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Online Bayesian optimization of Polynomial-Multigrid Cycles for Flux Reconstruction

AIAA JOURNAL 2024年第7期62卷 2692-2707页

作者： Mishra, Sambit Trojak, Will Witherden, Freddie D. Texas A&M Univ Dept Ocean Engn Haynes Engn Bldg727 Ross St College Stn TX 77843 USA IBM Res UKI Hartree Ctr London SE1 7ND England

In this study, we present a novel strategy for dynamically optimizing polynomial multigrid cycles to accelerate convergence within the dual-time-stepping formulation of the artificial compressibility method. To accomplish this, a Gaussian process model is developed using Bayesian optimization to efficiently sample possible cycles to minimize run-time. To allow the use of conventional optimization methods, we developed fractional smoothing steps, moving the optimization from a discrete space to a continuous space. Initially, a static, offline, approach was developed, and optimal cycles were found for two flow past cylinder test cases with Re=200 and Re=500;however, when exchanging optimal cycles between the different test cases, there was significant degradation in speedup. Toward this, a dynamic, online, approach was developed where cycles are optimized during a simulation. The performance of the resulting optimal cycles gave a similar speedup to the offline approach while achieving a net reduction in run-time. Again testing the optimization strategy on the flow past a cylinder, this yielded candidates with mean speedups of similar to 3.0x and similar to 2.1x, respectively. Finally, testing online optimization on a turbulent flow past a cylinder at Re=3900 resulted in an overall speedup of similar to 1.9x.

关键词： Bayesian optimization Two Dimensional Flow Thermodynamic Properties optimization algorithm Statistical Analysis Computational Fluid Dynamics Boundary Layer Separation Discontinuous Spectral Element Method Real Time optimization Tetrahedral Element

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Coupling simulation of pipeline nodes - Storage tank linkage in urban high-density built-up areas using optimization model

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 357卷 120850页

作者： Zhang, Yangxuan Jiang, Chunbo Han, Qiaohui Zhang, Xiang Li, Jiake Xiao, Yi Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China Wuhan Univ Hubei Key Lab Water Syst Sci Sponge City Construct Wuhan 430072 Peoples R China

Climate change and urbanization contribute to the increased frequency of short-duration intense rainstorms. Traditional solutions often involve multiple scenarios for cost-effectiveness comparison, neglecting the rationality of placement conditions. The effective coupling and coordination of the location, number, size, and cost of storage tanks are crucial to addressing this issue. A three-phase approach is proposed to enhance the dynamic link between drainage pipeline and storage tanks in urban high-density built-up areas, integrating Python language, SWMM, the Elitist Non-Dominated Sorting Genetic algorithm (NSGA-III), and the Analytic Hierarchy Process (AHP) methods. In the first stage, each node within the pipeline network is considered as a potential storage tank location. In the second stage, factors such as the length and diameter of the upstream connecting pipeline, as well as the suitability of the storage tank location, are assessed. In the third stage, the length and diameter of the downstream connecting pipeline node are evaluated. The results show that the 90 overflow nodes (overflow time >0.5h) have been cleared using the three-phase approach with a 50a (duration = 3h) return period as the rainfall scenario, which meets the flooding limitations. After the completion of the three-phase method configuration, the total overflow and SS loads were reduced by 96.45% and 49.30%, respectively, compared to the status quo conditions. These two indicators have decreased by 48.16 and 9.05%, respectively, compared to the first phase (the traditional method of only replacing all overflow nodes with storage tanks). The proposed framework enables decision-makers to evaluate the acceptability and reliability of the optimal management plan, taking into account their preferences and uncertainties.

关键词： Storage tank optimization algorithm Dynamic process Position rationality

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Study and Application of optimization algorithm about Sinusoidal Steady-State Circuit

Study and Application of Optimization Algorithm about Sinuso...

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第三十四届中国控制会议

作者： Hou Xiang Lin Li Qi Chen Cong Sun Hong School of Mechanical Engineering Shenyang Jianzhu University SKLOIS Institute of Information Engineering CAS Graduate University of Chinese Academy of Sciences

ISBN: (纸本)9781467374439

Based on Kirchhoff Law about arbitrary sinusoidal steady-state circuit network, optimization principle of dynamic design variables is adopted. Making real parts and imaginary parts of sub-circuit current and node potential as design variables, and equilibrium relation between node potential and sub-circuit current as frame-objective function, dynamic design variables optimization algorithm analysis of arbitrary complicated sinusoidal steady-state circuit network is proposed. Universal program computing sub-circuit current and node potential is completed. Practical examples are computed. Effectiveness and feasibility is verified. A new clue is set up for computing complicated alternating-current circuit network rapidly and precisely.

关键词： sinusoidal steady-state circuit network dynamic design variables frame-objective function optimization algorithm program design

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Carbon Emissions and Parameter optimization for Machine Tool Processing

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IEEE SENSORS JOURNAL 2024年第17期24卷 27225-27237页

作者： Yau, Her-Terng Kuo, Ping-Huan Tu, Tzung-Lin Chen, Yu-Tsun Natl Chung Cheng Univ Dept Mech Engn Chiayi 62102 Taiwan Natl Chung Cheng Univ Adv Inst Mfg High Tech Innovat AIM HI Chiayi 62102 Taiwan

The imposition of carbon taxes on goods imported by the European Union can considerably affect economic development. Computer numerical control (CNC) machine tools are indispensable in manufacturing, and reducing their environmental impact is crucial. Most machining pollution is produced during the operation of these tools. In this study, the carbon emissions of machine tools during energy consumption (EC) and machining processes were examined. Next, EC models were curve-fit using the least-squares method, and the leave-one-out method was employed to enhance the fairness and stability of the training process. In this manner, second-order power consumption models were obtained. On the basis of these models, this study conducted multiobjective optimization for effectively reducing carbon emissions, minimizing processing times, and maximizing surface quality. Three optimization algorithms-particle swarm optimization (PSO), the genetic algorithm (GA), and gray wolf optimization (GWO)-were used for the multiobjective optimization, and the GWO algorithm was found to yield the best results. Implementation of the GWO-optimized parameters in an actual machining process resulted in reductions of 54.4%, 16.3%, and 14.7% in carbon emissions, processing time, and surface roughness, respectively. Thus, the method proposed in this article can achieve efficient green manufacturing without sacrificing machining quality, thereby contributing to sustainable machining operations.

关键词： Carbon dioxide Feeds Machining Production optimization Surface roughness Rough surfaces Carbon emissions least-squares method optimization algorithm surface roughness

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Fast and Robust optimization of Full Trajectory from Entry Through Powered Descent

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2024年第2期47卷 203-216页

作者： Lu, Ping Sandoval, Sergio Davami, Christopher San Diego State Univ Dept Aerospace Engn San Diego CA 92182 USA NASA Johnson Space Ctr Houston TX USA

A high-mass Mars entry, descent, and landing (EDL) mission for cargo delivery or human exploration faces the challenge of a high propellant mass fraction requirement for powered descent. This work develops a novel method and the associated algorithm that utilize existing entry and propellant-optimal powered descent guidance algorithms for fast and robust optimization of the end-to-end EDL trajectory for achieving the overall optimized propellant efficiency. A bilevel optimization formulation aided by a predictive logic based on the optimal powered descent algorithm to determine the near-optimal transition from entry to powered descent allows the end-to-end trajectory to be optimized in a relatively simple manner. No new major software or algorithms are required other than the existing guidance algorithms. A solution to the bilevel optimization problem is shown to exist, and the convergence of the bilevel optimization algorithm is guaranteed under certain mild assumptions. The algorithm developed in this paper is able to find consistently an end-to-end near-optimal EDL trajectory in just over 10 s on a desktop computer, while general-purpose modern trajectory optimization software can take thousands of seconds. The effectiveness and robustness of the algorithm are demonstrated by successfully optimizing thousands of complete EDL trajectories efficiently and reliably from dispersed initial entry conditions.

关键词： optimization algorithm Entry, Descent, and Landing Spacecraft Guidance Mars Atmospheric Entry Mars Landers Manned Mars Missions Trajectory optimization Hypersonic Flight Powered Descent Guidance Reentry Guidance

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Advanced modelling and optimization of steam methane reforming: From CFD simulation to machine learning - Driven optimization

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 96卷 1262-1280页

作者： Jafarizadeh, Azadeh Panjepour, Masoud Emami, Mohsen Davazdah Isfahan Univ Technol Dept Mat Engn Esfahan *** Iran Isfahan Univ Technol Dept Mech Engn Esfahan *** Iran

Computational fluid dynamics simulations were utilized to investigate the steam methane reforming process with the aim to improve its efficiency. Key parameters examined for their impact on process performance included surface heat flux (73-108 kW/m2), tube length (1-16 m), steam-to-carbon ratio (1.4-4), and flow rate (0.22-0.38 kg/s). To analyze the simultaneous effects of these variables while reducing computational costs, Deep Neural Networks (DNN) were employed. An optimized DNN was designed to achieve acceptable performance, featuring an input layer with four neurons that represent reformer length, flow rate, heat flux, and steamto-carbon ratio. The network includes four hidden layers with 32, 16, 8, and 8 neurons respectively, and concludes with an output layer comprising seven neurons for residual methane, water vapor, produced hydrogen, carbon dioxide, carbon monoxide, wall temperature, and gas outlet temperature. The results indicated that the proposed model achieved high accuracy, exceeding 99%, in predicting both training and test data. Following the DNN modeling, an optimization algorithm based on the random search method was developed. This algorithm searches a wide range of parameters to identify the optimal conditions for simultaneously maximizing hydrogen production and minimizing reformer length.

关键词： Computational fluid dynamics Steam methane reforming Deep neural networks optimization algorithm Hydrogen production

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Structural optimization Design of Microfluidic Chips Based on Fast Sequence Pair algorithm

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MICROMACHINES 2023年第8期14卷 1577-1577页

作者： Wu, Chuang Sun, Jiju Almuaalemi, Haithm Yahya Mohammed Sohan, A. S. M. Muhtasim Fuad Yin, Binfeng Yangzhou Univ Sch Mech Engn Yangzhou 225127 Peoples R China Nantong Fuleda Vehicle Accessory Component Co Ltd Nantong 226300 Peoples R China Jiangsu Tongshun Power Technol Co Ltd Nantong 226300 Peoples R China Univ Adelaide Fac Engn Dept Mech Engn Adelaide SA 5000 Australia

The market for microfluidic chips is experiencing significant growth;however, their development is hindered by a complex design process and low efficiency. Enhancing microfluidic chips' design quality and efficiency has emerged as an integral approach to foster their advancement. Currently, the existing structural design schemes lack careful consideration regarding the impact of chip area, microchannel length, and the number of intersections on chip design. This inadequacy leads to redundant chip structures resulting from the separation of layout and wiring design. This study proposes a structural optimization method for microfluidic chips to address these issues utilizing a simulated annealing algorithm. The simulated annealing algorithm generates an initial solution in advance using the fast sequence pair algorithm. Subsequently, an improved simulated annealing algorithm is employed to obtain the optimal solution for the device layout. During the wiring stage, an advanced wiring method is used to designate the high wiring area, thereby increasing the success rate of microfluidic chip wiring. Furthermore, the connection between layout and routing is reinforced through an improved layout adjustment method, which reduces the length of microchannels and the number of intersections. Finally, the effectiveness of the structural optimization approach is validated through six sets of test cases, successfully achieving the objective of enhancing the design quality of microfluidic chips.

关键词： microfluidic chip simulated annealing algorithm fast sequence pair algorithm structural design optimization algorithm

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Comparison of different optimization techniques applied to optimal operation of energy storage systems in standalone and grid-connected direct current microgrids

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JOURNAL OF ENERGY STORAGE 2024年 96卷

作者： Montano, Jhon Guzman-Rodriguez, Juan Pablo Palomeque, Jose Mena Gonzalez-Montoya, Daniel Inst Tecnol Metropolitano Dept Elect & Telecommun Medellin 050028 Colombia Inst Univ Digital Antioquia Fac Ingn & Ciencias Agr Medellin 050028 Colombia

This study addresses the problem of optimal operation and functioning of lithium-ion batteries in direct current (DC) microgrids with photovoltaic generators, in both urban and rural settings, under an approach that considers three mathematical models as objective functions: reduction of operational costs, reduction of power losses associated with energy transport, and reduction of CO 2 emissions produced by conventional generators. After defining the objective functions, six optimization algorithms are integrated as solution methodologies: Equilibrium Optimizer (EO), Salp Swarm algorithm (SSA), Particle Swarm optimization (PSO), Sine and Cosine algorithm (SCA), Black Hole Optimizer (BHO), and the Generalized Normal Distribution Optimizer (GNDO) algorithm. These methodologies are used to determine the hourly power flow through successive approximations, thus evaluating each of the objective functions with the system's constraints in order to achieve reductions of each objective function and thereby confirm the quality of the solutions applied to the problem under analysis. Where, for the urban case of Medell & iacute;n, the optimization achieved minimum reductions of 0.163% in fixed costs, 1.436% in variable costs, 7.160% in power losses, and 0.165% in CO 2 emissions. In the rural case of Capurgan & aacute;, the results showed minimum reductions of 0.095% in energy costs and 10.938% in power losses. These numerical results confirm the effectiveness of the applied optimization algorithms in reducing operational costs, power losses, and emissions, thereby validating the quality of the solutions for the analyzed problem.

关键词： Energy management system Batteries DC microgrids optimization algorithm Variable power demand Variable renewable generation

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Gradient-Based optimization Method for Experimental Modal Parameter Estimation with Finite Element Model

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AIAA JOURNAL 2024年第9期62卷 3544-3558页

作者： Xu, Zhaoyi Zheng, Gangtie Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

This paper presents a novel gradient-based optimization algorithm for improving the accuracy of experimentally estimated modal parameters with the assistance of finite element models. Initially, we recast the discrete vibration response equation into a matrix form and formulate the parameter estimation problem in modal analysis as an optimization problem. Then the problem is solved with a gradient-based iterative algorithm, which explicitly exhibits the closed form of gradients used in optimization. Initial values for this iteration are parameters derived from finite element models, since every important engineering structure should be analyzed with a finite element model before it is constructed. Subsequently, the performance of this algorithm is validated by both pure numerical experiments, which simulate the physical world, and experiments using real measurement data gathered by sensors in the real physical world. The algorithm's performance is further enhanced by incorporating gradient clipping and an adaptive iteration threshold. As a comparison, a discussion on classical least-squares time-domain method for the problem is provided. For practical applications, the Shi-Tomasi corner detection and Lucas-Kanade optical flow methods are deployed to detect corner points from videos taken during the vibration of a structure and track the motion of these points in the videos.

关键词： optimization algorithm Finite Element Modeling Acceleration Sensors Operational Modal Analysis algorithms and Data Structures Corner Detection Mechanical and Structural Vibrations Beam (Structures) Applied Mathematics Signal Processing

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BIM-based intelligent optimization of complex steel joints using SVM and NSGA-II

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2024年 223卷

作者： Lai, Yaping Ke, Ke Wang, Letian Wang, Lufeng Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China TY Lin Int Engn Consulting China Co Ltd Chongqing 401121 Peoples R China Chongqing Univ Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China

Steel joints are vital load-bearing components in structures. In some structures like large-scale bridges, these joints become increasingly complex. However, the current optimization of complex steel joints primarily relies on empirical knowledge and manual trial-and-error. This paper proposes an approach for the optimization of complex steel joints using SVM (support vector machines) and NSGA-II (non-dominated sorting genetic algorithms II) in BIM environment. Initially, this research utilizes Rhino, Grasshopper, and Abaqus to create a BIM framework for intelligent optimization of complex steel joints. Partial components of joints are parameterized, followed by the implementation of finite element (FE) parametric modeling. Subsequently, the outcomes from FE analysis of the parameterized model are employed to train a surrogate FE model of complex steel joints using SVM. Finally, the optimized design is achieved using NSGA-II, based on the surrogate model. Through the comparison experiment of a practical engineering case, it is proved that the proposed approach can effectively assist designers in the complex steel joint design.

关键词： Steel structure Steel joints Intelligent design optimization algorithm Grasshopper

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