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Recent advances of helical baffle heat exchangers: Principles, structural optimization, application and beyond

APPLIED THERMAL ENGINEERING 2025年 274卷

作者： Cheng, Lingyan Liu, Zhan Gu, Huaduo Wu, Jiafeng Chen, Yaping Sunden, Bengt China Univ Min & Technol Sch Mech & Civil Engn State Key Lab Intelligent Construct & Hlth Operat Xuzhou 221116 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong 999077 Peoples R China Southeast Univ Engn Res Ctr BEEE Sch Energy & Environm Minist Educ Nanjing 210096 Peoples R China Lund Univ BS Heat Transfer & Fluid Flow S- 26253 Angelholm Sweden

Due to high heat transfer efficiency and low flow resistance, helical baffle heat exchangers (HBHEs) have attracted much attention and are widely utilized in industrial engineering. Aiming at efficient performances and optimal design parameters, this review provides a unified view of shell-side heat transfer enhancement techniques for HBHEs and outlines a comprehensive optimization process, including helical baffle design, shell-side fluid optimization, and advanced algorithm application. We summarize the evaluation indices for shell-side heat transfer enhancement effects, including heat transfer coefficient, pressure drop, Nusselt number, friction factor, and comprehensive performance indices. Eventually, we point out the potential challenges in terms of heat transfer improvement, pressure drop control, and fabrication processes, as well as future research directions in this field.

关键词： Helical baffle heat exchangers Heat transfer performance Shell-side optimization Baffle structures optimization algorithm

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Layout optimization algorithms for the Offshore Wind Farm with Different Densities Using a Full-Field Wake Model

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ENERGIES 2023年第16期16卷 5916页

作者： Liang, Zhichang Liu, Haixiao Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

To decrease the power deficit of a wind farm caused by wake effects, the layout optimization is a feasible way for the wind farm design stage. A suitable optimization algorithm can significantly improve the quality and efficiency of the optimization process. For exploring the high-performance algorithms under different layout densities, a comparison is conducted by optimizing the layout of a real offshore wind farm with five algorithms, namely two population-based algorithms and three single-point algorithms. Wake effects are considered by a full-field wake model. A penalty function is proposed for the population-based algorithms to handle the constraint violations. Different iterations and constraints of the layout density are applied in the optimization. The random search has the best optimization results in all the cases and the control of the feasibility check is necessary for this algorithm. More iterations can advance the optimization results. The density constraint greatly affects the computational cost of the random search, which is significantly increased under the strict constraint. Except under the strict constraint, the random search has the best performance of optimization efficiency. A combination of the pattern search and random search is recommended when the strict constraint is applied in the layout optimization.

关键词： wind farm layout optimization optimization algorithm layout density wake model penalty function random search pattern search

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optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Kazemi, Farzin Asgarkhani, Neda Jankowski, Robert Gdansk Univ Technol Fac Civil & Environm Engn ul Narutowicza 11-12 PL-80233 Gdansk Poland Univ Naples Federico II Sch Polytech & Basic Sci Dept Struct Engn & Architecture Naples Italy

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and postprocessing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, lambda, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.

关键词： Reinforced concrete shear wall Machine learning algorithm Seismic failure probability optimization algorithm Seismic performance curve Seismic retrofit

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optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm

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SCIENCE PROGRESS 2024年第1期107卷 368504241238078页

作者： Zhao, Weixi Liang, Te Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming Peoples R China Beihang Univ Fac Instrumentat Sci & Optoelect Engn Beijing Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

关键词： Flight sequence optimization algorithm sparrow algorithm air traffic management

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optimization problems in water distribution systems using Non-dominated Sorting Genetic algorithm II: An overview

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AIN SHAMS ENGINEERING JOURNAL 2023年第4期14卷

作者： Shirajuddin, Talhah Mohamad Muhammad, Nur Shazwani Abdullah, Jazuri Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Univ Teknol MARA Coll Engn Sch Civil Engn Shah Alam 40450 Selangor Malaysia

The application of a genetic algorithm inspired multi-objectives optimization algorithms, known as the Non-dominated Sorting Genetic algorithm (NSGAii) in solving optimization problems related to Water Distribution System (WDS) is critically reviewed. The list of variations of NSGAii used by other research-ers in solving WDS are assessed, including an improved version of NSGAii and its combination with other algorithms in solving the problems. The optimization problems include WDS optimal design, pipe reha-bilitation strategies, water quality, leakages and pump operation. The existing algorithm has been improved to solve multi-objectives and yet researchers are keen in using the NSGAii algorithm. Improvised and revolutionary algorithms, such as RNSGAii, sigma-NSGAii and NSGAiii, still received less attention even though these algorithms have been introduced since 2005 and model capabilities to solve more complex problem is proved. The application of these algorithms should be seriously considered due to increasing aging of pipe in the future.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： NSGAii optimization algorithm Optimal design problems Pipe rehabilitation Water distribution system Water quality problems

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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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Prediction accuracy improvement of pressure pulsation signals of reversible pump-turbine: A LSTM and VMD-based optimization approach

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ENERGY SCIENCE & ENGINEERING 2024年第1期12卷 102-116页

作者： Fang, Mingkun Zhang, Fangfang Cao, Zhong Tao, Ran Xiao, Wei Zhu, Di Gui, Zhonghua Xiao, Ruofu China Agr Univ Coll Water Resources & Civil Engn Beijing Peoples R China China Agr Univ Beijing Engn Res Ctr Safety & Energy Saving Techno Beijing Peoples R China CTG Hainan Green Dev Investment Co Ltd Operat Management Dept Hainan Peoples R China Wuhan Univ State Key Lab Water Resources Engn & Management Wuhan Peoples R China State Grid Xinyuan Co Ltd Pumped Storage Technol & Econ Res Inst Beijing Peoples R China China Agr Univ Coll Engn Beijing Peoples R China

The reversible pump-turbine plays an important role in hydropower stations, but pressure pulsation during their operation affects their performance and lifespan. Accurate prediction of pressure pulsation signals can provide an important basis for energy planning and stable operation of pumped storage units, thereby promoting sustainable development of the environment. This study introduces an optimization method that combines long short-term memory (LSTM) and variable mode decomposition (VMD) to enhance the prediction accuracy of pressure pulsation signals. First, by decomposing the pressure pulsation signal into multiple relatively stable subsequence components using VMD, the characteristics of the original signal become more distinct. Subsequently, individual LSTM-based time series prediction models were constructed for each modal function, and the hyperparameters related to subsequence were optimized using the sparrow search algorithm. To validate the efficacy of the proposed approach, this paper conducted experiments using pressure pulsation signals of a pump-turbine obtained through numerical simulation. The experimental data was divided into training and testing sets, with the former used to train the LSTM model and the latter used for validation. The experimental results show that the optimized VMD with an optimized LSTM method can effectively improve the prediction accuracy of pressure pulsation signals in reversible pump-turbine. A dual optimization prediction model based on a sparrow search algorithm, variable mode decomposition, and long short-term memory is proposed, and the results are compared with traditional prediction models. The pressure pulsation signals at different monitoring points of the pump-turbine are predicted, and the prediction accuracy and error of different prediction models are analyzed, verifying the superiority of the proposed prediction model in this ***

关键词： machine learning optimization algorithm pump turbine signal decomposition variational mode decomposition

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Multi-Disciplinary and Multi-Objective optimization Method Based on Machine Learning

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AIAA JOURNAL 2024年第2期62卷 691-707页

作者： Dai, Jiahua Liu, Peiqing Li, Ling Qu, Qiulin Niu, Tongzhi Beihang Univ Sch Aeronaut Sci & Engn Beijing 100191 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

The optimization of aircraft is a typical multidisciplinary and multi-objective problem. To solve this problem, the difficulty lies not only in the high cost of discipline performance evaluation but also in the complex coupling relationship between different disciplines. To improve the optimization efficiency, a new optimization method is proposed, including two new algorithms: conditional generative adversarial nets with vector similarity (VS-CGAN) and distributed single-step deep reinforcement learning with transfer learning (TL-DSDRL). For low-cost disciplines, VS-CGAN learns the relationship between variables and objectives through presampling to compress the variable domains. The cosine function is used to evaluate the similarity between the random noise and generated variables to avoid mode collapse. For high-cost disciplines, TL-DSDRL improves optimization efficiency through pretraining. The newly designed reward function and multi-agent cooperation mechanism enhance the multi-objective search ability of reinforcement learning.

关键词： Reinforcement Learning optimization algorithm Aerodynamic Performance Mean Aerodynamic Chord Generative Adversarial Network Aerodynamic optimization Take off and Landing Reynolds Averaged Navier Stokes

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Online Bayesian optimization of Polynomial-Multigrid Cycles for Flux Reconstruction

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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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