This document summarizes a project to study the effects of surface erosion and roughness on wind turbine performance. Wind tunnel tests were conducted on airfoils with simulated surface contamination, and computational models were developed to predict airfoil and turbine performance degradation from roughness. Testing showed roughness reduced maximum lift and increased drag on airfoils. Modeling predicted a 4-8% reduction in annual energy capture for a 5MW reference turbine from representative levels of airfoil roughness. The study aims to improve understanding of roughness effects to help mitigate performance losses in wind turbines.
Wind energy is a promising energy source. Modern wind power industry officially started in 1979 in Denmark with a
turbine of few KW and its evaluation brought up to now, devices of which rated power is higher than 20 MW.
The size of wind turbine’s massively increased and their design achieved a common standard device: Horizontal axis,
Three blades, Upwind, Pitch controlled blades, Active yaw system.
This slideshare provides geotechnical engineers and nondestructive testing professional with information on low strain impact integrity testing of deep foundations and piles.
Wind energy is a promising energy source. Modern wind power industry officially started in 1979 in Denmark with a
turbine of few KW and its evaluation brought up to now, devices of which rated power is higher than 20 MW.
The size of wind turbine’s massively increased and their design achieved a common standard device: Horizontal axis,
Three blades, Upwind, Pitch controlled blades, Active yaw system.
This slideshare provides geotechnical engineers and nondestructive testing professional with information on low strain impact integrity testing of deep foundations and piles.
Effect of wind turbine on tlp floating platform responseseSAT Journals
Abstract Ever increasing population of India demands high production of electrical energy which puts immense pressure on our limited stock of non-renewable sources of energy and makes us dependent over imports from foreign countries. The present study focuses on the innovative concept of renewable offshore wind energy wherein the hydrodynamic analysis of Tension Leg Platform (TLP) Floating Offshore Wind Turbine (FOWT) which supports 5MW wind turbine tower is carried out using ‘ANSYS Workbench 14.5’. The six degree responses of the structure are obtained in operational conditions considering rated wind velocity of 11.4m/s in an irregular wave environment. Two cases are mainly considered, the first-one with incident wave and wind combined action along 00 (case 1) and the second–one with incident wave and wind combined action along 450 (case 2). The effect of wind turbine on TLP responses is compared in between 10 different geometric models; 5 models (A’, B’, C’, D’, E’) considering only the TLP platform and 5 models (A, B, C, D, E) considering the same platforms along with wind turbine tower. It is observed that TLP FOWT has higher translational motions (surge, sway, and heave) as compared to rotational motions (roll, pitch, and yaw). The metacentric height improves drastically after adding weight to concrete ballast. Higher reserve buoyancy helps reduce surge, sway, roll and yaw. The direction of the incident wave and wind does not affect heave response and remains same when incident wave and wind acts at 00 or 450. Higher reserve buoyancy increases pitch response only when incident wave and wind is acting at 00 but the reverse effect is observed when incident wave and wind is acting at 450. Keywords: TLP, floating offshore wind turbine, hydrodynamic analysis.
The objective of this project is to design a wind turbine that is optimized for the constraints that come with residential use. The main tasks of this project are:
> To study the design process and methodology of wind turbine
> Derive the Blade Element Momentum (BEM) theory then use it to conduct a parametric study that will determine if the optimized values of blade pitch and chord length create the most efficient blade geometry
> Analyse different air-foils to determine which one creates the most efficient wind turbine blade.
Erosion Analysis of Subsea Equipment: A Case Study with High Solid LoadingAnsys
Prospect Flow presents a case study that utilizes ANSYS Fluent to analyze flows of a fluid with a high solid content (such as during a well kill operation). Engineers account for the high solid loading and its potential effect on erosion along with wear-induced geometry changes by combining various erosion mechanisms within a multiphase CFD solution.
Numerical Investigation of Aerodynamic Performance of H-Rotor Darrieus Wind T...Bharath Ningaraj
The objective of this project is to increase the performance of H-Rotor Darrieus turbine. A detailed numerical analysis has been made and the main aim is to enhance the performance of the turbine without changing its geometry. So we introduce two barrier plates. The effect of this barrier on the rotor performance has been analysed. To increase the rotor performance, it is important to prevent the negative torque that forms in the adverse direction of the rotor’s rotating direction. A new design has been put forward for the purpose of increasing the performance of the Darrieus wind rotor without making any modifications in its basic structure. The effect of barrier is to prevent the negative torque that forms in the adverse direction of the rotor’s rotating direction.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
The performance of a wind farm is affected by several key factors that can be classified into two cate- gories: the natural factors and the design factors. Hence, the planning of a wind farm requires a clear quantitative understanding of how the balance between the concerned objectives (e.g., socia-economic, engineering, and environmental objectives) is affected by these key factors. This understanding is lacking in the state of the art in wind farm design. The wind farm capacity factor is one of the primary perfor- mance criteria of a wind energy project. For a given land (or sea area) and wind resource, the maximum capacity factor of a particular number of wind turbines can be reached by optimally adjusting the layout of turbines. However, this layout adjustment is constrained owing to the limited land resource. This paper proposes a Bi-level Multi-objective Wind Farm Optimization (BMWFO) framework for planning effective wind energy projects. Two important performance objectives considered in this paper are: (i) wind farm Capacity Factor (CF) and (ii) Land Area per MW Installed (LAMI). Turbine locations, land area, and nameplate capacity are treated as design variables in this work. In the proposed framework, the Capacity Factor - Land Area per MW Installed (CF - LAMI) trade-off is parametrically represented as a function of the nameplate capacity. Such a helpful parameterization of trade-offs is unique in the wind energy literature. The farm output is computed using the wind farm power generation model adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The Smallest Bounding Rectangle (SBR) enclosing all turbines is used to calculate the actual land area occupied by the farm site. The wind farm layout optimization is performed in the lower level using the Mixed-Discrete Particle Swarm Optimization (MDPSO), while the CF - LAMI trade-off is parameterized in the upper level. In this work, the CF - LAMI trade-off is successfully quantified by nameplate capacity in the 20 MW to 100 MW range. The Pareto curves obtained from the proposed framework provide important in- sights into the trade-offs between the two performance objectives, which can significantly streamline the decision-making process in wind farm development.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
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When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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1. 1
Surface Erosion and
Roughness Effects on
Airfoil and Wind
Turbine Performance
C.P. (Case) van Dam
Department of Mechanical &
Aerospace Engineering
University of California, Davis
2014 Wind Turbine Blade Workshop
Albuquerque, NM
27 August 2014
2. 2
Contributors
• Sandia National Laboratories
– David Maniaci
– Mark Rumsey
– Matt Barone
• Texas A&M
– Ed White
– Robert Ehrmann
– Ben Wilcox
• UC Davis
– Chris Langel
– Ray Chow
– Owen Hurley
3. 3
Outline
• Background
• Project outline
– Field Measurements
– Wind tunnel testing
– Computational modeling
• Model validation
• Airfoil results
• Turbine performance
effects
– NREL 5-MW rotor
• Conclusions & Next steps
Source: Mayda
4. 4
Windplant Loss Categories
Walls & Kline (2012)
• Power losses can be as much as 20-30% in
state of the art windplants:
– Wake losses
– Turbine availability
– Balance of Plant (BOP) availability
– Electrical
– Environmental
– Turbine performance
– Curtailment
5. 5
Windplant Loss Categories
Walls & Kline (2012)
• Power losses can be as much as 20-30% in
state of the art windplants:
– Wake losses
– Turbine availability
– Balance of Plant (BOP) availability
– Electrical
– Environmental
– Turbine performance
– Curtailment
6. Environmental - Turbine Performance
6
Losses
• These losses typically range from 1% to 10%
• Impact on rotor aerodynamics:
– Icing
• Glaze
• Hoar
– Blade soiling
– Blade erosion
– Drop in air density (high temperature)
– Turbulence, shear, etc.
8. 8
Background - I
• Early stall controlled, constant speed wind
turbines were severely affected by blade
surface contamination and erosion. Large
performance losses resulted (40% in peak
power, ≥ 20% in energy capture).
• Development and introduction of blade
section shapes that were less roughness
sensitive mitigated this issue.
• Issue was focus of Wind Energy Conversion
System Blade-Surface Roughness Workshop
at NREL on April 20-21,1993.
9. 9
Blade Contamination
Moroz & Eggleston (1993)
• Surface soiling induced loss
in power for fixed pitch, stall
controlled rotors was big
problem
• Surface contamination
caused by insect
contamination, dust, erosion
of gel coat
• Surface roughness causes
reduced sectional lift curve
slope and maximum lift
coefficient, and increased
sectional drag
• Effect greater for stall
controlled rotors than pitch
controlled rotors
10. 10
Blade Contamination
Tangler (1993)
• Surface contamination
induced loss in power was
problem for stall controlled
rotors
• Aerostar blade uses NACA
4415-4424 airfoils
• NREL blade uses S805A,
806A, 807 airfoils
• NREL airfoils designed to
have less (maximum) lift
sensitivity to surface
roughness
• Tests show reduced loss in
turbine power due to
surface roughness for
NREL blade
11. 11
Background - II
• Effect of (small) roughness:
– It may cause premature transition from laminar to turbulent
boundary layer state
– It may cause boundary layer separation
– It may cause flow unsteadiness
– It removes energy from flow (increased skin friction)
– Effect depends on:
• Roughness height
• Roughness chordwise location
• Roughness density
• Pressure gradient
• Unit Reynolds number
• Mach number
12. 12
Background - III
• Variable speed, variable pitch turbines started to supersede the
constant speed, fixed pitch turbines and this significantly
mitigated the problem.
• However, a resurgence of the surface roughness problem has
occurred:
– More awareness as a result of improved windplant performance
analysis methods
– Higher maximum thickness-to-chord ratio (t/c) blade sections
– Higher lift-to-drag ratio (L/D) blade sections
– Higher Reynolds numbers
• Combination of high density altitude and blade surface
roughness can be especially troublesome.
• Because of size of turbines, blade washing is often cost
prohibitive.
• Detailed knowledge of loss mechanisms is still missing.
• Computational tools to analyze roughness sensitivity of airfoils
are missing.
13. 13
Surface Roughness and Erosion
Project
• Effects of Surface Contamination and Erosion on
Wind Turbine Performance
• Project started in April 2012
• Team:
– Sandia National Laboratories, Albuquerque
– Texas A&M University
– University of California, Davis
• Tasks:
– Field measurements of surface roughness and erosion
– Wind tunnel testing of effect of surface roughness and
erosion on airfoil performance
– Development of computational roughness model to account
for effect on aerodynamic performance of airfoils, blades,
rotors
– Correlate wind tunnel and CFD results
14. 14
Wind Tunnel
• Oran W. Nicks Low Speed Wind
Tunnel at Texas A&M
• Closed return tunnel
• Test section 7 ft × 10 ft
• Maximum velocity of 90 m/s
• Blockage of 4.8%
• Turbulence intensity of 0.25%
• Maximum Rec = 3.6 × 106 based
on loading at maximum lift
conditions
• Maximum Rec = 5.0 × 106 to α =
4°
Model installed in wind tunnel
freestream
16. pressure side suction side pressure side suction side
16
Distributed Roughness
Random insect distribution with 3% coverage. Random insect distribution with 15% coverage.
20. 20
Computational Modeling - I
• OVERFLOW-2
– Overset, multigrid, compressible Reynolds-averaged Navier-Stokes flow
solution method
– Semi-public domain
– Method newly developed Roughness Model has been coded into
• Reynolds averaged Navier-Stokes Equations
– Remove turbulent fluctuations from flow equations. All eddy scales are
ignored and mean flow can then be resolved with coarser computational
grid.
• Turbulence Modeling
– To properly account for turbulent fluctuations, there must be a way to
approximate the effect of the removed scales. In RANS methods, these
fluctuations are accounted for in the Reynolds stress terms
– Surface roughness has a prominent effect on this process
• Transition Modeling
– Baseline turbulence models must either assume fully laminar or “fully”
turbulent. Need additional correlation to automate switch between laminar
and turbulent.
– Surface roughness has a prominent effect on this process
22. 22
Computational Modeling - III
• Existing transition model Langtry-Menter:
– Recently developed
– Two variable model
• Local momentum thickness parameter, transition onset when
local momentum thickness ≥ critical momentum thickness
• Intermittency parameter governs growth turbulent kinetic
energy from transition onset to fully turbulent
• Roughness model adds 3rd variable to Langtry-
Menter transition model:
– Roughness amplification parameter (Ar)
• Turbulence model modified to account for surface
roughness effects
– Currently based on Wilcox
23. 23
Roughness Variable (Ar) Distribution
• There is a direct correlation
between distribution of Ar and
skin friction due to dependence
on wall shear stress (τw)
Flat plate flow, Re = 1.34 million, Ma = 0.30
Top: Distribution of Ar variable along flat plate
Bottom: Corresponding skin friction distribution
Ar Rough Wall Boundary
= f (k+ )
k+ =
U!ks
"
ks
= Roughness Height
U!
=
!w
#
Cf
=
!w
1
2 #U2
24. 24
Initial Validation Cases
• Flat plate with distributed sand-grain
roughness of varying heights (Feindt, 1956)
– Zero pressure gradient
– Adverse pressure gradient
• NACA 0012 with leading edge roughness
(Kerho & Bragg, 1997)
• Texas A&M tunnel, NACA 633-418
– Clean
– Distributed roughness
25. 25
Effect of Roughness Height on
Skin Friction
Flat plate, zero-pressure gradient, Feindt (1956)
Re
k =
!U
k
k
μ
26. 26
Comparison of Measured and
Predicted Effect of Roughness on
Transition
Flat plate, zero-pressure gradient, Feindt (1956)
Re
k =
!U
k
k
μ
27. 27
Comparison of Measured and
Predicted Boundary Layer Profiles
NACA 0012, Re = 1.25 × 106, α = 0°
1/2 in. roughness strip applied at s = 4 mm (x/c = 0.0018 - 0.0191)
• Wind tunnel measurement from Kerho & Bragg (1997)
• Slight lag in boundary layer development at early stations
• Profiles match well at later stations
28. 28
Comparison of Measured and
Predicted Boundary Layer States
NACA 0012, Re = 1.25 × 106, α = 0°
29. 29
Comparison of Measured and
Predicted Drag Polars
NACA 633-418, Clean surface, Re = 1.6 × 106, Texas A&M tunnel
30. 30
Comparison of Measured and
Predicted Transition Location
NACA 633-418, k/c = 170 × 10-6 @ x/c = -0.12:0.04, Re = 1.6 × 106,
Texas A&M tunnel
31. 31
Comparison of Measured and
Predicted Transition Location
NACA 633-418, k/c = 170 × 10-6 @ x/c = -0.12:0.04, Re = 2.4 × 106,
Texas A&M tunnel
32. 32
NREL 5-MW Rotor
• Geometry based on
6MW DOWEC rotor
– Conceptual off-shore
turbine design
– ECN (Energy Research Centre
of the Netherlands)
• Rotor diameter
truncated and hub
diameter reduced
33. 33
NREL 5-MW Rotor
• Rotor diameter =126 m
• Specific power = 401 W/m2
• 12.1 RPM
• 3 m hub diameter
• 61.5 m blade length
• 4.7 m max chord
• 13.3° inboard twist
• 3 m/s cut-in speed
• 25 m/s cut-out
• 12 m/s rated speed
34. 34
Performance Prediction Using
Computational Roughness Model
• Six different airfoil profiles
• Airfoils analyzed using OVERFLOW-2 in both
“clean” and “rough” configuration
• Roughness applied from 5% chord on lower to
5% chord on upper surface
• Height of roughness set at k/c = 240 × 10-6 ( k
= 0.24 mm or 0.001 in. for a chord of 1 m)
• Corresponds to relatively heavy soiling
36. Effect of Blade Roughness on Turbine
36
Power
WT-Perf, NREL 5-MW turbine, Roughness height k/c = 240 × 10-6
Percent power loss due to
degradation
Gross power loss due to
degradation
37. Effect of Blade Roughness on Turbine
37
Performance
NREL 5 MW turbine, Roughness height k/c = 240 × 10-6
Change in
Annual
Energy
Capture (%)*
Turbine
Capacity
Factor (rough)*
Turbine
Capacity
Factor (clean)*
Mean wind
speed at hub
height (m/s)
5.5 0.194 0.186 -4.26
6.0 0.241 0.231 -3.82
6.5 0.287 0.278 -3.43
7.0 0.334 0.323 -3.08
8.0 0.420 0.409 -2.80
8.5 0.459 0.449 -2.52
* = based on Raleigh distribution
40. 40
Conclusions
• Comprehensive study on effect of blade surface
erosion and soiling on wind turbine performance is
being conducted:
– Field measurements of blade erosion
– Wind tunnel testing (NACA 633-418)
– Computational modeling of surface roughness
• Study is providing significant aerodynamic insight into
surface roughness effects
• Newly developed model allows for specifying
roughness and analyzing impact on airfoil/blade/rotor
performance
• Computational modeling and wind tunnel studies will
be published in two Sandia reports in fall 2014
41. 41
Next Steps
• Near term:
– Implement improvements in computational roughness
model:
• Pressure gradient effect
• Distributed roughness density effect
– Calibrate/validate computational roughness model against
Texas A&M wind tunnel results
• Longer term:
– Evaluate (experimentally and computationally) roughness
sensitivity of higher t/c and higher L/D section shapes
– 3D RANS modeling of roughness effect on rotor
performance
– Implement boundary modifiers (VGs) in RANS and study
their effectiveness mitigating surface roughness effects
– Develop lower-order tool to evaluate surface roughness
effects and optimize boundary layer modifiers (size, location)
42. 42
Acknowledgements
• U.S. Department of Energy
• Sandia National Laboratories
• Warren and Leta Giedt Endowment
• National Science Foundation GK-12 RESOURCE
program