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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Fazeli, Hamid Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9780791885673

The use of nanofluids as coolant fluid in the microchannel heat sink (MCHS) is an effective technique for the improvement of thermal performance in electronic devices. A comparative study is performed with the help of a multi-objective genetic algorithm (MOGA) to find the optimal geometric variables of the MCHS and choose the appropriate nanofluid and its optimal characteristics. For that purpose, four practical nanofluids, including Al2O3-water, Cu-water, SiO2-water, and carbon nanotube (CNT) -water, are thoroughly investigated. Simultaneously minimization of the total thermal resistance and pumping power consumption are taken into account as the optimization goal, and the MOGA is employed to achieve the optimal solution. To check the accuracy of the thermal resistance modeling and assessing the optimization algorithm, several case studies with a different number of optimization variables are defined to investigate the capability of the algorithm in finding the optimal microchannel design variables and choosing the suitable nanofluid. The optimization variables consist of the channel aspect and wall ratios, base thickness, volume nanoparticle concentration, diameter, and volume flow rate. Compared to other nanofluids, CNT provides better thermal performance. Increasing the concentration of nanoparticles enhances thermal performance, which can also be achieved through the reduction of nanoparticle diameter. Results of the considered case studies also show that considering more design variables through the optimization procedure, better thermal performance is achievable.

关键词： optimization Genetic algorithms Heat sinks Nanofluids Microchannels Water Carbon nanotubes Nanoparticles Design Thermal resistance optimization algorithms Modeling Energy consumption algorithms Flow (Dynamics) Fluids Coolants

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optimization of design parameters and 3D-printing orientation to enhance the efficiency of topology-optimized components in additive manufacturing

Results in Materials

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Results in Materials 2025年 26卷

作者： Efa, Dame Alemayehu Ifa, Dejene Alemayehu School of Mechanical Engineering Institute of Technology Wallaga University P.O. Box 395 Nekemte Ethiopia

Computer-aided design (CAD) is revolutionizing 3D object production in Additive Manufacturing (AM), especially enhancing the creation of complex optimized structures. However, due to the difficulties of testing the thickness or size of structural components using physical testing in topology-optimized parts, optimizing parts for high efficiency remains a significant challenge. In addition to optimizing 3D printing orientation, this study uses Response Surface Methodology (RSM) and optimization algorithms to optimize design parameters such as safety factor and thickness for predicting stress and deformation in topology optimization. The results show that RSM, Artificial Neural Network (ANN) and observed simulation results strongly correlate, confirming their reliability for determining the most optimal design parameters for topology optimization. Maximum displacement and Von-Mises stress at 0.101 mm and 29.1 MPa were found to be the optimal responses. In contrast, the optimum input parameters include a minimum safety factor of 1.2, a minimum thickness of 3.85 mm, and a maximum thickness of 8.42 mm, which are identified for optimal topology optimization using Genetic algorithms (GA). These results were verified using optimization software, confirming the effectiveness of the study's methodology. Vertical orientation is the optimal printing orientation to develop parts with greater hardness, according to X-ray Diffraction (XRD), hardness, wear resistance, and morphological tests. Testing confirmed that the topology-optimized result, which utilized the most optimal process condition and orientation, effectively produced a stronger 3D-printed part. Therefore, this method saves material and the time waste usually caused by trial and error by predicting the optimal 3D printing orientation and design parameters in providing more effective and intended results. © 2025 The Authors

关键词： Additive manufacturing optimization algorithms Parametric optimization Printing orientation Topology optimization

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GRADIENT-FREE optimization IN THERMOACOUSTICS: APPLICATION TO A LOW-ORDER MODEL

GRADIENT-FREE OPTIMIZATION IN THERMOACOUSTICS: APPLICATION T...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Reumschuessel, Johann Moritz von Saldern, Jakob G. R. Li, Yiqing Paschereit, Christian Oliver Orchini, Alessandro Tech Univ Berlin Chair Fluid Dynam Berlin Germany Tech Univ Berlin Lab Flow Instabil & Dynam Berlin Germany Harbin Inst Technol Ctr Turbulence Control Shenzhen Guangdong Peoples R China

ISBN: (纸本)9780791884942

Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimization can avoid cumbersome theoretical studies as input parameters are purely adapted based on output data. As no knowledge about the objective function is provided to the algorithms, this approach might reveal unconventional solutions to complex problems that were out of scope of classical solution strategies. In this study, the potential of these optimization methods on thermoacoustic problems is examined. The optimization algorithms are applied to a generic low-order thermoacoustic can-combustor model with several fuel injectors at different locations. We use three optimization algorithms the well established Downhill Simplex Method, the recently proposed Explorative Gradient Method, and an evolutionary algorithm - to find optimal fuel distributions across the fuel lines while maintaining the amount of consumed fuel constant. The objective is to have minimal pulsation amplitudes. We compare the results and efficiency of the gradient-free algorithms. Additionally, we employ model-based linear stability analysis to calculate the growth rates of the dominant thermoacoustic modes. This allows us to highlight general and thermoacoustic-specific features of the optimization methods and results. The findings of this study show the potential of gradient-free optimization methods on combustor design for tackling thermoacoustic problems, and motivate further research in this direction.

关键词： optimization Thermoacoustics Fuels Combustion chambers algorithms optimization algorithms Surges Evolutionary algorithms Gradient methods Design Fuel injectors Machine learning Stability

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Distributed Network optimization for Secure Operation of Interdependent Complex Networks

Distributed Network Optimization for Secure Operation of Int...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Amini, M. Hadi Njilla, Laurent L. Imteaj, Ahmed Mark, Calvin Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Inter Dependent Networks Lab Solid Lab Sustainabil Optimizat & Learning Miami FL USA Air Force Res Lab Boston MA USA

ISBN: (纸本)9781665458412

The optimal operation of complex networks and critical infrastructures requires solving various large-scale decision-making problems. These problems usually are formulated as optimization problems with several variables and constraints. This leads to the high computational complexity of solving the underlying optimization problem. Hence, we require efficient methods to first model the operational objective function and constraints of the complex networks, and how they can leverage available computational resources to achieve the optimal operation of the entire system. We further need to ensure data security of decision-making entities, e.g., network flow problems, and their impact on the secure operation of the system. The proposed framework and algorithms in this paper include distributed intelligence among heterogeneous agents in a complex network represented by a graph of nodes and edges among them. Our utilized methods act as efficient computational algorithms to solve the underlying optimization problems of these networks in a computationally-efficient fashion. In order to evaluate the introduced distributed algorithm for linear-constrained optimization with a quadratic cost function, we used a random network with different numbers of nodes and edges. We illustrate the run-time and convergence of the distributed method over various networks.

关键词： Complex networks heterogeneous agents distributed intelligence optimization algorithms computation time

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optimization OF HYDRODYNAMIC COEFFICIENTS FOR VIV PREDICTION 40

OPTIMIZATION OF HYDRODYNAMIC COEFFICIENTS FOR VIV PREDICTION

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40th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Yin, Decao Wu, Jie Lie, Halvor Jin, Jingzhe Passano, Elizabeth Saevik, Svein Riemer-Sorensen, Signe Rustad, Anne Marthine Tognarelli, Michael A. Grytyr, Guttorm Andersen, Torgrim Karunakaran, Daniel Igland, Ragnar Gaskill, Collin SINTEF Ocean Trondheim Norway Norwegian Univ Sci & Technol Trondheim Norway SINTEF Digital Oslo Norway BP Houston TX USA Equinor Fornebu Fornebu Norway Kongsberg Maritime Kongsberg Norway Subsea7 Stavanger Norway Aker Solut Trondheim Norway Trelleborg Offshore Skelmersdale England

ISBN: (纸本)9780791885185

Vortices generated when flow pass offshore slender structures may cause structural vibrations due to fluid-structure interaction, termed vortex-induced vibrations (VIV). VIV can cause severe accumulated fatigue damage, which need to be considered in the structure design. To capture the non-linearities of different riser systems and time varying flow, time domain models are better alternatives than frequency domain models to predict VIV. The input hydrodynamic parameters of time domain models are semiempirical. However, versatile databases of hydrodynamic parameters are not well established, due to the large variations of the flow conditions, riser systems, and corresponding sub-structures. An optimization procedure could be implemented as an alternative to conventional experimental methods for establishing adaptive hydrodynamic parameter databases capable of accurately handling changes in the underlying structure of the system or flow regime. Literatures on application of optimization algorithms for various offshore engineering problems have been reviewed. VIV represents a constrained non-linear programming problem, where the sequential quadratic programming (SQP) algorithm - NLPQLP has been applied in this study. This paper proposes an optimization procedure of input hydrodynamic parameters for the time domain VIV solver - VIVANA-TD [21], for VIV prediction of various riser systems. A workflow integrating optimization and time domain VIV simulation has been established in the software work bench SIMA. By investigating the VIV prediction of typical laboratory model tests with optimized parameters, the feasibility of optimization using sequential quadratic non-linear programming algorithm - NLPQLP has been demonstrated.

关键词： optimization Vortex-induced vibration Flow (Dynamics) Pipeline risers Risers (Casting) Ocean engineering algorithms Nonlinear programming Databases Fatigue damage Simulation optimization algorithms Quadratic programming Design Experimental methods Vibration Vortices Computer software Workflow Fluid structure interaction

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ESTIMATING THE COMPLETE SOLUTION SET OF THE APPROXIMATE PATH SYNTHESIS PROBLEM FOR FOUR-BAR LINKAGES USING RANDOM MONODROMY LOOPS

ESTIMATING THE COMPLETE SOLUTION SET OF THE APPROXIMATE PATH...

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ASME International Design Engineering Technical Conferences / 42nd Annual Computers and Information in Engineering Conference (IDETC-CIE) / 46th Mechanisms and Robotics Conference (MR)

作者： Baskar, Aravind Hills, Caroline Plecnik, Mark Hauenstein, Jonathan D. Univ Notre Dame Aerosp & Mech Engn Notre Dame IN 46556 USA Univ Notre Dame Appl & Computat Math & Stat Notre Dame IN 46556 USA

ISBN: (纸本)9780791886281

It is natural to employ an optimization algorithm for the approximate kinematic synthesis of linkages. The hope is to find some superior points in the design space that indicate dimensions that are practically useful. One way to achieve this is to find all minima of an objective, then to filter them so the best remain. However, the prospect of finding all minima is bleak unless the optimization problem at hand is particularly small. In this work, we show how to find nearly all minima for a large optimization problem using polynomial homotopy continuation in the approximate synthesis of a four-bar path generator. The system at hand has a Bezout bound of 543,848,665 and a Schnabel estimate to the maximum number of stationary points of 6 center dot ( 303,249 +/- 713), within a 95% confidence interval. At least with regards to mechanism synthesis, this work represents the largest scale deployment to date of homotopy continuation to solve an unconstrained optimization problem. The challenges of scaling and suggestions for design are given. Example usage for the design of a leg mechanism is given. On the mechanism design front, this is the first presentation of a nearly complete (within the limitations of numerical discernment) solution of the general four-bar optimal path synthesis problem.

关键词： Linkages Design optimization Filters Generators optimization algorithms Polynomials Kinematics Dimensions

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AN optimization BASED APPROACH FOR DAMAGE IDENTIFICATION OF IDEALIZED SHIP STRUCTURAL ASSEMBLIES 40

AN OPTIMIZATION BASED APPROACH FOR DAMAGE IDENTIFICATION OF ...

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40th ASME International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Silionis, Nicholas E. Anyfantis, Konstantinos N. Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Athens Greece

ISBN: (纸本)9780791885123

Damage identification in ship structures is traditionally performed through on-site inspections. In this work, a first step is made towards assessing an in-line with operation ship hull Structural Health Monitoring system by registering onboard sensor data. Specifically, an optimization-based approach is proposed for solving the inverse problem for damage identification through processing static response data. Idealized geometry and loading conditions are considered for the deck and shell plating. Damage is abstractly represented as a single circular hole randomly located within the defined domain. Strain readings representing onboard measured data are provided by a FE model developed for this purpose. These correspond to zero-strain paths for each considered case: axial strains along the ship's neutral axis on the side shells and shear strains along the deck's centerline. Damage detection amounts to predicting its location, essentially considered the design variable of an optimization problem seeking to minimize an error function between strains measured for various damage scenarios and an indicative target scenario. Three established optimization algorithms are used for this task: a gradient-based, a Genetic Algorithm-based and a statistics-based method (Design of Experiments and Response Surface Methodology). Results indicate that the gradient and GA based approaches are more efficient while the less efficient statistics-based approach proved less computationally demanding.

关键词： optimization Ships Damage Statistics Shells Structural health monitoring Hull Experimental design Finite element model Geometry Inverse problems optimization algorithms Response surface methodology Sensors Inspection Plating Error functions Shear (Mechanics) algorithms Design

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Harmonized Integration of GWO and J-SLnO for Optimized Asset Management and Predictive Maintenance in Industry 4.0

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SENSORS 2025年第9期25卷 2896-2896页

作者： Arularasan, A. N. Ganeshkumar, P. Alkhatib, Mohammad Albalawi, Tahani BS Abdur Rahman Crescent Inst Sci & Technol Dept Comp Sci & Engn Chennai 600048 Tamil Nadu India Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Dept Comp Sci Riyadh 11432 Saudi Arabia

Highlights What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction *** What are the main findings? The study shows that, in terms of execution speed, cost-effectiveness, and energy usage, GWO and J-SLnO perform better than conventional scheduling algorithms. Compared to GWO, J-SLnO performs predictive maintenance activities with greater accuracy and stability. What is the implication of the main finding? In Industry 4.0, the suggested optimization strategies improve the effectiveness of predictive maintenance and asset management. J-SLnO is a dependable option for practical industrial applications that demand endurance and excellent prediction *** The study encompasses the application of two different advanced optimization algorithms on asset management and predictive maintenance in Industry 4.0-Grey Wolf optimization and Jaya-based Sea Lion optimization (J-SLnO). Using this derivative, the authors showed how these techniques could be combined through resource scheduling techniques to demonstrate drastic improvement in the level of efficiency, cost-effectiveness, and energy consumption, as opposed to the standard MinMin, MaxMin, FCFS, and Round Robin. In this sense, GWO results in an execution time reduction between 13 and 31%, whereas, in J-SLnO, there is an execution time reduction of 16-33%. In terms of cost, GWO shows an advantage of 8.57-9.17% over MaxMin and Round Robin, based on costs, while J-SLnO delivers a bette

关键词： Industry 4.0 optimization algorithms predictive maintenance Grey Wolf optimization (GWO) J-SLnO

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Gradient-Free optimization in Thermoacoustics: Application to a Low-Order Model

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JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME 2022年第5期144卷 051004-051004页

作者： Reumschuessel, Johann Moritz von Saldern, Jakob G. R. Li, Yiqing Paschereit, Christian Oliver Orchini, Alessandro Tech Univ Berlin Chair Fluid Dynam D-10623 Berlin Germany Tech Univ Berlin Lab Flow Instabilities & Dynam D-10623 Berlin Germany Harbin Inst Technol Ctr Turbulence Control Shenzhen 518055 Peoples R China Tech Univ Berlin Inst Fluid Mech & Tech Acoust D-10623 Berlin Germany

Machine learning and automatized routines for parameter optimization have experienced a surge in development in the past years, mostly caused by the increasing availability of computing capacity. Gradient-free optimization can avoid cumbersome theoretical studies as input parameters are purely adapted based on output data. As no knowledge about the objective function is provided to the algorithms, this approach might reveal unconventional solutions to complex problems that were out of scope of classical solution strategies. In this study, the potential of these optimization methods on thermoacoustic problems is examined. The optimization algorithms are applied to a generic low-order thermoacoustic can-combustor model with several fuel injectors at different locations. We use three optimization algorithms - the well established downhill simplex method, the recently proposed explorative gradient method, and an evolutionary algorithm - to find optimal fuel distributions across the fuel lines while maintaining the amount of consumed fuel constant. The objective is to have minimal pulsation amplitudes. We compare the results and efficiency of the gradient-free algorithms. Additionally, we employ model-based linear stability analysis to calculate the growth rates of the dominant thermoacoustic modes. This allows us to highlight general and thermoacoustic-specific features of the optimization methods and results. The findings of this study show the potential of gradient-free optimization methods on combustor design for tackling thermoacoustic problems, and motivate further research in this direction.

关键词： Stability Fuel injectors Oscillations optimization Fuels optimization algorithms Flames Fluctuations (Physics) Acoustics Gradient methods Pressure algorithms Evolutionary algorithms

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THREE-DIMENSIONAL ASTEROID RECONSTRUCTION VIA MULTI-ASPECT GROUND-BASED SAR IMAGES: AN optimization COMPARISON

THREE-DIMENSIONAL ASTEROID RECONSTRUCTION VIA MULTI-ASPECT G...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Liu, Yi Ding, Zegang Wang, Yan Zeng, Tao Dong, Zehua Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401147 Peoples R China

ISBN: (纸本)9781665403696

Main approaches of observing asteroids rely especially on ground-based optical telescopes, yet not capable of detecting detailed structures of the surface. However, ground-based radar observations can provide delay-Doppler images which help to reconstruct a more accurate shape model. To form the shape model of asteroids, we propose a shape reconstruction method via multi-aspect delay-Doppler radar images and describe the surface through spherical harmonic coefficients. In order to reconstruct shape model as accurate as possible, an optimal solution of the coefficients is required. Three traditional optimization algorithms are applied and performances are compared. When positive decision is made, suitable parameters can achieve effectively with these algorithms followed with an accurately recovered shape of asteroids. Numerical simulations have been conducted to demonstrate the effectiveness of the proposed method.

关键词： Asteroid shape model reconstrution Spherical harmonic coefficient Delay-doppler images optimization algorithms

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