scholarly journals Mathematical Strategies for Design Optimization of Multiphase Materials

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Rick Catania ◽  
Abdalla Diraz ◽  
Dominic Maier ◽  
Armani Tagle ◽  
Pınar Acar
Keyword(s):  
Design Optimization ◽  
Phase Distribution ◽  
Control Point ◽  
Structural Performance ◽  
Computational Time ◽  
Critical Set ◽  
Multiphase Materials ◽  
Analysis Scheme ◽  
Fiber Path ◽  
Spatially Varying

This work addresses various mathematical solution strategies adapted for design optimization of multiphase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multiphase materials and composites with spatially varying fiber paths using a finite element analysis scheme. In the first application, the phase distribution of a two-phase material is optimized to improve the structural performance. A radial basis function (RBF) based machine learning algorithm is utilized to perform a computationally efficient design optimization and it is found to provide equivalent results with the physical model. The second application concentrates on the optimization of spatially varying fiber paths of a composite material. The fiber paths are described by the Non-Uniform Rational Bezier (B)-Spline Surface (NURBS) using a bidirectional control point representation including 25 parameters. The optimum fiber path is obtained for various loading configurations by optimizing the NURBS parameters that control the overall distribution of fibers. Next, a direct sensitivity analysis is conducted to choose the critical set of parameters from the design point to improve the computational time efficiency. The optimized fiber path obtained with the reduced number of NURBS parameters is found to provide similar structural properties compared to the optimized fiber path that is modeled with a full NURBS representation with 25 parameters.

Taguchi-EM-AI Design Optimization Environment for SynRM Drives in Traction Applications

2021 ◽  
Vol 35 (11) ◽  
pp. 1372-1373
Author(s):  
A.A. Arkadan ◽  
N. Al Aawar
Keyword(s):  
Design Optimization ◽  
Search Algorithm ◽  
Motor Drives ◽  
Computational Time ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Motor Drive System ◽  
Taguchi Orthogonal Arrays ◽  
Optimization Search

Multi-objective design optimization environments are used for electric vehicles and other traction applications to arrive at efficient motor drives. Typically, the environment includes characterization modules that involve the use of Electromagnetic Finite Element and State-Space models that require large number of iterations and computational time. This work proposes the utilization of a Taguchi orthogonal arrays method in conjunction with a Particle Swarm Optimization search algorithm to reduce computational time needed in the design optimization of electric motors for traction applications. The effectiveness of the Taguchi method in conjunction with the optimization environment is demonstrated in a case study involving a prototype of a Synchronous Reluctance Motor drive system.

Efficient Robust Design Optimization of a Stacker–Reclaimer Structure Under Uncertainty

2019 ◽  
Vol 26 (02) ◽  
pp. 1950009
Author(s):  
Soumya Bhattacharjya ◽  
Mithun Sarkar ◽  
Gaurav Datta ◽  
Saibal Kumar Ghosh
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Least Squares Method ◽  
Bulk Material ◽  
Robust Design Optimization ◽  
Computational Time ◽  
Computationally Efficient ◽  
Massive Structure ◽  
Accurate Design ◽  
Design Solutions

A stacker–reclaimer structure (SRS) is a massive structure used for bulk material exploration. Performance of SRS is sensitive to the effect of uncertainty which may lead to catastrophic failure consequences. Thus, in this paper a comparatively new robust design optimization (RDO) approach for design of SRS is explored. The involved parameter of SRS e.g., material loading, incrustation, normal digging, etc., may not have well-defined probability density functions and can be expressed as uncertain but bounded (UBB) type parameters. Hence, RDO is explored for probabilistic as well as UBB cases. Solution of such RDO problem in full simulation approach would require extensive computational time. Hence, response surface method (RSM) based metamodeling approach has been adopted here to alleviate computational burden. Also, as conventional least squares method (LSM) based RSM may be a source of error, this study adopts a comparatively new moving LSM (MLSM) based adaptive RSM in RDO. The RDO results depict that UBB type uncertainty is more critical than the probabilistic case. The proposed MLSM based RDO approach yields reasonably accurate design solutions in a computationally efficient way. The proposed MLSM based RDO approach yields design solutions which ensures safe performance of SRS even in the presence of uncertainty.

scholarly journals Experimental Validation of a New Modeling for the Design Optimization of a Sliding Vane Rotary Expander Operating in an ORC-Based Power Unit

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4204 ◽  
Author(s):  
Fabio Fatigati ◽  
Marco Di Bartolomeo ◽  
Davide Di Battista ◽  
Roberto Cipollone
Keyword(s):  
Design Optimization ◽  
Power Unit ◽  
Waste Heat ◽  
Combustion Engine ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Computational Time ◽  
Manufacturing Cost ◽  
External Diameter ◽  
New Model

Sliding Rotary Vane Expanders (SVRE) are often employed in Organic Rankine Cycle (ORC)-based power units for Waste Heat Recovery (WHR) in Internal Combustion Engine (ICE) due to their operating flexibility, robustness, and low manufacturing cost. In spite of the interest toward these promising machines, in literature, there is a lack of knowledge referable to the design and the optimization of SVRE: these machines are often rearranged reversing the operational behavior when they operate as compressors, resulting in low efficiencies and difficulty to manage off-design conditions, which are typical in ORC-based power units for WHR in ICE. In this paper, the authors presented a new model of the machine, which, thanks to some specific simplifications, can be used recursively to optimize the design. The model was characterized by a good level of physical representation and also by an acceptable computational time. Despite its simplicity, the model integrated a good capability to reproduce volumetric and mechanical efficiencies. The validation of the model was done using a wide experimental campaign conducted on a 1.5 kW SVRE operated on an ORC-based power unit fed by the exhaust gases of a 3 L supercharged diesel engine. Once validated, a design optimization was run, allowing to find the best solution between two “extreme” designs: a “disk-shaped”—increasing the external diameter of the machine and reducing axial length—and by a “finger-shaped” machine. The predictions of this new model were finally compared with a more complex numerical model, showing good agreement and opening the way to its use as a model-based control tool.

A Simultaneous Computing Framework for Metamodel-Based Design Optimization

2014 ◽  
Author(s):  
Teng Long ◽  
Lv Wang ◽  
Di Wu ◽  
Xiaosong Guo ◽  
Li Liu
Keyword(s):  
Design Optimization ◽  
Data Exchange ◽  
Optimization Problems ◽  
Configuration Software ◽  
Computational Time ◽  
Benchmark Problems ◽  
Distributed Data ◽  
Legacy Software ◽  
Distributed Framework ◽  
Hardware Configuration

At the aim of reducing the computational time of engineering design optimization problems using metamodeling technologies, we developed a flexible distributed framework independent of any third-part parallel computing software to implement simultaneous sampling during metamodel-based design optimization procedures. In this paper, the idea and implementation of hardware configuration, software structure, the main functional modular and interfaces of this framework are represented in detail. The proposed framework is capable of integrating black-box functions and legacy software for analyzing and common MBDO methods for space exploring. In addition, a message-based communication infrastructure based on TCP/IP protocol is developed for distributed data exchange. The Client/Server architecture and computing budget allocation algorithm considering software dependency enable samples to be effectively allocated to the distributed computing nodes for simultaneous execution, which gives rise to decreasing the elapsed time and improving MBDO’s efficiency. Through testing on several numerical benchmark problems, the favorable results demonstrate that the proposal framework can evidently save the computational time, and is practical for engineering MBDO problems.

The Influence of Metamodeling Techniques on the Multidisciplinary Design Optimization of a Radial Compressor Impeller

2012 ◽  
Author(s):  
Christopher Chahine ◽  
Joerg R. Seume ◽  
Tom Verstraete
Keyword(s):  
Fluid Dynamics ◽  
Design Optimization ◽  
Design Space ◽  
Optimization Algorithms ◽  
Computational Cost ◽  
Computational Time ◽  
Radial Compressor ◽  
Compressor Impeller ◽  
The Impact ◽  
Low Computational Cost

Aerodynamic turbomachinery component design is a very complex task. Although modern CFD solvers allow for a detailed investigation of the flow, the interaction of design changes and the three dimensional flow field are highly complex and difficult to understand. Thus, very often a trial and error approach is applied and a design heavily relies on the experience of the designer and empirical correlations. Moreover, the simultaneous satisfaction of aerodynamic and mechanical requirements leads very often to tedious iterations between the different disciplines. Modern optimization algorithms can support the designer in finding high performing designs. However, many optimization methods require performance evaluations of a large number of different geometries. In the context of turbomachinery design, this often involves computationally expensive Computational Fluid Dynamics and Computational Structural Mechanics calculations. Thus, in order to reduce the total computational time, optimization algorithms are often coupled with approximation techniques often referred to as metamodels in the literature. Metamodels approximate the performance of a design at a very low computational cost and thus allow a time efficient automatic optimization. However, from the experiences gained in past optimizations it can be deduced that metamodel predictions are often not reliable and can even result in designs which are violating the imposed constraints. In the present work, the impact of the inaccuracy of a metamodel on the design optimization of a radial compressor impeller is investigated and it is shown if an optimization without the usage of a metamodel delivers better results. A multidisciplinary, multiobjective optimization system based on a Differential Evolution algorithm is applied which was developed at the von Karman Institute for Fluid Dynamics. The results show that the metamodel can be used efficiently to explore the design space at a low computational cost and to guide the search towards a global optimum. However, better performing designs can be found when excluding the metamodel from the optimization. Though, completely avoiding the metamodel results in a very high computational cost. Based on the obtained results in present work, a method is proposed which combines the advantages of both approaches, by first using the metamodel as a rapid exploration tool and then switching to the accurate optimization without metamodel for further exploitation of the design space.

Fluid Dynamic Design Optimization of the Intake of a Small Turbojet

2006 ◽  
Author(s):  
Riccardo Amirante ◽  
Luciano A. Catalano ◽  
Andrea Dadone ◽  
Vito S. E. Daloiso ◽  
Dario Manodoro
Keyword(s):  
Design Optimization ◽  
Electricity Generation ◽  
Fluid Dynamic ◽  
Optimization Procedure ◽  
Black Box ◽  
High Load ◽  
Computational Time ◽  
Design Criteria ◽  
Dynamic Design ◽  
Gradient Based

This paper proposes an efficient gradient-based optimization procedure for black-box simulation codes and its application to the fluid-dynamic design optimization of the intake of a small-size turbojet, at high load and zero flight speed. Two simplified design criteria have been considered, which avoid to simulate the flow in any turbojet components other than the intake itself. Both design optimizations have been completed in a computational time corresponding to that required by eight flow analyses and have provided almost coincident optimal profiles for the intake. The flow fields computed with the original and the optimal profiles are compared to demonstrate the flow pattern improvements that can be theoretically achieved. Finally, the original and the optimal profiles have been mounted on the same small-size turbojet and experimentally tested, to assess the resulting improvements in terms of overall performances. All numerical and experimental results can be obviously extended to the intake of a microturbine for electricity generation.

Reliability-Based Design Optimization Using Response Surface Method With Prediction Interval Estimation

2008 ◽  
Vol 130 (12) ◽  
Author(s):  
Chwail Kim ◽  
K. K. Choi
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Prediction Interval ◽  
Upper And Lower Bounds ◽  
Computational Time ◽  
Engineering System ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Surface Method

Since variances in the input variables of the engineering system cause subsequent variances in the product output performance, reliability-based design optimization (RBDO) is getting much attention recently. However, RBDO requires expensive computational time. Therefore, the response surface method is often used for computational efficiency in solving RBDO problems. A method to estimate the effect of the response surface error on the RBDO result is developed in this paper. The effect of the error is expressed in terms of the prediction interval, which is utilized as the error metric for the response surface used for RBDO. The prediction interval provides upper and lower bounds for the confidence level that the design engineer specified. Using the prediction interval of the response surface, the upper and lower limits of the reliability are computed. The lower limit of reliability is compared with the target reliability to obtain a conservative optimum design and thus safeguard against the inaccuracy of the response surface. On the other hand, in order to avoid obtaining a design that is too conservative, the developed method also constrains the upper limit of the reliability in the design optimization process. The proposed procedure is combined with an adaptive sampling strategy to refine the response surface. Numerical examples show the usefulness and the efficiency of the proposed method.

scholarly journals Experimental Study and Mixed-Dimensional FE Analysis of T-Rib GFRP Plate-Concrete Composite Bridge Decks

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jun Tian ◽  
Xiaowei Wu ◽  
Yu Zheng ◽  
Yinfei Du ◽  
Xiankai Quan
Keyword(s):  
Bearing Capacity ◽  
Bridge Decks ◽  
Concrete Structures ◽  
Ultimate Bearing Capacity ◽  
Fe Analysis ◽  
Interface Roughness ◽  
Structural Performance ◽  
Computational Time ◽  
Analysis Model ◽  
Composite Bridge

In order to extend the understanding of structural performance of a T-rib glass fibre-reinforced polymer (GFRP) plate-concrete composite bridge deck, four GFRP plate-concrete composite bridge decks were tested, which consist of cast-in-place concrete sitting on a GFRP plate with T-ribs. Subsequently, a mixed-dimensional finite element (FE) analysis model was proposed to simulate the behavior of the test models. The test and simulation results showed that the composite specimens had an excellent interface bonding performance between GFRP plate and concrete throughout flexural response until specimens failure occurred. The failure mode of those composite specimens was shear failure in concrete structures. It was found that the interface roughness of the GFRP plate could not affect the ultimate bearing capacity and stiffness of composite specimens significantly. However, the height of concrete structures had a strong effect on those structural behaviors. In addition, the longitudinal compressive reinforcing CFRP rebars had a little influence on ultimate bearing capacity of composite specimens, while it had a significant influence on ductility of composite specimens. The mixed-dimensional FE analysis model can accurately simulate the local complex stress state of GFRP plates, ultimate loads, stiffness, and midspan deflections and simultaneously can significantly reduce computational time. Therefore, mixed-dimensional FE analysis can provide a suitable solution to simulate the structural performance of T-rib GFRP plate-concrete composite bridge decks.

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