Multi-objective mixed-integer design optimization of planar inductors using surrogate modeling techniques

2017 ◽  
Author(s):  
Slawomir Koziel ◽  
Piotr Kurgan ◽  
John W. Handler
Keyword(s):  
Design Optimization ◽  
Surrogate Modeling ◽  
Mixed Integer ◽  
Multi Objective ◽  
Modeling Techniques

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.

Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components

2016 ◽  
Vol 33 (1) ◽  
pp. 184-201 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Surrogate Modeling ◽  
Simulation Models ◽  
Time Frame ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Trade Offs ◽  
Microwave Components ◽  
Modeling Techniques ◽  
Em Simulation

Purpose – Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues. Design/methodology/approach – Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures. Findings – It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Research limitations/implications – The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value – The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

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.

scholarly journals A Multi-Objective Approach toward Optimal Design of Sustainable Integrated Biodiesel/Diesel Supply Chain Based on First- and Second-Generation Feedstock with Solid Waste Use

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2261
Author(s):  
Evgeniy Ganev ◽  
Boyan Ivanov ◽  
Natasha Vaklieva-Bancheva ◽  
Elisaveta Kirilova ◽  
Yunzile Dzhelil
Keyword(s):  
Supply Chain ◽  
Optimal Design ◽  
Solid Waste ◽  
Second Generation ◽  
Agricultural Land ◽  
Optimization Problems ◽  
Biodiesel Production ◽  
Mixed Integer ◽  
Economic Criterion ◽  
Multi Objective

This study proposes a multi-objective approach for the optimal design of a sustainable Integrated Biodiesel/Diesel Supply Chain (IBDSC) based on first- (sunflower and rapeseed) and second-generation (waste cooking oil and animal fat) feedstocks with solid waste use. It includes mixed-integer linear programming (MILP) models of the economic, environmental and social impact of IBDSC, and respective criteria defined in terms of costs. The purpose is to obtain the optimal number, sizes and locations of bio-refineries and solid waste plants; the areas and amounts of feedstocks needed for biodiesel production; and the transportation mode. The approach is applied on a real case study in which the territory of Bulgaria with its 27 districts is considered. Optimization problems are formulated for a 5-year period using either environmental or economic criteria and the remainder are defined as constraints. The obtained results show that in the case of the economic criterion, 14% of the agricultural land should be used for sunflower and 2% for rapeseed cultivation, while for the environmental case, 12% should be used for rapeseed and 3% for sunflower. In this case, the price of biodiesel is 14% higher, and the generated pollutants are 6.6% lower. The optimal transport for both cases is rail.

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