Exploring the pareto front of multi-objective single-phase PFC rectifier design optimization - 99.2% efficiency vs. 7kW/din3 power density

2009 ◽  
Author(s):  
J. Kolar ◽  
J. Biela ◽  
J. Minibock
Keyword(s):  
Design Optimization ◽  
Power Density ◽  
Pareto Front ◽  
Single Phase ◽  
Multi Objective

Multi-Objective Design Optimization of Topology and Performance of Branching Networks of Cooling Passages

2006 ◽  
Author(s):  
Maickel Gonzalez ◽  
Ramon J. Moral ◽  
Thomas J. Martin ◽  
Debasis Sahoo ◽  
George S. Dulikravich ◽  
...  
Keyword(s):  
Pressure Drop ◽  
Design Optimization ◽  
Total Pressure ◽  
Pareto Front ◽  
Total Heat ◽  
Pareto Optimal ◽  
Pumping Power ◽  
Multi Objective ◽  
Branching Networks ◽  
Number Of Branches

The objective of this study was to develop an automatic, self-sufficient, preliminary design algorithm for optimization of topologies of branching networks of internal cooling passages. The software package includes a random branches generator, a quasi 1-D thermo-fluid analysis code COOLNET, and multi-objective hybrid optimizer. COOLNET analysis software has the same trends as shown in an earlier publication depicting the results of a similar analysis code used by Pratt & Whitney. The hybrid multi-objective optimization code was verified against classical test cases involving multiple objectives. The number of branches per level was optimized in order to minimize coolant mass flow rate, total pressure drop, and maximize total heat removed. Optimization with four levels of fractal branching channel networks was tested. This optimization varied the number of branching channels extending from each single channel. COOLNET needed approximately forty iterations on average to analyze each configuration. The number of iterations necessary for each geometry depended on the number of branches per configuration. The hybrid multi-objective optimizer needed 500 iterations to create a converged Pareto front of optimized branching network configurations for the case of four branching levels. A population of 60 designs was used. The total number of function evluations analyzed was 30,000. The entire design optimization process takes approximately 3 hours on a single 3.0 GHz Pentium IV processor. In this work the total number of Pareto-optimal designs was 100. After finding the Pareto front points, the user has to decide which optimized cooling network configuration is the best for the desired application. It was demonstrated that this can be accomplished by utilizing Pareto-optimal solutions to create a curve representing pumping power vs. total heat removed and by observing which designs provide favorable break-even energy transfer. The magnitude of the ratio of heat transferred to total pressure drop and ratio of heat transfer to pumping power could be further increased by incorporating the channel’s hydraulic diameter, cross sectional area, lengths, and wall roughness as optimization variables.

Expected-Improvement-Based Methods for Adaptive Sampling in Multi-Objective Optimization Problems

2016 ◽  
Author(s):  
Jesper Kristensen ◽  
You Ling ◽  
Isaac Asher ◽  
Liping Wang
Keyword(s):  
Convex Hull ◽  
Design Optimization ◽  
Adaptive Sampling ◽  
Pareto Front ◽  
Performance Metrics ◽  
Sampling Methods ◽  
Optimization Problems ◽  
Expected Improvement ◽  
Multi Objective Optimization ◽  
Multi Objective

Adaptive sampling methods have been used to build accurate meta-models across large design spaces from which engineers can explore data trends, investigate optimal designs, study the sensitivity of objectives on the modeling design features, etc. For global design optimization applications, adaptive sampling methods need to be extended to sample more efficiently near the optimal domains of the design space (i.e., the Pareto front/frontier in multi-objective optimization). Expected Improvement (EI) methods have been shown to be efficient to solve design optimization problems using meta-models by incorporating prediction uncertainty. In this paper, a set of state-of-the-art methods (hypervolume EI method and centroid EI method) are presented and implemented for selecting sampling points for multi-objective optimizations. The classical hypervolume EI method uses hyperrectangles to represent the Pareto front, which shows undesirable behavior at the tails of the Pareto front. This issue is addressed utilizing the concepts from physical programming to shape the Pareto front. The modified hypervolume EI method can be extended to increase local Pareto front accuracy in any area identified by an engineer, and this method can be applied to Pareto frontiers of any shape. A novel hypervolume EI method is also developed that does not rely on the assumption of hyperrectangles, but instead assumes the Pareto frontier can be represented by a convex hull. The method exploits fast methods for convex hull construction and numerical integration, and results in a Pareto front shape that is desired in many practical applications. Various performance metrics are defined in order to quantitatively compare and discuss all methods applied to a particular 2D optimization problem from the literature. The modified hypervolume EI methods lead to dramatic resource savings while improving the predictive capabilities near the optimal objective values.

On deterministic procedures for low-cost multi-objective design optimization of miniaturized impedance matching transformers

2017 ◽  
Vol 34 (2) ◽  
pp. 403-419 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Design Optimization ◽  
Pareto Front ◽  
Low Cost ◽  
Impedance Matching ◽  
Simulation Models ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Modeling Techniques ◽  
Em Simulation

Purpose This paper aims to investigate deterministic strategies for low-cost multi-objective design optimization of compact microwave structures, specifically, impedance matching transformers. The considered methods involve surrogate modeling techniques and variable-fidelity electromagnetic (EM) simulations. In contrary to majority of conventional approaches, they do not rely on population-based metaheuristics, which permit lowering the design cost and improve reliability. Design/methodology/approach There are two algorithmic frameworks presented, both fully deterministic. The first algorithm involves creating a path covering the Pareto front and arranged as a sequence of patches relocated in the course of optimization. Response correction techniques are used to find the Pareto front representation at the high-fidelity EM simulation level. The second algorithm exploits Pareto front exploration where subsequent Pareto-optimal designs are obtained by moving along the front by means of solving appropriately defined local constrained optimization problems. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving expensive EM-simulation models of impedance transformer structures. Findings It is possible, by means of combining surrogate modeling techniques and constrained local optimization, to identify the set of alternative designs representing Pareto-optimal solutions, in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Multi-objective optimization for the considered class of structures can be realized using deterministic approaches without defaulting to evolutionary methods. Research limitations/implications The present study can be considered a step toward further studies on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value The proposed algorithmic solutions proved useful for expedited multi-objective design optimization of miniaturized microwave structures. The problem is extremely challenging when using conventional methods, in particular evolutionary algorithms. To the authors’ knowledge, this is one of the first attempts to investigate deterministic surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

scholarly journals A Multi-Objective Design Optimization for a Permanent Magnet Synchronous Machine with Hairpin Winding Intended for Transport Applications

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3162
Author(s):  
Mohammad Soltani ◽  
Stefano Nuzzo ◽  
Davide Barater ◽  
Giovanni Franceschini
Keyword(s):  
Design Optimization ◽  
Power Density ◽  
Electric Propulsion ◽  
Heat Dissipation ◽  
Proximity Effects ◽  
Electrical Machine ◽  
Potential Candidate ◽  
Electric Drives ◽  
Multi Objective ◽  
Good Heat

Nowadays, interest in electric propulsion is increasing due to the need to decarbonize society. Electric drives and their components play a key role in this electrification trend. The electrical machine, in particular, is seeing an ever-increasing development and extensive research is currently being dedicated to the improvement of its efficiency and torque/power density. Among the winding methods, hairpin technologies are gaining extensive attention due to their inherently high slot fill factor, good heat dissipation, strong rigidity, and short end-winding length. These features make hairpin windings a potential candidate for some traction applications which require high power and/or torque densities. However, they also have some drawbacks, such as high losses at high frequency operations due to skin and proximity effects. In this paper, a multi-objective design optimization is proposed aiming to provide a fast and useful tool to enhance the exploitation of the hairpin technology in electrical machines. Efficiency and volume power density are considered as main design objectives. Analytical and finite element evaluations are performed to support the proposed methodology.

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

Sign in / Sign up

Export Citation Format

Share Document