work to seismic reflection travel time modeling, ray tracing, tomography, and migration using the wavelet parameterization.
Descriptive modeling is a type of mathematical modeling that describes major historical events and relationships between elements that created those events. Descriptive climate models typically represent significant components of the climate system like the atmosphere, oceans, land, and their interactions. One strength is they can isolate factors contributing to climate change, like how changes in precipitation and temperature affect agricultural yields. Current examples include using descriptive models to simulate 20th century climate trends and the decrease in Arctic sea ice cover since 1960.
Climate change is projected to impact drastically in southern African during the 21st century
under low mitigation futures (Niang et al., 2014). African temperatures are projected to rise
rapidly, in the subtropics at least at 1.5 times the global rate of temperature increase (James
and Washington, 2013; Engelbrecht et al., 2015). Moreover, the southern African region is
projected to become generally drier under enhanced anthropogenic forcing (Christensen et
al., 2007; Engelbrecht et al., 2009; James and Washington, 2013; Niang et al., 2014). These
changes in temperature and rainfall patterns will plausibly have a range of impacts in South
Africa, including impacts on energy demand (in terms of achieving human comfort within
buildings and factories), agriculture (e.g. reductions of yield in the maize crop under higher
temperatures and reduced soil moisture), livestock production (e.g. higher cattle mortality as
a result of oppressive temperatures) and water security (through reduced rainfall and
enhanced evapotranspiration) (Engelbrecht et al., 2015).
This document compares the performance of three horizontal axis wind turbines with varying blade sizes (2m, 1.5m, and 1m) suitable for low wind regimes. Data was collected over three months on the power output of the three turbines. The results showed that while larger blades produced more power, the efficiency and percentage of rated power produced increased with decreasing blade size. Specifically, the 1m blade turbine performed at 94.89% of its rated capacity on average, compared to 95.81% for the 1.5m turbine and 83.96% for the 2m turbine. Thus, the smaller turbine was more effective at extracting power from the low wind speeds.
Climate Modelling, Predictions and Projectionsipcc-media
This document discusses climate modeling, predictions, and projections. It summarizes that global surface temperature change is likely to exceed 1.5°C by the end of the century for all scenarios. It also notes that ocean acidification is a clear signal of human-caused climate change and that global sea levels will continue rising through 2100 even with reductions in greenhouse gas emissions. Initialized climate simulations can reproduce temperature trends and internal variability to provide near-term climate predictions.
Ten most popular software for prediction of cyclonic stormsMrinmoy Majumder
In recent years the frequency and intensity of cyclones and hurricanes have been increased manifold compared to the last decade. As a result, the necessity for the development of computer models to predict the track, intensity, and time of occurrence of cyclonic storms has increased to avoid loss of life and prevention of damages to public properties. In this presentaion I had tried to highlight the ten most used models in this aspect which are responsible for saving millions of life and their livelihood.
This document summarizes research on modeling extreme hurricane winds and insured losses in the United States. The researchers use extreme value theory and Bayesian statistical methods to estimate return levels of hurricane winds for different regions and time periods. They find higher return levels are associated with warmer climate conditions. The same methods are applied to model insured hurricane losses and estimate extreme potential losses under different climate scenarios.
A WIND POWER PREDICTION METHOD BASED ON BAYESIAN FUSIONcsandit
The document proposes a wind power prediction method based on Bayesian fusion of multiple numerical weather predictions. It first establishes a relationship between wind speed and power using neural networks. It then analyzes the characteristics of wind speed forecasts from three independent weather sources. A Bayesian method is designed to fuse the wind speed forecasts, yielding a more accurate prediction than any single source. The fused wind speed is input to the neural network model to predict wind power at 15-minute intervals. Experimental results show the method improves accuracy of wind speed and power forecasting compared to using a single source.
1) This document summarizes a student's study on optimizing the shape and dimensions of a point absorber wave energy converter to improve power extraction.
2) The study evaluated three shapes - a cylinder, cylinder with hemispherical bottom (bullet), and cylinder with conical bottom (cone) - and determined the bullet shape with a 10m radius and 15m total vertical length was most efficient.
3) Accounting for viscous effects through computational fluid dynamics simulations reduced predicted power extraction by over 10% and shifted the optimal damper configuration for certain sea states.
Amtelco June 2010 webinar on Infinity Contact-Based Architecture NewAMTELCO
This webinar discussed Contact Based Architecture, which leverages Intelligent Series applications like IS Directory, IS Directory Contacts, IS Directory On-Call Scheduling, IS Appointment Scheduling, and IS Web Services. It shifts from account-based to contact-based messaging, dispatching, and scheduling. The webinar covered how to build an IS Directory, automate updates, link scripts to retrieve contacts, update scripts for contact dispatching, use on-call and appointment scheduling, take advantage of new web scripting features, and provide mobile scheduling apps. Contact Based Architecture forms the foundation for these applications.
This document summarizes Oman's history as a trading nation and its current efforts to diversify its economy and create jobs through investments in infrastructure, industries, and developing its oil and gas resources. It discusses how national oil company Petroleum Development Oman is increasing production through enhanced oil recovery techniques and creating local jobs. It also profiles independent operator Occidental Oman and its use of technology to increase production at fields in Oman.
Our client wanted a dedicated team with 12*7 support to keep them up-to-date with an hourly status of the articles published on their blog by various staff members and independent contributors associated with different channels and finance websites to ensure everything is functioning smoothly. We successfully fulfilled all their requirements and by providing every minute update when required we helped the client to be attentive for any hidden errors.
Publix Employees Federal Credit Union (PEFCU) implemented a private cloud infrastructure with VMware virtualization, EMC storage, and disaster recovery solutions to address challenges from hurricane season threats and infrastructure growth. This allowed PEFCU to fail over its entire virtual server environment to a remote site in 30-60 minutes, reduce backup storage needs by 96%, and complete backups in 8.5 hours compared to 12-36 hours previously. The solutions streamlined administration and reduced costs while improving data protection and availability.
Pharmaceutical Congress China 29-30 March 2016Helen P
Bringing 3 Significant Events Under 1 Roof!
The Pharmaceutical Congress China, part of IBC’s PharmaCon Series, recognizes the importance of regulation, clinical operation efficiencies, tackling pharmaceutical compliance issues and bringing products and services successfully in the market in China’s rapidly advancing pharmaceutical industry.
Way for short term support and resistanceforexbuffalo
Short-term support levels are formed when the price falls to a new low but then rallies, as buyers who missed the first opportunity will be inclined to buy again if the price returns to that level out of fear of missing out twice. Similarly, short-term resistance levels are formed when the price reaches a new high but then retreats, as sellers who missed the initial peak will be inclined to sell again at that price point due to the same fear of missing out a second time. Both scenarios can result in enough new market activity at that price to overwhelm the existing buyers or sellers and create a new support or resistance level.
Masaaki Imai June 2011 KAIZEN Institute USA forumMike Wroblewski
The document summarizes Masaaki Imai's week-long visit hosted by Kaizen Institute USA and Gemba Kaizen to promote lean transformation. It included two public knowledge forums on healthcare and manufacturing, three executive working sessions, and visits to five companies where Imai conducted gemba walks and workshops with CEOs and employees. An estimated 300 people from 35 companies participated in the events focused on applying Imai's philosophies of continuous improvement and respect for people.
Arturo Pelayo provides a qualitative and quantitative self-assessment of his soft skills. Some highlights include living and working in Mexico, Germany, and the US for a combined eight years to gain intercultural experience. He also spent 16 weeks living on a cruise ship with 250 international students from 56 nations to further develop his global skills. Pelayo has experience in roles involving instructional design, community management, systems engineering, and training. He emphasizes skills in intercultural communication, networking, strategic thinking, and entrepreneurship.
The document summarizes a social media listening study conducted for Coca-Cola brands Coke Zero, Dasani, and VitaminWater over 6 months. Key findings for each brand are presented along with 3 top-line strategic insights. The approach involved using Sysomos tools and custom queries to analyze conversations. For Coke Zero, events were found to be content-rich opportunities but some comments questioned aspartame safety. For Dasani, the PlantBottle was discussed positively but consumers questioned the difference from tap water. For VitaminWater, many events were identified as engagement opportunities but the brands were sometimes compared. The insights recommend optimizing media based on purchase funnel priorities and social insights for improved ROI.
The document discusses the challenges facing the current revenue cycle management (RCM) system in healthcare as the industry shifts towards a more consumer-driven model. It outlines how the growth of high-deductible health plans, consumer-directed healthcare spending, and the Affordable Care Act have significantly increased the proportion of provider revenue coming from patient payments. However, RCM systems remain focused on business-to-business transactions with insurers rather than the consumer experience. The HIMSS Revenue Cycle Improvement Task Force was formed to address this issue and create a vision for the next generation of RCM that prioritizes administrative cost containment, interoperability, and positive consumer engagement.
This document provides information about BD FACSTM lysing solution, which is used to lyse red blood cells following flow cytometric analysis of human peripheral blood cells. It is intended for use with BD monoclonal antibody reagents to stain blood samples, which are then treated with the lysing solution to eliminate red blood cells while preserving white blood cells. The document describes the product, provides instructions for use, expected results, limitations, and performance characteristics. It establishes reference ranges for lymphocyte subsets in normal adult subjects.
High volume test automation in practiceandytinkham
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1. Exploiting Weather Conditions for Minimizing Transportation Energy1
By Michael Servatius2
University of Notre Dame, Department of Mechanical Engineering
Fall 2014
1 In part supported by NSF Grant 1239224
2 Advised by Peter Bauer
2. Servatius 2
Table of Contents
I. Abstract—Page 3
II. Introduction—Pages 3-5
a. Motivation and Process—Pages 3-4
b. Prior Research and Resources—Page 4
c. Nomenclature and Notation—Pages 4-5
III. Results—Pages 5-17
a. Constant Drive Speed—Pages 5-16
i. Theoretical Straight Line Distance Energy Evaluation—Pages 5-6
ii. Comparing Energies of Different Routes Across the U.S.A—Pages 6-9
iii. Comparing Energies of the Same Route on Different Days—Pages 9-12
iv. Theoretical Map Showing Winds Necessary to Save Energy—Pages 12-16
b. Changing Driving Speed—Pages 16-17
IV. Conclusion—Pages 17-21
a. What We Learned—Pages 17-18
b. Future Work—Pages 19-21
i. Continued Research—Pages 18-19
ii. Implementation—Page 20-21
c. Significance—Pages 21-22
V. References—Page 23
VI. Appendix—Page 23-47
3. Servatius 3
I. Abstract
The main objective of the work done this semester was to determine the effects (and their
magnitude) of weather on transportation energy. While this work can be applied to anything
from snow on the ground to humidity in the air, we focused on the effects that wind conditions
have on energy consumption. This work can be useful for any drivers, but the simulations were
all done with a truck in mind. Universities and research companies should highly consider
looking further into the influence of weather on energy intake, as our findings show that it can
have a major impact on saving energy, and thus money, if the weather conditions are considered
appropriately.
II. Introduction
a. Motivation and Process
The motivation behind this work was to explore something that could be very important
to energy and cost savings in the transportation industry, yet has very little research done already
on it. The first thing that we studied was the energy difference between different routes from one
point to another. We found that in some cases, it may be worthwhile to travel a slightly longer
distance in order to save some energy. This is done by taking advantage of wind conditions. In
other words, avoiding headwinds and trying to drive with tailwinds can make a big difference in
energy use.
Next, experiments were done to compare energy consumption on different days. Real
time wind conditions were tested on a relatively calm day, and then on a very windy day. The
energy uses were very different. This work brings the question of whether delaying or pushing
up transportation could be beneficial. For instance, a company may be able to save a good deal
of energy, and therefore money, if they chose to travel on a day with tailwinds rather than a calm
4. Servatius 4
day or a day with headwinds. Of course, this type of decision can only be applied in very unique
circumstances, but sometimes the little things can make a big difference.
The studies involving the testing of different routes and changing travel day were both
done at constant speed, so our next experiment dealt with altering the speed based on the wind
conditions. Overall, we found that changing speeds based on winds and keeping the relative wind
speed the same throughout a drive can have significant impacts on energy. We only touched the
surface of this idea, however, and there is much more that can, and should, be explored in terms
of changing speeds based on weather.
b. Prior Researchand Resources
There has been very little research done involving weather and its impact on
transportation energy. Hopefully, people see the possibilities that can come with maximizing
energy use based on weather and the topic is explored further in the future. In terms of our work,
the starting point was [1]. The equations and theory in this text was the backbone of the research,
as the energy equations were used along with wind conditions to prove that weather can have a
major impact on energy. Furthermore, we looked a little bit into systems that update routes based
on traffic conditions to explore if this could eventually become a reality with wind and other
weather conditions.
c. Nomenclature and Notation
As suggested earlier, the energy equation from Ehsani et al was the main driving point
behind the work done. The energy equation a can be manipulated slightly to include wind
conditions, and coding this equation based on different routes was much of the work done. The
equation is as follows:
𝐸 = ∆𝑥(𝑚𝑔𝑓𝑟 + 0.5𝐶 𝑑 𝐴𝜌𝑣2
)
5. Servatius 5
where E is the energy, ∆𝑥 is the change in distance, m is the mass of the vehicle, g is the
gravitational constant, 𝑓𝑟 is the rolling resistance coefficient, 𝐶 𝑑 is the air drag coefficient, A is
the frontal area of the vehicle, 𝜌 is the density of air, and v is the velocity of the vehicle. In the
majority of our tests, the velocity was held constant at about 55 mph (~24.58 m/s). In all the
tests, the rolling resistance was held at 0.01, the air drag at 0.8, the mass at 10000 kg, the frontal
area at 10 m2, and the density of air at 1.2 kg/m3. The change in distance must be in meters for
the units to remain consistent and then all energies were converted to kWhr. The wind conditions
were taken into account by altering the velocity term. To do this, we also had to segment the
distance based on where the winds were, often creating multiple energy calculations and then
summing them to get the total energy across the entire distance being studied.
III. Results
a. Constant Driving Speed
i. Theoretical Straight Line Distance Energy Evaluation [a]
The first step in our research was to transfer the energy equation into MATLAB and
ensure that the code was running properly. We did this on a small scale and used a theoretical
drive. This meant that we did not use real roads and just considered a straight-line path. After
confirming that the code was working by showing that with zero wind the energy consumption
was equal to that solved for by hand, we were able to take the winds into account. Because the
simulation was theoretical, we could control the winds. We chose to create 4 cases: no wind, all
tail winds, all head winds, and alternating tail and head winds.
To truly show that the wind could in fact make a difference in energy use for a truck, we
chose to use large winds for this first simulation. The distance traveled was chosen to be 96.2
miles (154818.9 m), the distance between South Bend and Chicago with wind speeds of 20 mph
6. Servatius 6
(8.94 m/s). The result showed that tailwinds could make a big difference in energy use when
compared to both a no-wind case and a test with only headwinds. The energy with no wind was
166.86 kWh, with tailwinds was 92.64 kWh, with headwinds was 274.08 kWh, and finally with
alternating winds was 183.36 kWh. The fact that the alternating case used more energy than the
no wind case (by about 9.9%) also shows that the negative effect of headwinds is greater than the
positive effect on energy of tailwinds. Thus, if choosing a driving route, the most important thing
in terms of energy use is to avoid headwinds.
ii. Comparing Energies of Different Routes Across the U.S.A [b]
The next simulation involved testing how wind changes energy use in a real scenario. To
do this, we chose three different routes from Phoenix to Chicago. One route was the direct route
suggested by [2], while the other two routes were chosen based on the wind conditions on
September 30th. The three routes are shown in Figures 1-3, and the wind conditions in Figure 4.
The wind maps found online at [3] allowed for easy calculation of wind magnitude and direction
across the country and this data was integrated into our code.
7. Servatius 7
Figure 1. Phoenix to OKC to Omaha to Chicago
Figure 2. Phoenix to Salt Lake City to Chicago
8. Servatius 8
Figure 3. Phoenix to Chicago Direct
Figure 4. Wind Conditions on September 30, 2014
In this particular case, the most direct route (Figure 3) is the smartest route in terms of time,
9. Servatius 9
distance and energy consumption. This route from Phoenix to Chicago spanned a distance of
1802 miles (2900038 m) and used 3013.6 kWh of energy. The route from Phoenix to OKC to
Omaha to Chicago of 1931 miles (3107643 m) used 3100 kWh while the route from Phoenix to
Salt Lake City to Chicago of 2051 miles (3300765 m) used 3436.7 kWh.
Although the most direct route also is the most energy efficient, going from Phoenix to
OKC to Omaha to Chicago utilizes the winds more than any other route. This shows that the
wind does make a significant impact, as the distance traveled is 7.16% more than the direct
route, but only 2.87% more energy is used. The main takeaway from this simulation is that the
wind does have a substantial impact on energy, but it may only apply in particular cases. In this
case, it makes the most sense to take the most direct route. If the winds were a bit stronger,
however, it is very feasible that the longer route shown in Figure 1 could save some energy over
the direct route.
The effects of the winds are further demonstrated by comparing the energy consumptions
of all the routes with and without wind. Without wind, the direct route shown in Figure 3 used
3125.6 kWh of energy, or 3.7% more energy than with the wind included. Next, the route from
Phoenix to Salt Lake City to Chicago used 3557.5 kWh, or 3.5% more energy than with the wind
included. Finally, the route from Phoenix to OKC to Omaha to Chicago used 3349.3 kWh of
energy, or a whopping 8% more than with the wind included. Overall, including the wind in
energy calculations gives a more realistic picture of the energy being consumed by the truck, and
if used correctly, this can save a lot of energy.
iii. Comparing Energies of the Same Route on Different Days [c]
Given our previous experiment, it is easy to see that going a slightly further distance
could potentially be beneficial is winds are accounted for properly. So going a different route to
10. Servatius 10
save energy was a possibility, and the next step was to ask if going a different time or even day
could save money. Thus, we used the same wind conditions from September 30th found in Figure
4 and compared them to the winds on October 6th. We chose a path from New Orleans to
Columbus (as shown in Figure 6), as the winds were very large in this direction on October 6th as
shown in Figure 5. The September 30th winds were relatively calm in this direction, so the aim of
this test was to show that delaying or pushing up travel has the potential to save a good amount
of energy, again only if the winds are used properly.
Figure 5. Wind Conditions on October 6, 2014
11. Servatius 11
Figure 6. New Orleans to Columbus
The results of this experiment demonstrated the importance of winds in energy
calculations for two main reasons. The first, as hinted at earlier, deals with comparing winds on
separate occasions. With no wind, the trip from New Orleans to Columbus used 1575.5 kWh of
energy. With the calm winds on September 30th, 1569.3 kWh of energy were used. Finally, with
strong tailwinds on October 6th, 1180.5 kWh of energy were used. This is a 25.1% decrease in
energy consumption from no wind to the conditions on October 6th and a 24.8% decrease from
September 30th. These savings in energy could save a lot of money. In this case, the winds are a
week apart, so a company may not be able to delay a trip for that long. It is certainly worth
looking into the wind conditions on several days when planning a trip, however, as it could save
a lot of energy to travel when there are advantageous winds.
12. Servatius 12
The second demonstration of the large impact the winds have on energy is comparing the
trip from New Orleans to Columbus and vice versa on both September 30th and October 6th. On
the first date, the energy used from New Orleans to Columbus was 1569.3 kWh while it was
1582.2 in the opposite direction. On October 6th, the energy consumed was 1180.5 kWh going
north to Columbus and 2053.2 kWh going from Columbus to New Orleans. The calm winds on
the final day of September had relatively little affect on energy, while the large winds (ranging
from 3.5-5.5 m/s) on October 6th had a huge influence depending on whether the truck travels
with or against the wind. Just to further illustrate this point, with calm winds on September 30th,
the energy changed less than 1% depending on the direction of travel. On the other hand, the
energy changed 42.5% depending on the direction of travel with strong winds on October 6th.
iv. Theoretical Map Showing Winds Necessary to Save Energy [d1]-[d3]
While these studies done on the U.S. roadways are extremely useful in showing the impact of
weather on energy use, it can be a bit difficult to visualize the winds along with the roadways.
Therefore, we decided to return to a theoretical case. The main idea was to create a wind map
and vary the conditions how we pleased, and then have an animated line show a path moving
through the wind map. There is no way to show the animation in this report, so the maps are
shown after the truck’s route has been finished. The first theoretical test is found in Figure 7,
with winds varying between 0-18 mph (0-8 m/s). Three routes were tested and all begin in the
bottom left of the figure and end in the top right. The first was a test case, taking the most direct
route (diagonally) with no headwinds or tailwinds. The second route (to the right and then up)
went around the outside with only winds that would save energy. Finally, the third route (up and
the to the right) went around the outside with only winds that would lose energy. Much like we
13. Servatius 13
did earlier, the main goal was to determine whether going a further distance could actually save
energy due to strong tailwinds.
Figure 7. Theoretical 10 x 10 Map (Varying Wind Conditions) [d1]
Much like before, the effects of the wind were very noticeable, but it was the best choice
to follow the most direct route. The direct route spanned almost 88 miles (141,421 m) and used
152.4 kWh. The route designed to best utilize the winds spanned 124 miles (200,000 m) and
used 170.7 kWh. Lastly, the course that went against the winds also covered 124 miles (200,000
m) but used 263.1 kWh of energy. While covering the same distance, the two routes that
traversed the outside of the theoretical map had a 54% difference in energy use. This percentage
14. Servatius 14
can be solely attributed to the wind conditions. Furthermore, the long route that utilized positive
winds traveled 41% further than the direct route, but only used 12% more energy.
Given the results from our first theoretical test, we decided to increase the wind
conditions in the same size theoretical map. This test is depicted in Figure 8, and the winds
varied between 0-40 mph (0-18 m/s)3. Again, there are 3 routes—a direct diagonal route, an
efficient route around the outside, and an inefficient route around the outside.
Figure 8. Theoretical 10 x 10 Map (Varying High Wind Conditions) [d2]
In this case, the weather made a huge difference, which was expected given the large
magnitude of the winds. The efficient route along the edge of the map ended up being the
3 Consult the appendix to see the wind conditions in detail
15. Servatius 15
smartest route to take, proving that the winds can make it advantageous to take a longer route in
order to save energy. This path, which covered 124 miles, used only 101.5 kWh of energy. The
direct route used 152.4 kWh of energy across 88 miles, and finally the inefficient route used a
massive 397.9 kWh.
These numbers need to be considered carefully given the large winds tested. Rarely are
there 40 mph winds, so we decided to run one more simulation on our theoretical map in order to
determine what winds would make the long and direct paths have equal energy consumption.
This data would allow us to know exactly which wind conditions need to be present in order to
save energy going the long way. In order to simplify the map, we removed all winds that were
not along the route and kept the winds the same along the route. As shown in Figure 9, the direct
route has no wind. After calculating the average winds required on the long route to utilize the
same amount of energy as the direct one, we found that the average wind conditions must be
equal to 12.1 mph (5.412 m/s). With these winds, the energy used over the 124-mile road was
152.4 kWh. The 88-mile direct course also used 152.4 kWh.
Like stated before, the difference between the routes in terms of distance was about 41 %,
but the energy difference was less than .0008 %. The reason that this model is so important is
that 12 mph winds are very realistic, and we traveled more than 40% further with the same
energy considerations. This can make a huge difference if considered over all drives, as there are
likely many cases that a relatively direct route (only go slightly out of the way), but also save
some energy. If studied and implemented correctly, this could save a lot of energy for truck
companies in the long run.
16. Servatius 16
Figure 9. Theoretical 10 x 10 Map (Constant Wind Conditions) [d3]
b. Changing Driving Speed [e]
After determining that the wind can provide energy returns while traveling at a constant
speed, the next step in our work was to investigate whether altering the speed of the truck based
on the wind conditions would change the energy considerations. The main idea in this simulation
was to slow down the truck when headwinds were present and speed up when tailwinds were
present. Our thinking in this was that we would take advantage of winds that help lower energy
consumption (tailwinds) and minimize the negative energy effects of the headwinds.
We decided to run two tests across the same path, one with constant truck speed and one
that would alter the speed based on the winds as described above. We took a 20-hour drive and
17. Servatius 17
broke it down into 10 different wind segments. We altered the speed of the truck so that the
relative air speed would remain constant. In other words, if a 5 m/s tailwind were present, the
truck would speed up 5 m/s. If a 5 m/s headwind existed, the truck would slow down 5 m/s.
Overall, over our 20 hours, the wind speeds averaged out to zero so that we could compare the
results to a test case with a constant drive speed and still have the 20-hour time period for both
cases. The winds varied between 0-33.5 mph (0-15 m/s), however, 9 of the 10 wind conditions
were 22 mph (10 m/s) or less.
It was found that keeping relative air speed constant by changing truck speeds saved
energy compared to the test case of driving at a constant speed. Both tests were done over 20
hours, and the truck with changing speed resulted in 1367.6 kWh of energy. If we did not alter
the speed of the truck and remained at a constant 56 mph (25 m/s), the energy use was 1814
kWh. This is a 32.6% difference in energy use, a huge savings when considering the time
traveled is identical. While the winds considered were relatively high, they were large for both
headwinds and tailwinds, so it was not a highly favorable path for the constant case. This
demonstrates that if we were to apply some of our previous work with rerouting based on winds
along with altering the speed of the truck, we could save a great deal of energy.
IV. Conclusion
a. What We Learned
The goal of our work was to determine if weather has an impact on energy consumption
in transportation. More specifically, we wanted to explore the effects of wind on a truck. After
doing a myriad of simulations, it is clear that wind does in fact have a noticeable impact on
energy. Asking and exploring three main questions helped determined this.
18. Servatius 18
The first was to determine whether taking alternative routes could help save energy. We
looked at going from one place to another via different path both across the country and
theoretically and showed that, if exploited properly, the wind can help save a substantial amount
of energy. The extent of energy savings depends on the strength and direction of the winds, of
course. It is up to the user, most likely a truck company, to determine if the savings along one
route are worthwhile as opposed to another route.
Second, we looked at wind conditions along the same route on multiple occasions to
decide if changing travel time could provide energy savings significant enough to change travel
plans. Based on our comparison of winds on September 30th to October 6th along the path from
New Orleans to Columbus, we showed that considering wind conditions when planning travel
has the potential to save a notable amount of energy. In this case, the difference between the two
days was about 25%. Now this difference was a week apart, but these conditions could be
present days apart or even hours apart under the right circumstances. Because of this, universities
and research companies should seriously consider researching more into planning travel based on
the winds, as it can go a long way in saving money.
Finally, the last question involved changing the speed of the truck, something we had not
explored in any previous simulation. We decided to keep the relative wind speed the same and
this led to noticeable results, saving 32.6% energy over a 20-hour drive with reasonably high
wind speeds. It would have been interesting to continue to explore changing speeds, whether
keeping the relative wind speed constant or not, but unfortunately we ran out of time before the
semester’s end.
19. Servatius 19
b. Future Work
i. Continued Research
Because there has been very little work done on this subject to date, there are many
directions to take this research in. First, there are numerous more studies that can be done
involving the wind. More specifically, we did not do a great deal of work changing speeds so
there is a lot more that can be done in this domain. Furthermore, if the areas we studied were
combined, this would likely lead to some astounding results. For instance, if a truck company
were to consider taking a different route to avoid headwinds and take advantage of tailwinds
while also keeping relative wind speed constant by changing the truck’s speed, this would likely
lead to major energy savings.
In addition to the effects of wind, there are many other weather conditions that would
certainly change the energy considerations. For example, having wet road conditions due to rain,
slush, snow, or ice would alter the rolling contact coefficient and thus modify the energy
calculations. Furthermore, there are weather conditions such as humidity that may or may not
have any influence on energy, but must be studied further in order to know for sure. And aside
from weather, we also did not include any acceleration effects or traffic patterns. In a realistic
situation, the truck would not be driving at a constant rate the entire time of travel, so that must
be taken into account when considering our results. If traffic and weather were integrated
together in a simulation, this would help give a more realistic answer as to how energy can be
saved.
Additionally, this study was done with the mass, frontal area, and air drag coefficient of a
medium-sized truck, so further research can be done to determine if the wind has the same effect
on a sedan, crossover, motorcycle, or any form of transportation. It is very viable that the wind
20. Servatius 20
has an effect on all forms of outdoor transportation and we simply need to determine how great
an impact that is in order to decide whether it is worth taking advantage of.
Finally, some of the research not previously mentioned was to calculate the percentage of
the energy consumption that can be attributed to rolling resistance, and that attributed to air drag.
It was found that the rolling resistance accounted for 25.26% of the energy, and the air drag
74.74%. This data suggests that the wind conditions are even more important than we believed
before, as the air drag component of energy makes up almost three-fourths of the total energy. It
would be interesting to see how much these percentages change based on altering the coefficients
of air drag and rolling resistance, and it would not be tough a study to pursue further.
ii. Implementation
This research is interesting, but it is useless unless it is implemented correctly. As we
have shown, the wind has a visible effect on energy use. Therefore, it would be beneficial for
universities and companies to at least look into further research and determine whether rerouting,
preplanning travel, or other considerations based on wind are a realistic possibility to apply to
truck businesses or even an average driver.
The most logical thing to determine rerouting would be to set up a system similar to those
that are already in place to give the most efficient traffic routes. If a GPS system were improved
to show not only roads and traffic, but also energy based on weather conditions this would allow
a customer to make decisions about which route to take based on the value they place on time
and energy. Because heavy traffic wastes a lot of energy, it is likely that in many cases the
shortest travel route in terms of time would also be the most efficient in terms of energy.
Furthermore, there are a few different ways to employ our new knowledge that keeping
the relative wind speed constant can help save energy. First, is by simply making people more
21. Servatius 21
aware to check the winds and drive accordingly. This is a nuisance, however, and the vast
majority of people would be unlikely to change their habits. Instead, having digital speed limit
signs that adapt based on the wind conditions would help change people’s habits. For example, if
strong tailwinds were present, the speed limit would be increased. If strong headwinds existed,
the speed limit would be dropped. This would help save energy and money for all drivers
whether they know of the effects of the wind or not. Another solution might be to have cars that
drive themselves and adjust speed based on the wind. This is not that far fetched considering cars
have and continue to be developed that drive unmanned, and sensors measuring the wind are
very prevalent.
b. Significance
As mentioned before, the goal of this work was to determine if manipulating
transportation based on winds could save energy. While energy saving is extremely important
and a growing concern, at the end of the day, the significance of this work revolves around
money. Some of the ideas discussed in the previous section have the potential to be very
lucrative business ideas. If a GPS system could update traffic and weather, it would provide
insights that truck companies and regular drivers alike could utilize to make quick decisions
about saving both time and energy (and thus money).
Next, average people owning cars and trucks that drive themselves is extremely likely to
occur in the future, so being able to program them to adjust their speed based on the wind would
help save a huge amount of energy and money. As we showed, keeping the relative wind speed
constant by changing truck speed can save a significant amount of energy for one truck, but
imagine the possibilities if this were programmed into cars and trucks on a larger scale.
22. Servatius 22
Very few businesses will get involved in a project that they do not eventually see
becoming profitable, so for research to be truly significant, there must be business opportunities.
That is what makes this work so meaningful, as it has the possibility of helping to save a great
deal of energy if studied and implemented further, while also allowing businesses to profit by
developing the software and bringing it to market. We have only uncovered the very tip of the
iceberg, and we need to continue to research this field as there is great opportunity to not only do
good for the world by saving energy, but also develop something extremely useful and
profitable.
23. Servatius 23
V. References
[1] Ehsani, Mehrdad. Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals,
Theory, and Design. Boca Raton: CRC, 2005. Print.
[2] "MapQuest Maps - Driving Directions - Map." MapQuest Maps - Driving Directions - Map.
N.p., n.d. Web. 03 Dec. 2014. <http://www.mapquest.com/>.
[3] "Wind Map." Wind Map. N.p., n.d. Web. 04 Dec. 2014. <http://hint.fm/wind/>.
VI. Appendix
[a]
%% Michael Servatius
% 9/26/14
clear all
clc
%% Parameters
Cd=0.8; % air drag coefficient
m=10000; % mass= 10,000kg
ar=10; % area= 10 m^2
fr=0.01; % rolling resistance coefficient
rho=1.2; % density of air
g=9.8; % 9.8m/s
v=24.58; % 24.58 m/s = 55 mph
deltax=154818.9;%96.2 miles (Chi to South Bend)
deltax10=deltax/10;
%% Wind at 20 mph (32 km/h) in alternating directions
c1=cosd(0); % angle of wind speed
c2=cosd(180);
c3=cosd(0);
c4=cosd(180);
c5=cosd(0);
c6=cosd(180);
c7=cosd(0);
c8=cosd(180);
c9=cosd(0);
c10=cosd(180);
vwind1=8.94; % wind speed (ignoring direction)
vwind2=8.94; % angle takes care of direction
vwind3=8.94;
vwind4=8.94;
vwind5=8.94;
vwind6=8.94;