A Numerical Study of Crash Optimization

1999 ◽  
Author(s):  
R. J. Yang ◽  
C. H. Tho ◽  
C. C. Wu ◽  
D. Johnson ◽  
J. Cheng
Keyword(s):  
Software Package ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Numerical Study ◽  
Surface Method ◽  
Gradient Based ◽  
Nonlinear Transient ◽  
Commercial Software Package ◽  
Crash Model ◽  
Single Objective

Abstract In this paper, numerical crash optimization was performed and studied. Three design optimization approaches: gradient-based, DOE/Penalty/Gradient-based, and DOE/Penalty/Response Surface Method, were employed to solve a front rail crash optimization problem using two single-objective formulations. An in-house explicit code FCRASH was used for solving the nonlinear, transient crash problem. The optimization process was carried out using a commercial software package, iSIGHT from Engineous Software Inc. A front rail crash model was used as the benchmark to demonstrate those approaches. Based on the numerical experiments, a simple, viable, and relatively efficient approach is proposed.

Composite right/left-handed transmission line with array of thermocouples for generating terahertz radiation

2020 ◽  
Vol 92 (2) ◽  
pp. 20502
Author(s):  
Behrokh Beiranvand ◽  
Alexander S. Sobolev ◽  
Anton V. Kudryashov
Keyword(s):  
Transmission Line ◽  
Terahertz Radiation ◽  
Transmission Lines ◽  
Software Package ◽  
Optical Pulses ◽  
Capacitively Coupled ◽  
Voltage Difference ◽  
Left Handed ◽  
Microwave Transmission ◽  
Commercial Software Package

We present a new concept of the thermoelectric structure that generates microwave and terahertz signals when illuminated by femtosecond optical pulses. The structure consists of a series array of capacitively coupled thermocouples. The array acts as a hybrid type microwave transmission line with anomalous dispersion and phase velocity higher than the velocity of light. This allows for adding up the responces from all the thermocouples in phase. The array is easily integrable with microstrip transmission lines. Dispersion curves obtained from both the lumped network scheme and numerical simulations are presented. The connection of the thermocouples is a composite right/left-handed transmission line, which can receive terahertz radiation from the transmission line ports. The radiation of the photon to the surface of the thermocouple structure causes a voltage difference with the bandwidth of terahertz. We examined a lossy composite right/left-handed transmission line to extract the circuit elements. The calculated properties of the design are extracted by employing commercial software package CST STUDIO SUITE.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

scholarly journals Nonconvex constrained optimization by a filtering branch and bound

2020 ◽  
Author(s):  
Gabriele Eichfelder ◽  
Kathrin Klamroth ◽  
Julia Niebling
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Constrained Optimization Problem ◽  
Numerical Tests ◽  
Nonconvex Constrained Optimization ◽  
Objective Value ◽  
Feasible Solutions ◽  
Quality Guarantee ◽  
Single Objective

AbstractA major difficulty in optimization with nonconvex constraints is to find feasible solutions. As simple examples show, the $$\alpha $$ α BB-algorithm for single-objective optimization may fail to compute feasible solutions even though this algorithm is a popular method in global optimization. In this work, we introduce a filtering approach motivated by a multiobjective reformulation of the constrained optimization problem. Moreover, the multiobjective reformulation enables to identify the trade-off between constraint satisfaction and objective value which is also reflected in the quality guarantee. Numerical tests validate that we indeed can find feasible and often optimal solutions where the classical single-objective $$\alpha $$ α BB method fails, i.e., it terminates without ever finding a feasible solution.

EVOLUTIONARY MULTI-OBJECTIVE OPTIMISATION OF NEURAL NETWORKS FOR FACE DETECTION

2004 ◽  
Vol 04 (03) ◽  
pp. 237-253 ◽  
Author(s):  
STEFAN WIEGAND ◽  
CHRISTIAN IGEL ◽  
UWE HANDMANN
Keyword(s):  
Search Strategy ◽  
Human Face ◽  
Video Streams ◽  
Feed Forward Neural Network ◽  
Image Region ◽  
Multi Objective ◽  
Strategy Adaptation ◽  
Gradient Based ◽  
Speed And Accuracy ◽  
Single Objective

For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.

scholarly journals Cloud Brokering with Bundles: Multi-objective Optimization of Services Selection

2019 ◽  
Vol 44 (4) ◽  
pp. 407-426
Author(s):  
Jedrzej Musial ◽  
Emmanuel Kieffer ◽  
Mateusz Guzek ◽  
Gregoire Danoy ◽  
Shyam S. Wagle ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Cloud Service ◽  
Optimal Choice ◽  
Cloud Services ◽  
Multi Objective Optimization ◽  
Problem Instances ◽  
The Cost ◽  
Special Case ◽  
Single Objective

Abstract Cloud computing has become one of the major computing paradigms. Not only the number of offered cloud services has grown exponentially but also many different providers compete and propose very similar services. This situation should eventually be beneficial for the customers, but considering that these services slightly differ functionally and non-functionally -wise (e.g., performance, reliability, security), consumers may be confused and unable to make an optimal choice. The emergence of cloud service brokers addresses these issues. A broker gathers information about services from providers and about the needs and requirements of the customers, with the final goal of finding the best match. In this paper, we formalize and study a novel problem that arises in the area of cloud brokering. In its simplest form, brokering is a trivial assignment problem, but in more complex and realistic cases this does not longer hold. The novelty of the presented problem lies in considering services which can be sold in bundles. Bundling is a common business practice, in which a set of services is sold together for the lower price than the sum of services’ prices that are included in it. This work introduces a multi-criteria optimization problem which could help customers to determine the best IT solutions according to several criteria. The Cloud Brokering with Bundles (CBB) models the different IT packages (or bundles) found on the market while minimizing (maximizing) different criteria. A proof of complexity is given for the single-objective case and experiments have been conducted with a special case of two criteria: the first one being the cost and the second is artificially generated. We also designed and developed a benchmark generator, which is based on real data gathered from 19 cloud providers. The problem is solved using an exact optimizer relying on a dichotomic search method. The results show that the dichotomic search can be successfully applied for small instances corresponding to typical cloud-brokering use cases and returns results in terms of seconds. For larger problem instances, solving times are not prohibitive, and solutions could be obtained for large, corporate clients in terms of minutes.

scholarly journals Modelling of hydrodynamic cavitation for treatment of wastewater in a Venturi tube

2020 ◽  
Vol 14 (1) ◽  
pp. 61-66
Author(s):  
Betti Bolló
Keyword(s):  
Software Package ◽  
Venturi Tube ◽  
Hydrodynamic Cavitation ◽  
Ansys Fluent ◽  
High Effectiveness ◽  
Commercial Software Package ◽  
Promising Application ◽  
Low Costs ◽  
Modern Technologies ◽  
Degradation Of Pollutants

Treatment of municipal effluents has long been a challenge for modern technologies combining high effectiveness of degradation of pollutants with low costs of the process. Hydrodynamic cavitation is a promising application in wastewater treatment due to its simple reactor design. In this work, for a system available in the laboratory a hydrodynamic reactor is designed based on literature recommendations. On the designed Venturi tube, two-dimensional numerical simulations were investigated by the means of CFD computations using the commercial software package, Ansys Fluent. The resulting cavitation bubbles were analysed at different inlet pressures.

scholarly journals Multiobjective optimal design of reinforced concrete frames using two metaheuristic algorithms

2021 ◽  
Vol 9 (4B) ◽  
Author(s):  
Mehdi Babaei ◽  
◽  
Masoud Mollayi ◽  
Keyword(s):  
Reinforced Concrete ◽  
Optimal Design ◽  
Optimization Problem ◽  
Numerical Models ◽  
Steel Structures ◽  
Metaheuristic Algorithms ◽  
Rc Structures ◽  
Weighted Sum Method ◽  
Rc Frames ◽  
Single Objective

Genetic algorithm (GA) and differential evolution (DE) are metaheuristic algorithms that have shown a favorable performance in the optimization of complex problems. In recent years, only GA has been widely used for single-objective optimal design of reinforced concrete (RC) structures; however, it has been applied for multiobjective optimization of steel structures. In this article, the total structural cost and the roof displacement are considered as objective functions for the optimal design of the RC frames. Using the weighted sum method (WSM) approach, the two-objective optimization problem is converted to a single-objective optimization problem. The size of the beams and columns are considered as design variables, and the design requirements of the ACI-318 are employed as constraints. Five numerical models are studied to test the efficiency of the GA and DE algorithms. Pareto front curves are obtained for the building models using both algorithms. The detailed results show the accuracy and convergence speed of the algorithms.

scholarly journals STEADY-STATE MODELLING OF PEM FUEL CELLS USING GRADIENT-BASED OPTIMIZER

10.6036/10099 ◽  
2021 ◽  
Vol DYNA-ACELERADO (0) ◽  
pp. [ 8 pp.]-[ 8 pp.]
Author(s):  
SALAH KAMAL ◽  
ATTIA EL-FERGANY ◽  
EHAB EHAB ELSAYED ELATTAR ◽  
AHMED AGWA
Keyword(s):  
Fuel Cells ◽  
Steady State ◽  
Fuel Cell ◽  
Optimization Problem ◽  
Pem Fuel Cells ◽  
Test Cases ◽  
Uncertain Parameters ◽  
Gradient Based ◽  
High Degree ◽  
Exchange Membrane

The accuracy of fuel cell (FC) models is important for the further numerical simulations and analysis at several conditions. The electrical (I-V) characteristic of the polymer exchange membrane fuel cells (PEMFCs) has high degree of nonlinearity comprising uncertain seven parameters as they aren’t given in fabricator's datasheets. These seven parameters need to be obtained to have the PEMFC model in order. This research addresses an up-to-date application of the gradient-based optimizer (GBO) to generate the best estimated values of such uncertain parameters. The estimation of these uncertain parameters is adapted as optimization problem having a cost function (CF) subjects to set of self-constrained limits. Three test cases of widely used PEMFCs units; namely, SR-12, 250-W module and NedStack PS6 to appraise the performance of the GBO are demonstrated and analyzed. The best values of the CF are 0.000142, 0.33598, and 2.10025 V2 for SR-12, 250-W module and NedStack PS6; respectively. Furthermore, the assessment of the GBO-based model is made by comparing its obtained results with the experiential results of these typical PEMFCs plus comparisons to other methods. At a due stage, many scenarios as a result of operating variations in regard to inlet regulation pressures and unit temperatures are performed. The copped reported results of the studied scenarios indicate the effectiveness of the GBO in establishing an accurate PEMFC model.

scholarly journals The optimal convergence factor of the gradient based iterative algorithm for linear matrix equations

Filomat ◽  
2012 ◽  
Vol 26 (3) ◽  
pp. 607-613 ◽  
Author(s):  
Xiang Wang ◽  
Dan Liao
Keyword(s):  
Iterative Algorithm ◽  
Numerical Experiments ◽  
Linear Matrix ◽  
Matrix Equations ◽  
Convergence Factor ◽  
Gradient Based ◽  
Gradient Type ◽  
And Mathematics ◽  
Computers And Mathematics ◽  
Linear Matrix Equations

A hierarchical gradient based iterative algorithm of [L. Xie et al., Computers and Mathematics with Applications 58 (2009) 1441-1448] has been presented for finding the numerical solution for general linear matrix equations, and the convergent factor has been discussed by numerical experiments. However, they pointed out that how to choose a best convergence factor is still a project to be studied. In this paper, we discussed the optimal convergent factor for the gradient based iterative algorithm and obtained the optimal convergent factor. Moreover, the theoretical results of this paper can be extended to other methods of gradient-type based. Results of numerical experiments are consistent with the theoretical findings.

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