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AIAA Aviation Forum and Exposition

作者： Chiang, Christopher Koo, David Zingg, David W. Univ Toronto Inst Aerosp Studies Toronto ON M3H 5T6 Canada

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

Ahigh-fidelity aerodynamic shape optimization framework based on the Reynolds-Averaged Navier-Stokes equations is applied to the optimization of a boundary layer ingesting S-duct designed for embedded engines on a high-subsonic, unmanned flight vehicle. The optimizations initially target a cruise operating condition and are further extended to single-point and multi-point optimizations considering descent and climb. Two different composite objective functions are used. The first combines distortion and swirl at the fan interface plane as well as total pressure recovery, with user-defined weights for each objective, while the second involves pressure recovery, fan blade load variation, and fan blade incidence variation. Pareto fronts show the trade-offs between objectives. The results indicate that compared to the baseline geometry, a simultaneous improvement in all objectives contained in the composite objective function can be obtained, depending on the priorities of each objective pre-assigned by the user. It was also found that if swirl can be ignored, then fan-face distortion can be greatly reduced while simultaneously reducing total pressure loss in the S-duct. Similarly, fan blade load variation and fan blade incidence variation can be significantly reduced while reducing total pressure loss. Finally, the multipoint optimization results show that a single S-duct geometry can perform well during cruise, climb, and descent conditions.

关键词： Momentum Boundary Layer Aerodynamic Shape optimization Reynolds Averaged Navier Stokes Fan Blades Flight Vehicle Blade Loading Aerodynamic Performance Flow Conditions optimization algorithm Sparse Nonlinear Optimizer

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A quantum inspired differential evolution algorithm with multiple mutation strategies 21

A quantum inspired differential evolution algorithm with mul...

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21st IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom)

作者： Liu, Jie Qin, XingSheng Jiang, F. Guangxi Normal Univ Coll Elect & Engn Guilin Peoples R China Univ Technol Sydney NSW Australia

ISBN: (纸本)9781665494250

The advent of the digital age and the internet has recently seen a corresponding increase in security concerns. Intrusion detection systems are one of the crucial factors to consider in today's digital world. This paper proposes a metaheuristic algorithm based on quantum differential evolution with multiple strategies. This algorithm proposes a new differential mutation strategy approach to improve the search capability and convergence speed. Then, a quantum rotation gate is used to perform the secondary evolution of the population. Finally, various benchmark functions are chosen to demonstrate the optimization ability of the algorithm. The experimental results show that the model outperforms differential evolution and quantum differential evolution. And has better optimization capability, efficiency and stability. This evolutionary technique plays an important role in identifying network intrusions and security attacks.

关键词： quantum inspired differential evolution algorithm mutation strategy network security rotation gate optimization algorithm

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Robust Pole Assignment via State Feedback for Uncertain High-order System 4

Robust Pole Assignment via State Feedback for Uncertain High...

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4th International Conference on Control and Robotics (ICCR)

作者： Gao, Yajun Duan, Guang-Ren Harbin Inst Technol Ctr Control Syst & Guidance Technol Harbin Peoples R China Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9781665486415

The problem of robust pole assignment for the uncertain high-order system is formulated. On the strength of a previous eigenstructure assignment result for the high-order system, by introducing the sensitivity of uncertain parameters in the system model to the closed-loop poles, the parametric expression of the state feedback gain matrix to the problem of robust pole assignment is obtained. The robust optimal solution can be derived by a simple independent optimization algorithm. The closed-loop poles can be fixed or chosen within certain desired regions on the complex plane, which further improves the robustness. The example illustrates robust state feedback controller obtained by the proposed algorithm has better robustness.

关键词： Robust pole assignment Uncertain high-order system Complete parametric solution optimization algorithm

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Optimizing manufacturing plant layouts using AI and heuristic algorithms

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年 1-11页

作者： Raju, G. S. Vizhian, S. Paul Kumar, M. Vijay Sufyan, Md Saeb Madhusudan, M. Gururaja, M. N. Prasad, C. Durga Mohan, Dhanesh G. Hanumanthappa, Harish Bangalore Univ Dept Mech Engn UVCE Bengaluru 560001 Karnataka India JSS Sci & Technol Univ Dept Mech Engn SJCE Mysore 570006 Karnataka India Govt Engn Coll Dept Civil Engn Nawada 805111 India Dayananda Sagar Coll Engn Dept Mech Engn Bengaluru 560111 Karnataka India Dayananda Sagar Acad Technol & Management Dept Mech Engn Kanakapura Rd Bengaluru 560082 Karnataka India RV Inst Technol & Management Dept Mech Engn Bengaluru 560076 Karnataka India Univ Sunderland Fac Technol Sch Engn Sunderland England Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura Punjab India RV Univ Sch Comp Sci & Engn Bengaluru Karnataka India

The study outlines an artificial intelligence-based methodology for defining manufacturing plant layout in the context of mass customisation, where a significant amount of part variability needs to be controlled. A variety of actions are suggested, with an emphasis on data transformations that make it possible to retrieve pertinent information for the plant layout design of the manufacturing facility. The article also uses optimization algorithms and simulation techniques for designing plant layouts and how those methods have changed throughout time as a result of the development of computer systems and artificial intelligence methods. A variety of layout metrics, including the number of facilities and temporary storage space, are gathered to determine the plan's efficiency. With the revised layout, the suggested solution boosts layout efficiency while reducing operator mobility and temporary storage space. Starting with the production routing of each product, the proposed algorithms would enable manufacturing companies to automate plant layout design in the context of mass customization using artificial intelligence techniques. It also emphasizes how important it is to review the layout design procedure whenever new products are introduced.

关键词： Manufacturing Plant layout optimization algorithm Artificial intelligence

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Leveraging Machine Learning for accurate Precipitation Forecasting

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Procedia Computer Science 2025年 259卷 1523-1532页

作者： T. Hashni T. Amudha

Forecasting precipitation is a vital in alerting people about potential challenges related to rain and enabling them to take active safety measures. Precipitation prediction will enhance the agricultural productivity, ensures a stable food supply, and maintains good water quality. This study emphases on developing the most accurately prediction model for the daily rainfall, considering the major effects of inconsistent rainfall in urban areas. The complex nature of precipitation, driven by a variety of environmental factors, which makes difficult to predict accurately. In this work, real-time environmental features for the model are fetched from Open Weather API. Subsequently, the Pearson correlation coefficient technique is applied to pick the relevant features which induce rainfall are utilized in the model as an input. The dataset was gathered from the resident meteorological division to evaluate the performance of various machine learning techniques such as Random Forest (RF), Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boost (XGBoost). In order to efficiently explore more parameter space, the swarm intelligence algorithm Fish Swarm optimization (FSO) was integrated with a better performing model. To validate the robustness and performance of the models, various classification metrics are utilized. With the accuracy of 87%, the XGBoost-FSO Hybrid Model performs superior than other models in predicting daily rainfall.

关键词： Precipitation Machine learning Swarm Intelligence optimization algorithm Pearson correlation

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A Repetitive Parameterization and optimization Strategy for the Calibration of Complex and Computationally Expensive Process-Based Models With Application to a 3D Water Quality Model of a Tropical Reservoir

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WATER RESOURCES RESEARCH 2022年第5期58卷

作者： Xia, Wei Shoemaker, Christine Ann Natl Univ Singapore Dept Ind Syst Engn & Management Singapore Singapore Campus Res Excellence & Technol Enterprise CREATE Energy & Environm Sustainabil Megac E2S2 Phase II Singapore Singapore Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore

Parameter calibration is critical for modeling, especially for current process-based models that are complex with many chemical and biological processes and immeasurable model parameters. This analysis quantifies significant disadvantages of the traditional use of local or global sensitivity analysis (SA) for selecting calibration parameters of nonlinear, expensive models when there are a large number of constituents and parameters. We propose a new Repetitive parameterization and optimization (Rep-OPT) strategy that uses multiple optimization steps;and between each optimization step, a modeler picks the parameters to be optimized in the next optimization step. The modeler picks the parameters in each iteration following a suggested set of steps that analyze which processes and parameters are related to the poorly fit constituents with the current parameter set. We successfully applied the Rep-OPT strategy on a complex tropical water quality model with more than 91 parameters using real data. We demonstrate that expert knowledge with assistance of proposed postanalysis techniques (i.e., trade-off analysis, component analysis, and mass-balance analysis) can identify the right calibration parameters and obtain excellent model fit. In contrast, the traditional approach using SA with optimization (SA-OPT) does not find the right calibration parameters for our data. The solution found by Rep-OPT excellently improves manual solution by 32.7% in goodness-of-fit, and all calibrated constituents fit well to observations. The solution found by SA-OPT using global SA improves manual solution by only 13.3%. Local sensitivity by SA-OPT performs very poorly being 49.6% worse than manual solution.

关键词： model calibration optimization algorithm expert knowledge water quality models sensitivity analysis

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Abatement of Sewer Overflow Pollution Based on Distributed Optimal Control Approach

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ACS ES&T WATER 2022年第3期4卷 859-868页

作者： Zhao, Zhichao Zhang, Huijin Yu, Ziwen Yin, Hailong Xu, Zuxin Changjiang Inst Survey Planning Design & Res Wuhan 430010 Hubei Peoples R China Tongji Univ Key Lab Yangtze River Water Environm Minist Educ Shanghai 200092 Peoples R China Tongji Univ Shanghai Inst Pollut Control & Ecol Safety Shanghai 200092 Peoples R China Univ Florida Gainesville FL 32603 USA

Optimal control is promising to mitigate the pollution from combined sewer overflows during wet-weather days. In this study, a distributed optimal control approach to regulating multipoint controlling units of the urban sewer system was developed. By introducing a differential evolution algorithm with ranking-based mutation operators, good simulation results with lower overflow mass loadings can obtain higher evolutionary probabilities. Thus, the optimal solutions to minimize the overflow mass from a large sewer network can be efficiently obtained. The developed approach was demonstrated in a catchment (total coverage area of 22.4 km2) served by one wastewater treatment plant in Chaohu City, Anhui Province of China, with five pumping stations and 18 associated sewage or stormwater pumps as the distributed controllers. After a global optimization among various controlling units, performance of the sewer network in reducing its overflow was improved obviously through fully utilizing in-sewer storage and adjusting the sewer flow transportation harmoniously (i.e., an alternative mode of full storing and fast transportation in sequence). Accordingly, the overflow volume and the overflow mass loading of total nitrogen was reduced by 32.3% and 37.6%, respectively, without retrofitting the current sewer network. On the basis of the presented approach, decision-makers can develop a database for optimal regulation of sewer network under various rainfall scenarios and respond to sewer overflow control flexibly and cost-effectiveness.

关键词： urban sewers optimization algorithm sewer overflow integrated regulation mathematical model

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Intra City Transport On-demand

Intra City Transport On-demand

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IFAC Workshop on Control for Smart Cities (CSC)

作者： Netov, Nikolay Spassov, Kamen Sofia Univ St Kliment Ohridski Sofia 1113 Bulgaria

An algorithm to calculate optimal trajectories of e-buses to fulfil on-demand requests for transportation in a smart city environment is presented. Instead of solving the transportation problem once when all transportation requests are registered, we solve the transportation problem at any virtual or public station during the taxi. This approach enables gathering transportation requests dynamically and also consider changes in the environment (e.g. weather conditions, roadblocks, traffic jams, etc) during the transportation. The algorithm is tested with real data. Some results of the simulation are presented. Copyright (C) 2022 The Authors.

关键词： Transport on demand Optimal route Transportation problem optimization algorithm Model Simulation Green city Smart city Optimal control

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An algorithm to optimize the optical sensor design for tip clearance and tip timing measurements 7

An algorithm to optimize the optical sensor design for tip c...

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Conference on Optical Sensing and Detection VII Part of SPIE Photonics Europe Conference

作者： Zubia, Gorka Amorebieta, Josu Zubia, Joseba Durana, Gaizka Univ Basque Country Dept Commun Engn Plaza Ingeniero Torres Quevedo 1 E-48013 Bilbao Basque Country Spain

ISBN: (纸本)9781510651555;9781510651548

In this work, an algorithm for the optimization of the design of an optical fiber bundle displacement sensor for Tip Clearance and Tip Timing measurements is presented. The former is based on the gaussian beam mathematical approach, and implements a fiber arrangement comprised of a transmitting single-mode fiber and two concentric rings of receiving multimode fibers. The software includes a graphical user interface that allows modifying relevant geometrical parameters of the sensor and fiber characteristics, and enables monitoring in real time the effects of such modifications. Results indicate that in order to achieve the most sensitive design with the largest operating range, the key parameters are the gain configuration, the distance between the receiving fiber rings and the radii of receiving fibers, and that the best fitting sensor configuration is a tradeoff among the aforementioned characteristics as it is heavily dependent on the measurement constraints. The results obtained by the simulation tool were validated in good agreement with the calibration curve of the manufactured optical fiber bundle displacement sensor in a scaled rotor.

关键词： optical fiber sensor optimization algorithm tip clearance tip timing aircraft turbines displacement measurement

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Double Decomposition-Based Wind Speed Prediction Model for Urad Area 2022

Double Decomposition-Based Wind Speed Prediction Model for U...

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6th International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Liu, Chuyi Zheng, Fengfeng Wan, Jianxiong Li, Leixiao Ma, Zhiqiang Wang, Yongsheng Inner Mongolia Univ Technol Hohhot Inner Mongolia Peoples R China Inner Mongolia Autonomous Reg Engn & Technol Res Hohhot Inner Mongolia Peoples R China

ISBN: (纸本)9781450387477

Renewable energy becomes progressively more important as time goes on. Wind, as one of the main rapidly developing renewable energy, is free, widely distributed, clean, environmental protection and sustainable development. The uncertainty of wind power which causes the inaccuracy of traditional wind speed prediction has become great challenge for wind power utilization. This paper predicts the wind speed by analyzing the measured data of 70-100m wind tower in Urad area. Handling the high-frequency sub-sequences after the first-level decomposition which cause the inaccuracy, a double decomposition based on EWT-VMD is proposed, which uses SampEn to determine the parameters of them. And then applied LSTM neural network to predict the wind speed one step in advance. The MAE, RMSE and R-2 of the proposed model improved by at least 10.5%, 11.4% and 1.5% when comparing with other models.

关键词： wind energy wind speed prediction optimization algorithm decomposition algorithm

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