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18th Symposium on International Automotive Technology, SIAT 2024

作者： Karthikeyan, Vikram Raj Gumma, Muralidhar Altair Engineering India Pvt. Ltd. India

The objective of this paper is to determine and design an optimized thermal management system for a solar electric four-wheeler while considering system influence. The major systems that will be analyzed and optimized include the HVAC, solar system, and battery. The HVAC system imposes a challenge to the designers to fulfill the passenger's comfort and to operate it efficiently under a wide range of external loads from solar radiation, ambient temperature & and humidity, human metabolic activity, and other loads like the propulsion system temperature on the cabin. From the literature, it is found that the air conditioning system reduces on average 14% of the total battery capacity whereas the heating system reduces it by 18% [1], which makes the HVAC system design a crucial aspect to consider for the system influence. The battery car voltage changes significantly to meet the power demand and because of this, the battery system produces a large amount of heat while discharging which will overheat the battery system and can lead to hazardous chemical reactions or even the efficiency will get seriously compromised if the thermal management system is not properly designed. The solar power system being an additional source of power must be effectively designed with all the required power electronic controllers such that the additional power produced from the solar compensates for the thermal losses at the system level and provides an additional range to the battery. To achieve this system-level optimization, a model-based system simulation approach with a genetic algorithm will be applied to optimize the thermal variables for the system. The variables will be selected considering the trade-offs between performance, efficiency, and comfort. The expected outcome of this project is an efficient and optimized thermal management system that will enhance the range of the solar electric four-wheeler. © 2024 SAE International. All rights reserved.

关键词： Battery thermal management system (BTMS) genetic algorithm optimization HVAC system Model-based system simulation Solar electric four-wheeler Solar power system

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Large-Scale Inverse Design of a Planar On-Chip Mode Sorter

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ACS PHOTONICS 2022年第2期9卷 378-382页

作者： Di Domenico, Giuseppe Weisman, Dror Panichella, Annibale Roitman, Dolev Arie, Ady Tel Aviv Univ Sch Elect Engn Fleischman Fac Engn IL-69978 Tel Aviv Israel Delft Univ Technol Software Technol NL-2628 XE Delft Netherlands

Spatial modes of light can be used as carriers of information in classical optical communication or as an alphabet in quantum optical communication. In order to exploit the spatial domain, it is required to (de)multiplex different modes from a shared input channel into different output ports. Mode sorters have been employed in free-space and fiber systems but to date have not been realized for planar guided waves. Here we present a general method for compact on-chip sorting of different planar beams with a micrometric footprint and nanometric thickness. The designs were generated using a linkage-tree-based genetic algorithm and were experimentally demonstrated on a surface plasmon polariton platform by sorting of Hermite-Gaussian beams. The method used here can be readily applied to optimize complex, large-scale optical devices involving beam propagation methods.

关键词： integrated photonics mode (de)multiplexing dielectric metasurface genetic algorithm optimization subwavelength grating

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geneticSecOps: Harnessing Heuristic genetic algorithms for Automated Security Testing and Vulnerability Detection in DevSecOps 6

GeneticSecOps: Harnessing Heuristic Genetic Algorithms for A...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Thantharate, Pratik Anurag, T. IEEE Member United States

ISBN: (纸本)9798350304480

This paper addresses security testing and vulnerability detection within the DevSecOps framework, emphasizing the need for automated tools to streamline and enhance security practices. Current challenges are identified, and related work is reviewed to establish context. Our Python-based solution offers two main components: an automated security testing tool for rigorous testing throughout the software development lifecycle and an automated vulnerability detection tool to identify potential vulnerabilities in system logs and network traffic. To optimize feature selection in the vulnerability detection tool, we employ the genetic algorithm (GA). The GA efficiently explores potential feature combinations, selecting the most relevant features to maximize vulnerability detection accuracy while minimizing the number of features. Simulated results validate the effectiveness of our solution in real-world scenarios, with the optimal solution achieving a fitness score of 0.982. By automating security testing and vulnerability detection, our approach enhances the security and reliability of software development. The paper concludes by discussing the implications of our findings and proposes future research directions. Embracing automation in security testing fortifies software applications and safeguards critical data from threats. Our proposed solution offers a promising avenue for advancing automated security testing and vulnerability detection within the DevSecOps paradigm. © 2023 IEEE.

关键词： Automated Security Testing Cybersecurity in Software Development DevSecOps Automation genetic algorithm optimization Vulnerability Detection Tools

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Blade shape optimization of the Savonius wind turbine using a genetic algorithm

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APPLIED ENERGY 2018年 213卷 148-157页

作者： Chan, C. M. Bai, H. L. He, D. Q. Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China

The Savonius wind turbine is one of the best candidates for harvesting wind energy in an urban environment, due to unique features such as compactness, simple assembly, low noise level, self-starting ability at low wind speed, and low cost. However, the conventional Savonius wind turbine with semicircular blades has a relatively low power coefficient. This work focuses on optimizing the shape of the blade of the Savonius wind turbine to further improve its power coefficient. An evolutionary-based genetic algorithm (GA) is incorporated into computational fluid dynamics (CFD) simulations, thereby coupling blade geometry definition with mesh generation and fitness function evaluation in an iterative process. Three variable points along the blade cross-section are used to define the geometry of the blade arc, and the objective function of GA is set to maximize the power coefficient. Two-dimensional flow around the wind turbine is modeled by the shear-stress transport (SST) k-omega turbulence model and solved through the finite-volume method in ANSYS Fluent. Three GA optimization runs with different population and genetic operations have been carried out to provide the optimal shape of the blade of the Savonius turbine. Compared to the wind turbine with semicircular blades, the wind turbine with optimal blades and a tip speed ratio (TSR) of 0.8 achieved significant improvement (up to 33%) on the time-averaged power coefficient. In addition, the Savonius turbine with optimal blades outperformed the one with semicircular blades at a wide range of TSR (= 0.6-1.2), suggesting that the Savonius wind turbine with optimal blades has great potential to be applied in the real urban environment. The aerodynamic forces and flow structures pertaining to both wind turbines with optimal and semicircular blades are compared and discussed, to improve our understanding on their underlying mechanisms and to further improve their performance.

关键词： Wind energy Savonius wind turbine/rotor genetic algorithm optimization

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Novel renewable seawater desalination system using hydrogen as energy carrier for self-sustaining community

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DESALINATION 2024年 579卷

作者： Wen, Du Kuo, Po-Chih Aziz, Muhammad Univ Tokyo Dept Mech Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan Univ Tokyo Inst Ind Sci 4-6-1 KomabaMeguro Ku Tokyo 1538505 Japan

This study proposes a novel renewable seawater desalination system in which hydrogen is utilized as an energy carrier to balance the fluctuating electricity supply and demand and provide heat to the desalination system. The proposed system employs a water-hydrogen nexus framework to achieve an efficient operation for providing electricity, potable water, and green hydrogen. The optimal system configuration that considers seasonal variations in renewable energy production is determined by using a heuristic algorithm. It can sustain flexible and standalone operations with less curtailment. The results demonstrate that the system has an average efficiency of 38.9 %, which is higher than the results reported in previous studies. The daily differences in system efficiency are primarily caused by the solar and water electrolysis systems. Curtailment rates of 5 % to 14 % during the operation are owing to the energy storage capacity and supply-demand balance. A higher utilization rate of water electrolysis and fuel cell systems is beneficial for profitability. However, the present utilization rate ranges from 40 %-60 % for water electrolysis systems and <20% for fuel cell systems. Thus, an integrated net-zero system of water electrolysis and fuel cells is proposed, which can be used as a substitute for traditional fossil-based desalination plants.

关键词： Renewable energy Desalination Hydrogen utilization Energy storage Power -to -hydrogen genetic algorithm optimization

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Optimizing a Hybrid Controller for Automotive Active Suspension System by Using genetic algorithms With Two High Level Parameters

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IEEE ACCESS 2024年 12卷 172451-172464页

作者： Tan, Vu Van Tu, Do Trong Vinh, Nguyen Van Thang, Pham Tat Mihaly, Andras Gaspar, Peter Univ Transport & Commun Fac Mech Engn Dept Automot Mech Engn Hanoi 100000 Vietnam Elect Power Univ Fac Mech Automot & Civil Engn Hanoi 100000 Vietnam Hungarian Res Network Inst Comp Sci & Control Syst & Control Lab H-1111 Budapest Hungary

This article presents the in-depth research results concerning the actively controlled suspension system following the Hybrid Active Suspension System (HASS) in vehicles. Initially, detailed introductions are provided for the controller and actuator of this suspension system model. Afterward, the HASS model is proposed for application to the Active Suspension System with integrating Skyhook and Groundhook control methods, wherein a coefficient alpha = 0.3, 0.6, 0.9 is utilized to adjust the correlation between these two models. Through the amplitude transfer function, the transition in enhancing ride comfort and road holding criteria in the HASS is demonstrated. Subsequently, the coefficients of the HASS are optimized through the genetic algorithm optimization with the parameter beta =0.1, 0.5, 0.9, aiming to enhance ride comfort and road holding corresponding to each value of alpha. The simulation and evaluation results utilizing the HASS in both frequency and time domains demonstrate significant improvements in ride comfort and road holding criteria compared to passive suspension systems. Specifically, the ride comfort has improved from 60% to 80%, while the road holding has improved from 10% to 15%. These findings facilitate the development of applications for the controlled suspension system, allowing adaptation to diverse real-world conditions at varying levels.

关键词： Vehicle dynamics and control active suspension system hybrid active suspension system Skyhook model Groundhook model genetic algorithm optimization suspension system Vehicle dynamics and control active suspension system hybrid active suspension system Skyhook model Groundhook model genetic algorithm optimization suspension system

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Fuzzy-decision tree modeling for H2S production management in an industrial-scale anaerobic digestion process

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BIOCHEMICAL ENGINEERING JOURNAL 2024年 208卷

作者： Rahimieh, Armin Mehriar, Milad Zamir, Seyed Morteza Nosrati, Mohsen Tarbiat Modares Univ Fac Chem Engn Biochem Engn Dept Tehran Iran

H2S is a byproduct in anaerobic digesters (ADs), causing significant challenges due to its negative impact on biogas quality and equipment corrosion. As a solution, previous researchers developed complex mechanistic models that are computationally expensive due to solving equations with many variables. These models were only tested within normal H2S ranges. This research introduces a novel hybrid fuzzy-decision tree (F-DT) model to predict H2S production from primary/secondary sludge ratio (P/S), dry solids (DS), and volatile solid (VS) reduction parameters in the Qom wastewater treatment plant in Iran. Notably, the H2S concentrations in this facility consistently surpass the acceptable thresholds reaching levels as high as 6500 ppm, in contrast to the designed accepted limit of 3000 ppm. The process involved collecting and pre-processing data, creating a decision tree classifier for rule generation, developing a fuzzy model suited for complex systems like AD, and optimizing it with a genetic algorithm. The analysis of the final model revealed that the P/S ratio significantly influences elevated H2S levels, indicating that maintaining a P/S ratio > 1.2 holds promise as an effective strategy. The model is both simple and interpretable, with decent accuracy in predicting H2S compared to previous models. This study not only introduces a useful method for optimizing industrial-scale ADs, but also aims to uncover the reasons behind high H2S levels.

关键词： Anaerobic digestion Decision tree Fuzzy logic genetic algorithm optimization Hybrid modeling Hydrogen sulfide

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Comparative analysis: Exergetic and economic assessment of LNG cold energy power generation systems with different cold utilization methods

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 54卷

作者： Cai, Dongxu Zhao, Yiming Wang, Yingguang Liu, Haoshuang Liang, Yong Hu, Dapeng Dalian Univ Technol Dept Chem Machinery Dalian 116012 Peoples R China PipeChina Engn Technol Innovat Co Ltd Tianjin 300450 Peoples R China

Harnessing the cold energy inherent in LNG transportation processes can significantly mitigate energy wastage. Employing an innovative incremental analysis methodology, this study scrutinizes six LNG cold energy power generation systems, featuring a newly proposed parallel and cascade combined cycle (PAC) system. A novel approach that setting the minimum pressure within the systems higher than atmospheric levels has been adopted, for the optimal working fluid selection. The net power output (Wnet) of the direct expansion (DC) system registers at -129.51 kW, while both the Single-Stage Organic Rankine Cycle (SORC) and Combined Cycle (CC) systems yield Wnet of 2868.46 kW and 3081.46 kW with R32 as working fluid. Despite the lower available exergy extraction of the working fluid, the CC system outperforms due to its superior efficiency in converting pressure exergy into power output. Sensitivity analysis suggests the optimal Tcon in CC is limited by the normal boiling point temperature (NBPT) of working fluid, while that in SORC remains unrestricted. The maximum Wnet of two-stage Parallel Combined Cycle (PCC) and Cascade Combined Cycle (CCC) can reach 3291.65 kW and 4268.78 with the optimal working fluid combinations R32 + propane, and ethane + propane. The reason Wnet of CCC outperforms is the cascade utilization of LNG cold exergy enables the working fluid in its second stage obtains significantly 9.49 times higher amount of exergy compared to PCC. Through sensitivity analysis, while Tcon1 mostly predominates the performance of PCC, both Tcon1 and Tcon2 exert substantial influence on CCC. For the three-stage PAC, its Wnet can reach the highest 4700.82 kW with ethane + propane + propane. It is because the PAC not only utilizes LNG's cold exergy in a cascaded manner, but also obtains the exergy from working fluid. According to economic analysis, the PAC system exhibits an advantage with the highest annul total net income (ATNI). And the CC emerges as a cost-effective c

关键词： Cold energy power generation genetic algorithm optimization Liquefied natural gas Economic analysis

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Modeling and optimization of biomethane production from solid-state anaerobic co-digestion of organic fraction municipal solid waste and other co-substrates

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 8564-8580页

作者： Saghouri, Mohsen Abdi, Reza Ebrahimi-Nik, Mohammadali Rohani, Abbas Maysami, Mohammadali Univ Tabriz Dept Biosyst Engn Fac Agr Tabriz Iran Ferdowsi Univ Mashhad Dept Biosyst Engn Fac Agr Mashhad Razavi Khorasan Iran

One of the simplest methods for increasing productivity in biogas production is co-digestion. Co-digestion leads to more biogas yield as well as more nutrient bioavailability. This study reports the modeling and optimization of biomethane potential tests from different combinations of organic fraction of municipal solid wastes (OFMSW), cow manure (CM), and municipal sewage sludge (MSS) in solid-state. The simplex-centroid mixture design (SCMD) and artificial neural network (ANN) models were validated with a high correlation to the real data (R-2 = 0.99). Experimental results indicated that the maximum amount of CH4 production of 436 mL CH4/g VS occurs at the identical weight percent of the three substances (33.33%). The maximum methane yield was found to be 445.9 mL CH4/g VS as obtained by the genetic algorithm (GA) optimization process, while 448.5 mL CH4/g VS (448.5 +/- 3.05) of methane was produced experimentally. Therefore, integration of the SCMD and ANN model with the GA optimization is useful in the prediction of biomethane production. This research is the first in providing the best combination of co-treating and co-utilizing OFMSW with CM and MSS in the solid-state for biowaste management.

关键词： Anaerobic digestion artificial neural network modeling genetic algorithm optimization methane production statistical regression model

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optimization of Savonius turbine clusters using an evolutionary based genetic algorithm

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Energy Procedia 2019年 158卷 637-642页

作者： Y. Zheng H.L. Bai C.M. Chan Department of Civil and Environmental Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong KongSAR China

VAWTs benefit from constructive coupling effect that significantly improves efficiency. This work aims at optimizing the configuration of turbine clusters with three Savonius turbines. To maximize the aggregate power output of a turbine cluster, two-dimensional (2D) computational fluid dynamics (CFD) is utilized, along with an evolutionary based genetic algorithm (GA). Four scenarios are investigated considering different combinations of rotational directions. For the optimal cluster obtained, a highest 37% increase in averaged power coefficient ( C P ¯ )over an isolated Savonius turbine is achieved at Tip Speed Ratio ( TSR ) = 0.8. The substantial increase in efficiency for turbines in the optimal cluster is essentially due to constructive coupling in the flow field. Two major effective coupling mechanisms are proposed based on flow structures to explain the observed performance increase. Furthermore, a method to expand the optimal cluster into compact wind farms is proposed. It is verified that for a compact Savonis-type VAWT wind farm that consists of 12 turbines, a 30% increase in C P ¯ is obtained, showing the practicality of constructing efficient VAWT wind farms in areas with limited land.

关键词： Wind energy Vertical Axis Wind Turbines Savonius turbine genetic algorithm optimization

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