Power aware compilation is a software approach to reducing energy consumption by optimizing code during compilation. It works by [1] analyzing assembly code produced during compilation to calculate clock cycles needed for execution, [2] developing optimization models to replace instructions with equivalents needing fewer cycles, and [3] generating optimized assembly code requiring less energy to run. This technique aims to make developers aware of programs' energy efficiency and influence more energy-conscious software development.
1) The document proposes a Byzantine fault-tolerant MapReduce approach for cloud-of-clouds environments to guarantee integrity and availability of data despite task corruptions or cloud outages.
2) A basic scheme replicates MapReduce tasks across clouds but has problems with computation, communication, and job execution control.
3) The proposed approach improves on the basic scheme by using deferred execution to address computation problems, digest communication to reduce inter-cloud communication, and a distributed job tracker to improve job execution control.
4) An evaluation of the approach on word count jobs across 3 clouds showed it added minimal overhead while providing fault tolerance.
1) The document discusses using collaborative tagging to design new web items that attract maximum tags. It defines the problem as predicting new item attributes to maximize desirable tags.
2) It proposes two algorithms - an Exact Two-Tier Top K algorithm (ETT) that runs in exponential time, and a Polynomial Time Approximation Scheme (PTAS) that runs in polynomial time with provable error bounds.
3) An experiment tests the algorithms on synthetic and real camera datasets. It analyzes performance quantitatively and uses a user study to qualitatively assess algorithm results.
Using Reliability Modeling and Accelerated Life Testing to Estimate Solar Inv...Accendo Reliability
RAMS 2013 paper by Golnaz Sanaie and Fred Schenkelberg
Three-phase inverters are physically large, complex and expensive elements of major solar power generation systems. The inverter converts DC power created by the photovoltaic (PV) panels to AC power suitable for adding to the power grid.
The inverters’ reliability testing is a complex task and relies on reliability block diagrams (RBD), vendor and field data, plus selecting accelerated life tests (ALT) based on critical elements of the product.
This paper illustrates a case study that developed an RBD, used field and vendor data, and includes the design and use of two ALTs. The result is a working framework or model that provides a reasonable estimate of the expected lifetime performance of the inverter. While any project similar to this, is always a work in progress, the examination of the decisions and inputs for the model proves valuable for the continued improvement of the model and resulting life predictions. This project provides an excellent real life example of reliability estimation having multitude of constraints including sample size, test duration, and field data and thus having to rely on all sources of available data starting from field and vendor data to theoretical component reliability calculations, ALT plan execution, failure analysis, and finally summarizing the results using RBD to estimate product expected lifetime. At the time of writing this paper, based on completion of system level ALT, an availability of 99.97% is valid over a 10 year period according to Ontario weather as the main installation base. This will be revisited once subsystem ALT is completed.
FPGA Implementation of Multiplier-less CDF-5/3 Wavelet Transform for Image Pr...IOSRJVSP
Most of the digital image processing application uses various domain transformation technique to convert time domain information to transform domain which will help to simplify the mathematical modeling. Discrete Wavelet Transform is one of the best transformation techniques. The time-frequency resolution makes this transform sensitive to both time and frequency which will give very good compression and decompression. In this paper, we propose FPGA implementation of multiplier-less CDF-5/3 wavelet transform for image processing application using System-Generator tool.To maintain low area and high frequency we use multiplier-less architecture for CDF-5/3 DWT for our implementation. The VHDL code for multiplier-less structure is fed to system generator tool using standard procedure and synthesis the structure to get the area and frequency
This document describes a technique called "multi-supply digital layout" that allows reliable back-annotation between digital blocks powered by different voltage supplies. It presents a design flow that uses standard CAD tools from RTL to layout. Digital blocks are grouped into voltage regions separated by isolation rings. Level shifter cells are used for voltage conversion at region interfaces. Libraries are generated for level shifters to integrate them into the digital flow for synthesis, simulation, and test. Floorplanning scripts automate placement of cells into the appropriate voltage regions.
This is my presentation at the OTM/CoopIS 2012 conference in Rome, Italy from Sep 10-14, 2012 about the dynamic event-driven actor framework (DERA) for enhancing the flexibility and scalability of service-based integration architecture. The paper can be downloaded at ResearchGate: http://bit.ly/NudGPL.
Design and Implementation of Low Power DSP Core with Programmable Truncated V...ijsrd.com
The programmable truncated Vedic multiplication is the method which uses Vedic multiplier and programmable truncation control bits and which reduces part of the area and power required by multipliers by only computing the most-significant bits of the product. The basic process of truncation includes physical reduction of the partial product matrix and a compensation for the reduced bits via different hardware compensation sub circuits. These results in fixed systems optimized for a given application at design time. A novel approach to truncation is proposed, where a full precision vedic multiplier is implemented, but the active section of the truncation is selected by truncation control bits dynamically at run-time. Such architecture brings together the power reduction benefits from truncated multipliers and the flexibility of reconfigurable and general purpose devices. Efficient implementation of such a multiplier is presented in a custom digital signal processor where the concept of software compensation is introduced and analyzed for different applications. Experimental results and power measurements are studied, including power measurements from both post-synthesis simulations and a fabricated IC implementation. This is the first system-level DSP core using a high speed Vedic truncated multiplier. Results demonstrate the effectiveness of the programmable truncated MAC (PTMAC) in achieving power reduction, with minimum impact on functionality for a number of applications. On comparison with the previous parallel multipliers Vedic should be much more fast and area should be reduced. Programmable truncated Vedic multiplier (PTVM) should be the basic part implemented for the arithmetic and PTMAC units.
Power aware compilation is a software approach to reducing energy consumption by optimizing code during compilation. It works by [1] analyzing assembly code produced during compilation to calculate clock cycles needed for execution, [2] developing optimization models to replace instructions with equivalents needing fewer cycles, and [3] generating optimized assembly code requiring less energy to run. This technique aims to make developers aware of programs' energy efficiency and influence more energy-conscious software development.
1) The document proposes a Byzantine fault-tolerant MapReduce approach for cloud-of-clouds environments to guarantee integrity and availability of data despite task corruptions or cloud outages.
2) A basic scheme replicates MapReduce tasks across clouds but has problems with computation, communication, and job execution control.
3) The proposed approach improves on the basic scheme by using deferred execution to address computation problems, digest communication to reduce inter-cloud communication, and a distributed job tracker to improve job execution control.
4) An evaluation of the approach on word count jobs across 3 clouds showed it added minimal overhead while providing fault tolerance.
1) The document discusses using collaborative tagging to design new web items that attract maximum tags. It defines the problem as predicting new item attributes to maximize desirable tags.
2) It proposes two algorithms - an Exact Two-Tier Top K algorithm (ETT) that runs in exponential time, and a Polynomial Time Approximation Scheme (PTAS) that runs in polynomial time with provable error bounds.
3) An experiment tests the algorithms on synthetic and real camera datasets. It analyzes performance quantitatively and uses a user study to qualitatively assess algorithm results.
Using Reliability Modeling and Accelerated Life Testing to Estimate Solar Inv...Accendo Reliability
RAMS 2013 paper by Golnaz Sanaie and Fred Schenkelberg
Three-phase inverters are physically large, complex and expensive elements of major solar power generation systems. The inverter converts DC power created by the photovoltaic (PV) panels to AC power suitable for adding to the power grid.
The inverters’ reliability testing is a complex task and relies on reliability block diagrams (RBD), vendor and field data, plus selecting accelerated life tests (ALT) based on critical elements of the product.
This paper illustrates a case study that developed an RBD, used field and vendor data, and includes the design and use of two ALTs. The result is a working framework or model that provides a reasonable estimate of the expected lifetime performance of the inverter. While any project similar to this, is always a work in progress, the examination of the decisions and inputs for the model proves valuable for the continued improvement of the model and resulting life predictions. This project provides an excellent real life example of reliability estimation having multitude of constraints including sample size, test duration, and field data and thus having to rely on all sources of available data starting from field and vendor data to theoretical component reliability calculations, ALT plan execution, failure analysis, and finally summarizing the results using RBD to estimate product expected lifetime. At the time of writing this paper, based on completion of system level ALT, an availability of 99.97% is valid over a 10 year period according to Ontario weather as the main installation base. This will be revisited once subsystem ALT is completed.
FPGA Implementation of Multiplier-less CDF-5/3 Wavelet Transform for Image Pr...IOSRJVSP
Most of the digital image processing application uses various domain transformation technique to convert time domain information to transform domain which will help to simplify the mathematical modeling. Discrete Wavelet Transform is one of the best transformation techniques. The time-frequency resolution makes this transform sensitive to both time and frequency which will give very good compression and decompression. In this paper, we propose FPGA implementation of multiplier-less CDF-5/3 wavelet transform for image processing application using System-Generator tool.To maintain low area and high frequency we use multiplier-less architecture for CDF-5/3 DWT for our implementation. The VHDL code for multiplier-less structure is fed to system generator tool using standard procedure and synthesis the structure to get the area and frequency
This document describes a technique called "multi-supply digital layout" that allows reliable back-annotation between digital blocks powered by different voltage supplies. It presents a design flow that uses standard CAD tools from RTL to layout. Digital blocks are grouped into voltage regions separated by isolation rings. Level shifter cells are used for voltage conversion at region interfaces. Libraries are generated for level shifters to integrate them into the digital flow for synthesis, simulation, and test. Floorplanning scripts automate placement of cells into the appropriate voltage regions.
This is my presentation at the OTM/CoopIS 2012 conference in Rome, Italy from Sep 10-14, 2012 about the dynamic event-driven actor framework (DERA) for enhancing the flexibility and scalability of service-based integration architecture. The paper can be downloaded at ResearchGate: http://bit.ly/NudGPL.
Design and Implementation of Low Power DSP Core with Programmable Truncated V...ijsrd.com
The programmable truncated Vedic multiplication is the method which uses Vedic multiplier and programmable truncation control bits and which reduces part of the area and power required by multipliers by only computing the most-significant bits of the product. The basic process of truncation includes physical reduction of the partial product matrix and a compensation for the reduced bits via different hardware compensation sub circuits. These results in fixed systems optimized for a given application at design time. A novel approach to truncation is proposed, where a full precision vedic multiplier is implemented, but the active section of the truncation is selected by truncation control bits dynamically at run-time. Such architecture brings together the power reduction benefits from truncated multipliers and the flexibility of reconfigurable and general purpose devices. Efficient implementation of such a multiplier is presented in a custom digital signal processor where the concept of software compensation is introduced and analyzed for different applications. Experimental results and power measurements are studied, including power measurements from both post-synthesis simulations and a fabricated IC implementation. This is the first system-level DSP core using a high speed Vedic truncated multiplier. Results demonstrate the effectiveness of the programmable truncated MAC (PTMAC) in achieving power reduction, with minimum impact on functionality for a number of applications. On comparison with the previous parallel multipliers Vedic should be much more fast and area should be reduced. Programmable truncated Vedic multiplier (PTVM) should be the basic part implemented for the arithmetic and PTMAC units.
Optimization without Statistical DOE 2015 03 23Paul Fishbein
The document discusses various techniques for optimization without statistical design of experiments (DOE). It defines the optimum as the best compromise among outcomes for each measured result. Desirability functions combine multiple criteria into one number. Noise must be reduced before finding the optimum using techniques like statistical process control (SPC). One factor at a time optimization only works when factors do not interact. Randomized experimental variable optimization (EVOP) and simplex optimization are introduced as methods that can find optima with fewer experiments.
This webinar looks at answering this question, not by going deeply into the various designed experiment types, but from a process improvement perspective. Progressing from a definition of a designed experiment, to Why and when do I need a designed experiment?, What’s the concept? (and why can’t I do a “one-factor-at-a-time” series of experiments? , to Will this tool solve REAL WORLD problems?
Autologous and Allogeneic Cell Therapy Industrialisation – Overcoming Clinical Manufacturing Hurdles Early
A presentation by Chief Operating Officer, Dr Stephen Ward
A study on DOE of tubular rear axle twist beam using HyperStudyAltair
In terms of the compliance with new legal requirements and reduction of greenhouse gas effect , automotive industry focuses on weight reduction of vehicle components. Furthermore, manufacturers studies on new concept designs, processes and new generation materials without compromising the safety of the vehicle components. The optimization tools take up significant place in the automotive industry to analyze feasibility of parts quickly due to competitive market requirements. Furthermore, Hyperstudy offers a solution for rapid DOE opportunity in the product development cycle, minimizing optimization challenges and also costs.
In this study, DOE methodology is used in order to optimize tubular rear axle twist beam which meets forces from ground to car body and belongs to semi-independent suspension system by using Hyperstudy.
Speakers
Metin Çalli, FEA Responsible, COSKUNOZ A.S. R&D Department
S2 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
An efficient Design of Experiment (DOE)Mayank Garg
The document describes an optimal design of experiments (DOE) approach for cell culture media optimization. It discusses screening significant factors using a custom design with fewer experiments than a conventional approach. It then presents a case study where an optimal approach identified interactions and found the media optimization pathway with fewer experiments compared to conventional methods.
S3 - Process product optimization design experiments response surface methodo...CAChemE
Session 3/4 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
S4 - Process/product optimization using design of experiments and response su...CAChemE
Session 3 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Schedule and details:
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Selecting experimental variables for response surface modelingSeppo Karrila
This document discusses selecting variables and designing experiments for response surface modeling. It recommends beginning with identifying all possible factors, then controlling some and selecting others to experiment with using a design with three levels per variable, like a Box-Behnken design. Response surface modeling fits a quadratic surface to the experimental results rather than optimizing one variable at a time, which can miss the true optimal conditions. The goal is to approximate the maximum or minimum response across variable levels through designed experiments and modeling rather than sequential optimization of individual factors.
DOE Applications in Process Chemistry Presentationsaweissman
The document discusses how design of experiments (DOE) can help optimize chemical reactions and processes in a more efficient manner than traditional trial-and-error approaches. It provides examples of how pharmaceutical companies have used DOE to reduce costs and improve yields for key reaction steps. DOE allows researchers to systematically vary multiple reaction factors at once to understand their effects and interactions, requiring fewer total experiments than one-factor-at-a-time testing.
- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
This document discusses optimization techniques used in pharmaceutical development. It defines optimization as making a formulation or process perfect by finding the best use of resources while considering all influencing factors. It describes independent and dependent variables, different optimization methods like evolutionary operation, simplex method, and statistical experimental designs including factorial, response surface, and Plackett-Burman designs. The advantages of optimization include determining important variables, measuring interactions, and allowing extrapolation to find the best product. Optimization has applications in formulation development, dissolution testing, tablet coating, and capsule preparation.
RSM Design Solutions is Facade Consultant Specializing in all Facade Element Treatments. Founded in 2008, our design and consulting services
for state-of-the-art building envelope systems. RSM Design Solutions operates as a 100% Employee-owned and operated organization.
Management of the Company has come from within the ranks of the staff, with design professionals who are completely and solely dedicated to
design, engineering, and consulting for the building envelope. Our engineers participate with the whole design team to generate a balanced design
which satisfies a wide range of performance and aesthetic requirements. Facades form the envelope between the internal and external
environments and the design fundamentally affects the performance of the whole building. Good Facade design contributes to optimized
performance, reduced energy costs and improved comfort levels. Our goal is to integrate the thermal, acoustic, fire and ventilation performance
requirements, while honoring the aesthetic intent. All these objectives are to be delivered within the context of environmental sustain ability and
economic viability.
Our extensive experience, technical expertise and professional focus ensures our clients get the best possible service. Our policy is to form long
term associations with our clients and working closely in partnership throughout the period of the project. We endeavor to fully understand the
client’s objectives and utilize our knowledge and expertise to congregate their goals.
As a professional services firm, we provide design and engineering for some of the most well-known Facade cladding manufacturers, providing
design support for established and tested systems, creating new systems, and assisting in development of innovative new approaches to cladding
execution.
We work closely with Building Owners &Developers,Architects,PMCand Specialist Facade Contractors
For Building Owners&Developers ...We develop systems to guide you selecting appropriate&economical system for your project. .........
ForArchitects&PMC ......We assist Architects in making FacadeDetails, Tender andProject Specification. ............................
For Specialist Facade Contractor We provide complete Design-Developmentwith Project planning. ...............
Our Mission
To provide the efficient, cost effective and innovative design & engineering solutions that deal with the latest developments in the facade technology,
raising the quality standards and persistently setting a new benchmark in the facade engineering industry.
OurVision
To be a prominent global provider of facade consultancy, design and engineering services and to play an significant role in the development of our
industry.
This document summarizes a presentation on using gradient-based optimization methods to optimize radiative heat transfer design. It describes using ANSYS simulations coupled with the eArtius gradient optimization tool to minimize temperature variation across substrates during heating. The method finds solutions with only a few design evaluations, making it suitable when computational constraints limit evaluations. Initial designs are improved through a "multi-step fast start" approach to quickly guide the search toward better regions. The gradient-based approach automates the optimization process and outperforms human experts in minimizing non-uniformity.
Multi-Objective Optimization of Solar Cells Thermal Uniformity Using Combined...eArtius, Inc.
The document summarizes a case study by Intevac on optimizing the thermal uniformity of solar cells during the heating process using multi-physics simulation and multi-objective optimization. ANSYS Workbench was used to model the thermal conduction-radiation heating process. ModeFrontier and eArtius tools were then used to optimize lamp parameters like height, temperature, and spacing to minimize temperature variation across substrates while maintaining an optimal operating temperature, utilizing the hybrid multi-gradient explorer algorithm. This combined approach leveraged the strengths of computational modeling, design of experiments, and global multi-objective optimization to efficiently obtain a thermally optimized design.
This document discusses using ANSYS, modeFrontier, and eArtius software to optimize the thermal uniformity of solar cells through multi-objective optimization of lamp heating parameters. The optimization aims to minimize temperature differences across substrates during heating while meeting temperature requirements. ModeFrontier acts as an optimization scheduler and data analyzer while eArtius provides multi-objective optimization methods to guide the selection of better input variables to satisfy the design objectives.
This document discusses patterns of parallel programming. It begins by explaining why parallel programming is necessary due to limitations of Moore's Law like power consumption and wire delays. It then covers key terms and measures for parallel programming like work, span, speedup and parallelism. Common patterns are overviewed like pipeline, producer-consumer, and Map-Reduce. It warns of dangers like race conditions, deadlocks and starvation. Finally, it provides references for further reading on parallel programming patterns and approaches.
Optimization without Statistical DOE 2015 03 23Paul Fishbein
The document discusses various techniques for optimization without statistical design of experiments (DOE). It defines the optimum as the best compromise among outcomes for each measured result. Desirability functions combine multiple criteria into one number. Noise must be reduced before finding the optimum using techniques like statistical process control (SPC). One factor at a time optimization only works when factors do not interact. Randomized experimental variable optimization (EVOP) and simplex optimization are introduced as methods that can find optima with fewer experiments.
This webinar looks at answering this question, not by going deeply into the various designed experiment types, but from a process improvement perspective. Progressing from a definition of a designed experiment, to Why and when do I need a designed experiment?, What’s the concept? (and why can’t I do a “one-factor-at-a-time” series of experiments? , to Will this tool solve REAL WORLD problems?
Autologous and Allogeneic Cell Therapy Industrialisation – Overcoming Clinical Manufacturing Hurdles Early
A presentation by Chief Operating Officer, Dr Stephen Ward
A study on DOE of tubular rear axle twist beam using HyperStudyAltair
In terms of the compliance with new legal requirements and reduction of greenhouse gas effect , automotive industry focuses on weight reduction of vehicle components. Furthermore, manufacturers studies on new concept designs, processes and new generation materials without compromising the safety of the vehicle components. The optimization tools take up significant place in the automotive industry to analyze feasibility of parts quickly due to competitive market requirements. Furthermore, Hyperstudy offers a solution for rapid DOE opportunity in the product development cycle, minimizing optimization challenges and also costs.
In this study, DOE methodology is used in order to optimize tubular rear axle twist beam which meets forces from ground to car body and belongs to semi-independent suspension system by using Hyperstudy.
Speakers
Metin Çalli, FEA Responsible, COSKUNOZ A.S. R&D Department
S2 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
An efficient Design of Experiment (DOE)Mayank Garg
The document describes an optimal design of experiments (DOE) approach for cell culture media optimization. It discusses screening significant factors using a custom design with fewer experiments than a conventional approach. It then presents a case study where an optimal approach identified interactions and found the media optimization pathway with fewer experiments compared to conventional methods.
S3 - Process product optimization design experiments response surface methodo...CAChemE
Session 3/4 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
S4 - Process/product optimization using design of experiments and response su...CAChemE
Session 3 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Schedule and details:
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
S1 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Selecting experimental variables for response surface modelingSeppo Karrila
This document discusses selecting variables and designing experiments for response surface modeling. It recommends beginning with identifying all possible factors, then controlling some and selecting others to experiment with using a design with three levels per variable, like a Box-Behnken design. Response surface modeling fits a quadratic surface to the experimental results rather than optimizing one variable at a time, which can miss the true optimal conditions. The goal is to approximate the maximum or minimum response across variable levels through designed experiments and modeling rather than sequential optimization of individual factors.
DOE Applications in Process Chemistry Presentationsaweissman
The document discusses how design of experiments (DOE) can help optimize chemical reactions and processes in a more efficient manner than traditional trial-and-error approaches. It provides examples of how pharmaceutical companies have used DOE to reduce costs and improve yields for key reaction steps. DOE allows researchers to systematically vary multiple reaction factors at once to understand their effects and interactions, requiring fewer total experiments than one-factor-at-a-time testing.
- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
This document discusses optimization techniques used in pharmaceutical development. It defines optimization as making a formulation or process perfect by finding the best use of resources while considering all influencing factors. It describes independent and dependent variables, different optimization methods like evolutionary operation, simplex method, and statistical experimental designs including factorial, response surface, and Plackett-Burman designs. The advantages of optimization include determining important variables, measuring interactions, and allowing extrapolation to find the best product. Optimization has applications in formulation development, dissolution testing, tablet coating, and capsule preparation.
RSM Design Solutions is Facade Consultant Specializing in all Facade Element Treatments. Founded in 2008, our design and consulting services
for state-of-the-art building envelope systems. RSM Design Solutions operates as a 100% Employee-owned and operated organization.
Management of the Company has come from within the ranks of the staff, with design professionals who are completely and solely dedicated to
design, engineering, and consulting for the building envelope. Our engineers participate with the whole design team to generate a balanced design
which satisfies a wide range of performance and aesthetic requirements. Facades form the envelope between the internal and external
environments and the design fundamentally affects the performance of the whole building. Good Facade design contributes to optimized
performance, reduced energy costs and improved comfort levels. Our goal is to integrate the thermal, acoustic, fire and ventilation performance
requirements, while honoring the aesthetic intent. All these objectives are to be delivered within the context of environmental sustain ability and
economic viability.
Our extensive experience, technical expertise and professional focus ensures our clients get the best possible service. Our policy is to form long
term associations with our clients and working closely in partnership throughout the period of the project. We endeavor to fully understand the
client’s objectives and utilize our knowledge and expertise to congregate their goals.
As a professional services firm, we provide design and engineering for some of the most well-known Facade cladding manufacturers, providing
design support for established and tested systems, creating new systems, and assisting in development of innovative new approaches to cladding
execution.
We work closely with Building Owners &Developers,Architects,PMCand Specialist Facade Contractors
For Building Owners&Developers ...We develop systems to guide you selecting appropriate&economical system for your project. .........
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This document summarizes a presentation on using gradient-based optimization methods to optimize radiative heat transfer design. It describes using ANSYS simulations coupled with the eArtius gradient optimization tool to minimize temperature variation across substrates during heating. The method finds solutions with only a few design evaluations, making it suitable when computational constraints limit evaluations. Initial designs are improved through a "multi-step fast start" approach to quickly guide the search toward better regions. The gradient-based approach automates the optimization process and outperforms human experts in minimizing non-uniformity.
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1. Fast Design Optimization Strategy for
Radiative Heat Transfer
using ANSYS and eArtius Gradient Based
Optimization Method – Pt. 2
Vladimir Kudriavtsev, Terry Bluck (Intevac)
Vladimir Sevastyanov
(eArtius, Irvine, CA)
ANSYS Regional Users Conference, “Engineering the
System”, Irvine, CA, Oct. 6, 2011
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2. Outline
Part1 of this work presented at ANSYS Users Conference
2011 (Santa Clara) and devoted to hybrid genetic and
gradient based approach (HMGE)
http://www.slideshare.net/vvk0/optimization-intevac-aug23-7f
Part2 of this work is presented here and is devoted to pure
gradient based method, which is best used when only
limited number of design evaluations is possible (due to
CPU time limitations or other reasons)
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3. Current Computational Design Process
8 threads i7
CPU
Computer DELAY Ingenious
Solutions
240 cores TESLA Graphic
Processing Unit GPU (x2)
Human Thinking
and Analysis
slowest component
fastest component (meetings, reviews,
and grows exponentially faster
alignments, cancelations)
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4. Why Optimization by Computer?
Human can not match computer in repetitive tasks and consistency.
Assuming computational problem takes 4 hours of CPU time,
then in one day (between 8AM to 8 AM) computer is capable of
producing 6 design evaluations, with 42 designs completed in
just 7 work days.
Coupled with multi-processing capability of i7 workstation this number can
easily be multiplied by factors ranging from two to six.
Computer will work during the weekend; it will work when user is on vacation,
on sick leave or on business trip.
Personal “super computer” cost is now
inconsequential for the bottom line.
Software cost sky-rocketed, and its ROI and
utilization efficiency is now most important.
Computer needs algorithmic analogy of “human brain”
to self-guide solution steps.
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5. New Paradigm and CPU Time
New paradigm of multi-objective computational design is now
being born.
No longer designer needs to approach it through “trial-and-error”
simulations, but can rather use “artificial intelligence” of optimization
method to automatically seek and to find best combination of input
parameters (design). Depending on problem size (CPU time) this process
can take from minutes to weeks.
However, now engineer can view tens and hundreds of possible solutions,
automatically singling first truly best designs and then evaluate design
trade-offs between conflicting objectives (Pareto Frontier).
In many instances, examining dozens and hundreds of
computational designs is simply time prohibitive. What to do then?
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6. Intevac c-Si Technology
Substrate Motion
Direction
Si Substrates move on conveyer and heated
Motion Direction
http://www.intevac.com
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7. Problem Formulation
Minimize thermal variation across single substrate and across a group
of substrates during radiant heating stage (TempDiff)
Operate in required process temperature window, T-dev1<Top<T+dev2
Optimization Formulation ANSYS WB Formulation:
Top=400 deg.C
min (TempDiff)
min abs(Tmax-Top) & min abs(Tmin-Top)
Constraints to determine design feasibility:
T<Tmax.constr & T>Tmin.constr, where
Tmin.constr= Top-dev1, Tmax.constr=Top+dev2
If dev1 and dev2 are small, then optimization problem is very restrictive.
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8. Problem Analogy – Hidden Valley in the Mountains
Narrow optimum
Sub-Optimal
range
Optimal
range
xT^4 nonlinear
steep change
Narrow process window
Gradient method requires path, to enter narrow optimal range (due to nonlinearity)
it requires guidance or coincidence. Guidance comes from the previous history
(steps taken before, gradients) and coincidence from DOE or random mutations.
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9. MGP Method-- Analogy
DOE #N
DOE #2
MGP
Initial DOE #3
Tolerance
Design Space
DOE #1
Initial Design
MGP Design Vector Calculated using
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10. Problem Parameters – Geometry and
Temperature
<Tempdiff> =Tmax-Tmin
between 3 substrates
Si subst
minus rate T1
gap
T2
plus
Lamp Bank 1
Lamp Bank 2
lamps
height T1-radiation temperature of first
substrates lamp array;
T2-radiation temperature of
T1,T2 control heat flux from lamps. second lamp array;
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11. Thermal Heating (Radiation) Solution
Interplay between
two lamp arrays
Substrate Motion
Lamp Bank 1 Lamp Bank 2 Direction
Multi-Step Transient History
Transient Heating Scenario: Row1 of substrates is first heated by Lamp Bank1,
then these Substrates moved to Lamp Bank2 and get heated again till desired Top=400 deg.C is
reached. Simultaneously, new substrates with T=Tambient populate Row1 and get heated.
Thus, Row1 heats from 22 to 250 deg.c and Row 2 from 250 to 400 deg.C.
at time t=3.5 sec Row1 T is reset at 22 deg.C; Row2 T is reset at 250 deg.C.
at time t=0 sec Row1 T is set at 22 deg.C; Row2 T is set at 250 deg.C.
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12. About This Study
This Study Consists of Two Parts. In Part 1 (presented in Santa Clara) we
focused on hybrid genetic and gradient based method (HMGE). It has lots of
positive sides, but generally requires many design points, thus is less suitable
for quick improvement studies typical for computational models that require
many hours of CPU time.
In Part2 (presented here) we focus on gradient based approach (MGP) that is
generally capable to produce design improvements in just a few design
evaluations.
In our study we used modeFrontier as optimization enabling (Scheduler) and
statistical data post-processing tool and eArtius multi-objective optimization
methods plug-in tool to guide continuous process of selecting better input
variables to satisfy multiple design objectives.
This process follows “fire and forget” principle.
Gradient based computer thinking combines advantages of precise analytics
with human like decision making (selecting roads that lead to improvement,
avoiding weak links, pursuing best options, connecting dots).
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25. MGP (TempDiff+, SubMax400+)
Temp Diff,
deg. C Objective 1 (main consideration)
34.6 1
Obj1
Rapid SubMax400,
improvement deg. C Objective 2 ( second consideration)
1
6.7
5
Design ID #
Design ID #
Start from Arbitrary (Bad) Design
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26. MGP –Start from Good Point (Obj1)
12 Objective 1 (main consideration)
Obj1
2 Improvement
Obj1 got worse, Obj2 improved
SubMax400,
deg. C
Temp Diff, 23
deg. C Objective 2 (secondary consideration)
5.9 1
2
Start from Good Design (by Obj1)
3.2
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27. MGP: Start from Small DOE (12 designs)
Temp Diff,
deg. C Temp Diff, Obj1
19.7 deg. C 19.7
Obj1 DOE Points (designs) Pareto
DOE
Obj2
Obj2
40
converged
improvement
5.8
Design ID # Obj1
10
#13, first MGP point (design)
0.6
improvement
Obj2
Design ID #
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28. MGP: First Design after DOE (detail of previous slide)
19.8 Objective 2 (secondary consideration)
Objective 1 (main consideration)
6
DOE Points (designs)
6
improvement
~with best DOE
Objective 1
improvement 6
Objective 2 (secondary consideration)
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29. MGP: Start from Several Best Points
Temp Diff,
deg. C
Temp Diff,
Obj1 deg. C
Objective 1
9.97
Best Initial Points
Best Initial Points
5.83
5.44
Improvement
4.87
Improvement Improvement
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30. “Sequence Jumping” DOE for MGP
#3
Step2
Step3 #1
#2
Initial Point
Step2 #2 #3
#1
Step1
Step3
Step1
Initial Point #1, #2,#3 –best points on each step for
objective marked “+” (preferred objective)
Design Space
Multi-Step Fast Start
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31. Multi-Step Fast Start MGP
TempDiff, Objective 1 (main consideration) TempDiff,
deg. C
10.9 deg. C
33.9
1
Design
Improved -Stop Design
Improved -Stop
10.9
5.9
2
Step1 with Initial Tolerance Step2: Tolerance Reduced
Steps continue as long as improvement is reached within short number
of designs
Quick Search for Good Starting Point: multi-step “short” MGP instead of initial DOE.
Advantage: multi-step approach has solution feedback, DOE does not.
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32. Multi-Step Fast Start MGP (last step)
TempDiff, Objective 1 (main consideration)
deg. C 6
Results got worse,
No need to continue
multi-step improvement
any more
2
5.9 1
Step 3: Tolerance Reduced
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33. Computer vs. Human
In head-to-head competitions best “human guided” (case-by-case) studies
resulted in system design with ±10-20 deg.C thermal uniformity and took
several weeks to accomplish, while FAST MGP method based computer
optimization approach allowed to quickly yield design solutions capable
of reaching ± 3 deg. C. It took only 8-20 design evaluations for CPU to
“independently” accomplish this task.
Such an approach will not allow to uniformly cover entire design space,
but will work for engineers who need to find quick improvements for their
designs and work with large computational models that take many CPU
hours to solve (i.e. hundreds of design evaluations are not an option).
We can conclude that “Optimization Equals Innovation”!
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35. Acknowledgement
Authors are thankful to ESTECO engineers for developing
eArtius MGP modeFrontier plug-in; to Alberto Bassanese
(ESTECO) for introducing and helping with modeFrontier; to
ANSYS Distributor in Bay Area Ozen Engineering
www.ozeninc.com (Kaan Diviringi, Chris Cowan and Metin
Ozen) for help and dedicated support with ANSYS Workbench
model development and integration with modeFrontier .
Part 1 of this presentation is devoted to thermal optimization when CPU time budget is more flexible to
allow many computational design evaluations. It was presented at Santa Clara Aug. 2011 ANSYS Users
Conference
http://www.slideshare.net/vvk0/optimization-intevac-aug23-7f
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