The Multiobjective Design Optimization of pMUT Hydrophone

2015 ◽  
Vol 727-728 ◽  
pp. 660-665
Author(s):  
Shun Hsyung Chang ◽  
Fu Tai Wang ◽  
Jiing Kae Wu ◽  
Sergey N. Shevtsov ◽  
Igor V. Zhilyaev ◽  
...  
Keyword(s):  
Finite Element ◽  
Frequency Response ◽  
Design Optimization ◽  
Frequency Band ◽  
Vacuum Chamber ◽  
Fe Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Membrane Type ◽  
Multiobjective Design

The paper presents some results of multi-objective optimization for the multilayered membrane-type piezoceramic MEMS based transducers with perforated active PZT and intermediate diaphragms, covered by the protective plates, and a vacuum chamber. An influence of the protective plate elastic and viscous properties, the dimensions and the relative areas of the perforated holes on the sensitivity’s frequency response of the hydrophone was studied for the broadening and equalizes the operating frequency band. We optimize the key design’s parameters using the Pareto approach with the finite element (FE) model of coupled piezoelectric-acoustic problem for the hydrophone.

Multiobjective Pareto-Based Optimization of pMUT Hydrophone With Piezoelectric Active Diaphragm

2014 ◽  
Author(s):  
Sergey Shevtsov ◽  
Shun-Hsyung (Stephen) Chang ◽  
Valery Kalinchuk ◽  
Igor Zhilyaev ◽  
Maria Shevtsova
Keyword(s):  
Frequency Response ◽  
Vacuum Chamber ◽  
Ultrasonic Transducers ◽  
Fe Model ◽  
Spatial Averaging ◽  
New Approach ◽  
Ultrasound Field ◽  
Sound Detection ◽  
Membrane Type ◽  
Micro Electromechanical System

The design of high-sensitive hydrophones is one of the research interests in underwater acoustics. Due to progress of micro- and nanotechnology the most attention of researchers is attracted by the transducers that use the micro-electromechanical system (MEMS) concept. Piezoelectric micro-machined ultrasonic transducers (pMUTs) present a new approach to sound detection and generation that can overcome the shortcomings of conventional transducers. For accurate ultrasound field measurement, small size hydrophones which are smaller than the acoustic wavelength are required for providing an omnidirectional response and avoid spatial averaging. This paper presents some results of multiobjective optimization for membrane-type piezoceramic MEMS based transducers. We investigate the miniaturized membrane-type sensor with perforated holes in the active PZT and intermediate membranes, with the protective plates and a vacuum chamber. An influence of the protective plate elastic and viscous properties, the dimensions and the relative area of the perforated holes on the sensitivity’s frequency response of the hydrophone was studied for the broadening and equalizes the operating frequency band. We optimize these key parameters using the Pareto approach with the finite element (FE) model of coupled piezoelectric-acoustic problem. Finally, the set of optimized hydrophone structures and some examples of obtained sensitivity frequency response are demonstrated.

The Efficient Multi-Objective Optimization of Finite Element Analysis Model Using ModelCenter

2016 ◽  
Author(s):  
Lyu Wang ◽  
Yuan Yun ◽  
Bin Zhang ◽  
Tao Zhang
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Frequency Response ◽  
Space Environment ◽  
Multi Objective Optimization ◽  
Analysis Model ◽  
Element Analysis ◽  
Finite Element Analysis Model ◽  
Multi Objective ◽  
Software Analysis

The multi-objective optimization for a nested flying vehicle (NFV) of space science experiments is carried out aiming at the launch weight, frequency response and vacuum effect. The parametric model and finite element analysis are adopted to implement the structural analysis. The NFV is optimized to enhance the performance in the space environment where the lunch weight and structural strength are key constraints to concern about. The CAX software, analysis models and algorithms are integrated based on ModelCenter framework which makes modeling, analyzing and optimization more convenient and efficient. The optimizer of ModelCenter is chosen to optimize the structural performance of NFV, including the total mass, maximum deformation caused by vacuum environment and frequency response. As to validate the results, both weighting method with gradient optimization algorithm and Genetic Algorithm (GA) for multi-objective optimization are used. The optimization results of NFV verify the approaches proposed in this paper can improve the performance of NFV and apply to the finite element analysis model.

The Multi-Objective Optimization of the Planet Carrier in Wind Turbine Gearbox

2012 ◽  
Vol 184-185 ◽  
pp. 565-569 ◽  
Author(s):  
Peng Xing Yi ◽  
Li Jian Dong ◽  
Yuan Xin Chen
Keyword(s):  
Finite Element ◽  
Design Optimization ◽  
Response Surface ◽  
Minimum Principle ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Wind Turbine Gearbox ◽  
Element Analysis ◽  
Multi Objective ◽  
Planet Carrier

In order to improve the reliability of a planet carrier, a simulation method based on multi-objective design optimization was developed in this paper. The objective of the method was to reduce the stress concentration, the deformation, and the quality of the planet carrier by optimizing the structure dimension. A parametric finite element model, which enables a good understanding of how the parameters affect the reliability of planet carrier, was established and simulated by ANSYS-WORKBENCH. The efficiency of the design optimization was improved by using a polynomials response surface to approximate the results of finite element analysis and a screening algorithm to determine the direction of optimization. Furthermore, the multi-objective optimization was capable of finding the global minimum results in the use of the minimum principle on the response surface. Computer simulation was carried out to verify the validity of the presented optimization method, by which the quality and the stability of the planet carrier were significantly reduced and improved, respectively. The methodology described in this paper can be effectively used to improve the reliability of planet carrier.

On the Entropy of Multi-Objective Design Optimization Solution Sets

2002 ◽  
Author(s):  
Ali Farhang-Mehr ◽  
Shapour Azarm
Keyword(s):  
Design Optimization ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Solution Sets ◽  
Multi Objective ◽  
Speed Reducer ◽  
Quantitative Basis ◽  
Multiobjective Design

In this paper, an entropy-based metric is presented for quality assessment of non-dominated solution sets obtained from a multiobjective optimization technique. This metric quantifies the ‘goodness’ of a solution set in terms of its distribution quality over the Pareto-optimal frontier. Therefore, it can be useful in comparison studies of different multi-objective optimization techniques, such as Multi-Objective Genetic Algorithms (MOGAs), wherein the capabilities of such techniques to produce and maintain diversity among different solution points are desired to be compared on a quantitative basis. An engineering test example, the multiobjective design optimization of a speed-reducer, is presented in order to demonstrate an application of the proposed entropy metric.

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 Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Multi-Objective Optimization Model Development to Support Sizing Decisions for a Novel Reciprocating Steam Engine Technology

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
J. M. Hamel ◽  
Devin Allphin ◽  
Joshua Elroy
Keyword(s):  
Computational Model ◽  
Design Optimization ◽  
Kinematic Model ◽  
Operating Conditions ◽  
Steam Engine ◽  
System Model ◽  
System Level ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Crank Angle

A system-level computational model of a recently patented and prototyped novel steam engine technology was developed from first principles for the express purpose of performing design optimization studies for the engine's inventors. The developed system model consists of numerous submodels including a flow model of the intake process, a dynamic model of the intake valve response, a pressure model of the engine cylinder, a kinematic model of the engine piston, and an output model that determines engine performance parameters. A crank-angle discretization strategy was employed to capture the performance of engine throughout a full cycle of operation, thus requiring all engine design submodels to be evaluated at each crank angle of interest. To produce a system model with sufficient computational speed to be useful within optimization algorithms, which must exercise the system level model repeatedly, various simplifying assumptions and modeling approximations were utilized. The model was tested by performing a series of multi-objective design optimization case studies using the geometry and operating conditions of the prototype engine as a baseline. The results produced were determined to properly capture the fundamental behavior of the engine as observed in the operation of the prototype and demonstrated that the design of engine technology could be improved over the baseline using the developed computational model. Furthermore, the results of this study demonstrate the applicability of using a multi-objective optimization-driven approach to conduct conceptual design efforts for various engine system technologies.

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.

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