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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Duranti, Stefano Jones, Dominic Zhang, Yongzhe Goldin, Graham Siemens Digital Ind London England Siemens Digital Ind Melville NY USA Siemens Digital Ind Lebanon NH 03766 USA

ISBN: (纸本)9780791887943

The adjoint method efficiently computes the sensitivity of a global output objective function, such as a combustor outlet temperature uniformity or NOx emission, with respect to a large number of input design variables, such as the centroids of the wall boundary faces. This sensitivity gradient can be used in an optimization algorithm, in particular for shape or topology design to extremize the objective function. In this work, the adjoint model in Simcenter STAR-CCM+ is extended to the Flamelet Generated Manifold (FGM) combustion model. The discrete adjoint is solved by differentiating the linearized flow solver using Automatic Differentiation, transposing these operations, and executing the code in the reverse order. Since the code is complex with a large number of interacting models, a careful verification is required to ensure that the important parts of the code are differentiated properly, and the order of code execution is correct. A global sensitivity for a simple mixing layer flame is compared for forward derivatives ( tangent mode), reverse derivatives (adjoint mode) and finite differences. Despite vastly different code path execution for adjoint, tangent and finite-difference, the close agreement of various objective function calculations verifies the correct implementation of the adjoint and tangent code.

关键词： Temperature uniformity Shapes Emissions Combustion chambers Flow (Dynamics) Design Topology Flames Nitrogen oxides optimization algorithms Flamelet generated manifold Combustion

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Improved Multi-Objective Coati optimization Algorithm for Optimizing Energy Efficiency and Thermal Comfort in Chilled Water Systems

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Journal of Thermal Science and Engineering Applications 2025年第4期17卷 041009页

作者： Feng, Zengxi Liu, Xuefeng Zhang, Xian Lu, Shuai Wang, Bo Liu, Li Quan, Wei An, Jianhu Wang, Chang Kang, Limin School of Building Services Science and Engineering Xi’an University of Architecture and Technology Xi'an Shaanxi 710055 China State Grid Xi’an Electric Power Supply Company Xi'an Shaanxi 710032 China College of Information and Control Engineering Xi’an University of Architecture and Technology Xi'an Shaanxi 710311 China

As the main energy consumption part of the central air-conditioning systems, the energy saving of the chilled water system is particularly crucial. This system realizes heat exchange with indoor air by delivering chilled water to air-conditioning units, effectively regulating indoor temperature and humidity to ensure thermal comfort. In this article, an improved multi-objective coati optimization algorithm (IMOCOA) is used to optimize the operating parameters and thermal comfort environment parameters of chilled water systems to improve thermal comfort and reduce energy consumption. The algorithm introduces chaotic mapping to enhance search diversity, balances global and local search capabilities through Levy flight and Gauss variation strategies, and uses location greedy choices to help coatis jump out of local optima. To verify the optimization effect of IMOCOA, a multi-objective optimization model was established, combining the energy consumption model of the chilled water system and the simplified thermal comfort model. Key parameters, including chilled water supply temperature, pump speed ratio, indoor temperature, and relative humidity, are optimized. The simulation results from the experiments show that the average energy-saving rate of the chilled water system using IMOCOA is 7.8% and thermal comfort is improved by 19.6%. Compared to other optimization algorithms, this method demonstrates a better optimization effect.

关键词： multi-objective coati optimization algorithm optimization algorithms Energy consumption energy efficiency Water algorithms Flight indoor thermal comfort Modeling Temperature chilled water system parameter optimization optimization Pareto optimization energy systems thermal systems Air conditioning Pumps Energy efficiency

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Application of Control algorithms for Battery Scheduling in Grid-Connected Energy Prosumers 22

Application of Control Algorithms for Battery Scheduling in ...

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IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

作者： Becchi, Lorenzo Bindi, Marco Intravaia, Matteo Grasso, Francesco Pasqui, Mattia Carcasci, Carlo Univ Florence Dept Informat Engn Florence Italy Univ Florence Dept Ind Engn Florence Italy

ISBN: (纸本)9798350387032;9798350387025

This paper proposes an innovative approach in managing prosumer batteries using a rule-based control algorithm. The main information used in this work to develop actions on a Battery Energy Storage System (BESS) are the price of energy on the day-ahead electricity market, the prosumer consumption/production forecasts and the effect of charging/discharging operations on battery degradation. In order to evaluate the performance of the rule-based approach, a comparison with other optimization algorithms commonly used in the literature is proposed. Performance is evaluated in terms of economic gain and energy exchanged between prosumer and network. The rule-based algorithm proposed in this paper exploits a neural predictor to forecast load and production forecasts and processes the trend of the electricity market in order to find the best management of the BESS. The results obtained show the best performance of the proposed algorithm compared to the most common optimization methods and the possibility of easily extending this logic to control a large number of users. This is possible also thanks to the extremely low computational cost of the proposed approach, around twenty times faster than a Mixed-Integer Linear Programming (MILP) algorithm. This fact is very interesting regarding the diffusion of modern Renewable Energy Communities (RECs).

关键词： Renewable Energy Communities Energy Management System Battery Energy Storage System optimization algorithms

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Hyperspectral Unmixing With Row-Sparsity Enhancement: A Difference-of-Convex Approach

Hyperspectral Unmixing With Row-Sparsity Enhancement: A Diff...

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2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference

作者： Naganuma, Kazuki Ono, Shunsuke Tokyo Inst Technol Yokohama Kanagawa Japan

ISBN: (纸本)9798350367331;9798350367348

This paper proposes a new hyperspectral unmixing method that enhances the row sparsity of an abundance matrix via a difference-of-convex (DC) approach. One promising unmixing technique is to promote the row sparsity by designing and solving optimization problems. However, existing methods are unable to both fully enhance the row sparsity and guarantee the convergence of their optimization algorithms, resulting in performance degradation. To address this limitation, we introduce a DC approach to unmixing. First, we design a DC function that promotes the row sparsity while preserving the energies of some row vectors of an abundance matrix. Then, incorporating the DC function, we formulate unmixing as a constrained DC optimization problem. In addition, we develop an algorithm to solve the optimization problem based on the proximal linearized DC algorithm and the preconditioned primal-dual splitting algorithm and guarantee its convergence to a critical point of the problem. Experiments on synthetic and real HS images show that our method is superior to existing methods.

关键词： optimization algorithms

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Minimization of Material Waste Through Maintenance Interval optimization in Transport Systems

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APPLIED SCIENCES-BASEL 2024年第23期14卷 11318页

作者： Lorenc, Augustyn Kuznar, Malgorzata Cracow Univ Technol Fac Mech Engn PL-31864 Krakow Poland

The optimization of maintenance intervals is crucial for enhancing efficiency, sustainability, and cost-effectiveness in transport operations. This paper presents a method for optimizing maintenance intervals for vehicles in various modes of transport, focusing on minimizing downtime due to repairs and maintenance. By integrating advanced technologies like artificial intelligence (AI) and the Internet of Things (IoT), maintenance intervals are dynamically adjusted using real-time data, resulting in better resource utilization and reduced operational costs. The key findings of this research indicate significant reductions in downtime and maintenance costs, leading to improved efficiency and sustainability across transport modes. Although the case study is based on railway vehicles, the approach is applicable to road, maritime, and air transport as well. By leveraging optimization algorithms, such as machine learning, this solution predicts optimal maintenance timing, thereby reducing resource consumption and improving operational efficiency. The case study on pantograph maintenance demonstrates significant financial savings and reduced waste. This research highlights the benefits of maintenance optimization for sustainability and efficiency across the entire transport sector.

关键词： transport engineering rail networks train circuit optimization optimization algorithms waste minimization sustainable transport

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Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning 5

Optimizing Variational Quantum Circuits Using Metaheuristic ...

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2024 International Conference on Quantum Computing and Engineering

作者： Koelle, Michael Seidl, Daniel Zorn, Maximilian Altmann, Philipp Stein, Jonas Gabor, Thomas Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9798331541378

Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further validation. QRL faces challenges like flat solution landscapes, where traditional gradient -based methods are inefficient, necessitating the use of gradient-free algorithms. This work explores the integration of metaheuristic algorithms - Particle Swarm optimization, Ant Colony optimization, Tabu Search, Genetic Algorithm, Simulated Annealing, and Harmony Search - into QRL. These algorithms provide flexibility and efficiency in parameter optimization. Evaluations in 5 x 5 MiniGrid Reinforcement Learning environments show that, all algorithms yield near-optimal results, with Simulated Annealing and Particle Swarm optimization performing best. In the Cart Pole environment, Simulated Annealing, Genetic algorithms, and Particle Swarm optimization achieve optimal results, while the others perform slightly better than random action selection. These findings demonstrate the potential of Particle Swarm optimization and Simulated Annealing for efficient QRL learning, emphasizing the need for careful algorithm selection and adaptation.

关键词： Metaheuristics Variational Quantum Circuits optimization algorithms Quantum Reinforcement Learning

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A New Version of Energy-Efficient optimization Protocol Using ICMA-PSOGA Algorithm in Wireless Sensor Network

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SN Computer Science 2022年第5期3卷 353页

作者： Rambabu, Ch Prasad, V.V.K.D.V. Prasad, K. Satya Department of ECE JNTU Kakinada A.P Kakinada India Department of ECE Gudlavalleru Engineering College A.P Gudlavalleru India

In Wireless Sensor Networks (WSNs), the transmission of information through wireless medium is facilitated using nodes which are small and self-organizing and moreover enhances the process. The energy saving and effective utilization of energy are major issues in WSNs. The relationship between the cluster heads and sensor nodes was only considered in previous cluster-based routing protocols by ignoring the cost differences among them. The residual nodes, which are not considered as cluster members, may exist in certain existing clustering protocols. The network lifetime might be reduced by these residual nodes. Clustering is a proven technique for energy optimization. However, most of the clustering algorithms failed to address the routing overhead and the energy consumption rate between the CH nodes and the SINK node. Here, we introduce a new improved energy-efficient intra-cluster routing technique using mobile agents to reduce communication overhead and an optimal positioning strategy of SINK node using PSOGA for energy optimization. A novel framework is proposed in this method, in which an effective clustering mechanism using Intra Mobile Agents (IN-MA) is present. Moreover, on the basis of the network structure, an optimal position is determined for the sink by introducing a Particle Swarm optimization-Genetic Algorithm (PSO-GA)-based location estimation algorithm. The data are collected from the members and delivered to the CHs using Intra Mobile Agents. The PSO-GA algorithm estimates the optimal position of a sink. Based on the experimental results, the better outcomes are observed using the proposed method when compared to the previous techniques. The parameters like lifetime of a network, consumption of energy, and throughput rate are considered mainly to analyze the efficiency of the proposed energy-efficient strategy. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Energy consumption Energy-efficient routing Intraclustering Mobile agent Network lifetime optimization algorithms Wireless sensor network

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New algorithms for Computing Sibson Capacity and Arimoto Capacity

New Algorithms for Computing Sibson Capacity and Arimoto Cap...

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IEEE International Symposium on Information Theory (ISIT)

作者： Kamatsuka, Akira Ishikawa, Yuki Kazama, Koki Yoshida, Takahiro Shonan Inst Technol Fujisawa Kanagawa Japan Univ Electrocommun Tokyo Japan Nihon Univ Tokyo Japan

ISBN: (纸本)9798350382853;9798350382846

The Sibson and Arimoto capacity, which are based on the Sibson and Arimoto mutual information (MI) of order a, respectively, are well-known generalizations of the channel capacity C. In this study, we derive novel alternating optimization algorithms for computing these capacities by providing new variational characterizations of the Sibson and Arimoto MI. Moreover, we prove that all iterative algorithms for computing these capacities are equivalent under appropriate conditions imposed on their initial distributions. A full version with all proofs is accessible at: https:// ***/ abs/ 2401.14241 [1]

关键词： optimization algorithms

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Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback 41

Near-Optimal Regret in Linear MDPs with Aggregate Bandit Fee...

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41st International Conference on Machine Learning (ICML)

作者： Cassel, Asaf Luo, Haipeng Rosenberg, Aviv Sotnikov, Dmitry Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Univ Southern Calif Los Angeles CA USA Amazon Sci Seattle WA USA Google Res Mountain View CA USA

In many real-world applications, it is hard to provide a reward signal in each step of a Reinforcement Learning (RL) process and more natural to give feedback when an episode ends. To this end, we study the recently proposed model of RL with Aggregate Bandit Feedback (RL-ABF), where the agent only observes the sum of rewards at the end of an episode instead of each reward individually. Prior work studied RL-ABF only in tabular settings, where the number of states is assumed to be small. In this paper, we extend ABF to linear function approximation and develop two efficient algorithms with near-optimal regret guarantees: a value-based optimistic algorithm built on a new randomization technique with a Q-functions ensemble, and a policy optimization algorithm that uses a novel hedging scheme over the ensemble.

关键词： optimization algorithms

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Achieving green mobility: Multi-objective optimization for sustainable electric vehicle charging

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ENERGY STRATEGY REVIEWS 2024年 53卷

作者： Barakat, Shimaa Osman, Ahmed I. Tag-Eldin, Elsayed Telba, Ahmad A. Mageed, Hala M. Abdel Samy, M. M. Beni Suef Univ Fac Engn Dept Elect Engn Bani Suwayf Egypt Queens Univ Belfast Sch Chem & Chem Engn Belfast BT9 5AG North Ireland Future Univ Egypt Fac Engn & Technol New Cairo 11835 Egypt King Saud Univ Coll Engn Elect Engn Dept POB 800 Riyadh 11421 Saudi Arabia Natl Inst Stand NIS High Voltage Dept Giza 12211 Egypt

This study optimizes and evaluates a Photovoltaic-Wind-Battery/Electric Vehicle Charging Station (PVWB/ EVCS) system using four Multi-Objective optimization (MOO) techniques: MOPSO, NSGAII, NSGAIII, and MOEA/D. The main goals are to minimize the Total Net Present Cost (TNPC) and Loss of Power Supply Probability (LPSP) of the system, which are crucial for sustainable electric vehicle charging. The study analyzes the economic, operational, and sustainability aspects of the optimized system and compares it with HOMER software. The results show that NSGA-II is the best MOO technique for this problem, as it has the best performance and robustness. The Discounted Cash Flow analysis confirms the economic feasibility and sustainability of the optimized system over its lifetime. The technical analysis demonstrates the system's ability to use renewable energy from solar and wind sources, along with efficient energy storage and distribution. The study also conducts a sensitivity analysis to investigate the effects of changes in load, irradiance, wind speed, and component costs on the system performance. The findings reveal the system's resilience and adaptability under different scenarios, thus enhancing its suitability for renewable energy generation and electric vehicle charging. The study showed that the optimized PVWB/EVCS system is a promising solution for reducing reliance on non-renewable sources and promoting a more eco-friendly sustainable future.

关键词： Optimal hybrid system Electric vehicle charging stations (EVCS) MOPSO MOEA/D NSGA-II NSGA-III Performance evaluation of multi -objective optimization algorithms HOMER

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