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design optimization and similarity analysis of non-azeotropic organic radial inflow turbine for OTEC power and water cogeneration

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APPLIED THERMAL ENGINEERING 2025年 270卷

作者： Feng, Xin He, Guanglin Ma, Qingfen Li, Jingru Wu, Zhongye Lu, Hui Wang, Chengpeng Wang, Shenghui Hainan Univ Coll Mech & Elect Engn Haikou 570228 Hainan Peoples R China Chinese Acad Trop Agr Sci Inst Environm & Plant Protect Haikou 571101 Hainan Peoples R China MNR Inst Seawater Desalinat & Multipurpose Utilizat 55 Hanghai RD Tianjin 300192 Peoples R China

This paper builds upon a previously proposed novel OTEC power and water cogeneration system, focusing on optimizing and verifying the performance of a radial inflow turbine (RIT) using the non-azeotropic R1224yd(Z)/ R1243zf mixture as the working fluid. The study primarily aims to enhance the circumferential efficiency of the RIT while avoiding local optima in the optimization process. Three optimization algorithms-Particle Swarm optimization (PSO), Genetic Algorithm (GA), and Grey Wolf optimization (GWO)-were employed to identify key design parameters, with PSO yielding a maximum circumferential efficiency of 94.13 %. CFD simulations of the preliminary design showed a strong correlation with the optimization results, with parameter errors within 5 % at the design point. The off-design performance of the RIT was also evaluated under varying operational conditions, demonstrating excellent adaptability and minimal performance degradation. To validate the design and simulation, a similarity method for constructing an air-model turbine was proposed. This method successfully addressed challenges related to the OTEC RIT's unique operating conditions, confirming the similarity between the ORC RIT and the model turbine. The optimization algorithms and similarity approach provide valuable insights into the design and performance evaluation of organic working fluid turbines for OTEC systems, providing a basis for the experimental study of the similar air turbine.

关键词： Ocean Thermal Energy Conversion Radial Inflow Turbine Non-Azeotropic Organic Working Fluid design optimization Similarity Analysis

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design optimization of a lightweight secondary mirror assembly for a 1-m class ground telescope for satellite laser ranging

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INTERNATIONAL JOURNAL OF OPTOMECHATRONICS 2025年第1期19卷

作者： Lee, Jaehyun Park, Kisoo Son, Eui Seung Jeong, Suseong Kim, Do-Won Oh, Han-Gyol Kang, Pilseong Rhee, Hyug-Gyo Korea Res Inst Stand & Sci KRISS Daejeon South Korea Def Rapid Acquisit Technol Res Inst Seoul South Korea Hanwha Syst Seongnam South Korea Univ Sci & Technol Dept Precis Measurement Daejeon South Korea

Large-sized ground telescopes have been developed to meet the high demands for opto-mechanical imaging systems in space and military applications. In line with these advancements, we developed a 1-m class ground telescope for astronomical imaging and satellite laser ranging (SLR). In a ground telescope, mirror deflection is mainly induced by gravity and temperature change. In particular, the gravity vector varies depending on the pointing direction of a telescope, so the surface deformations of the mirrors due to self-gravity need to be managed in different observation directions. This study introduces a mechanical design for an optical tube assembly (OTA) and suggests an optimized design for the secondary mirror (M2) assembly. For a kinematic positioning of the M2, its lightweight was achieved based on the partially open-back structure with hexagonal pocket cells. Then, we optimized the flexure mount design with a bipod structure to minimize the surface errors (SFEs) of the M2 in both the horizontal and vertical pointing directions. Additionally, we simulated the deflections of the primary mirror (M1) and M2 assemblies when installed on the telescope. Based on our design, the M2 was fabricated and processed, and we demonstrated its assembly process and surface quality test.

关键词： design optimization optical telescope mirror lightweight optical alignment satellite laser ranging

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design optimization for Minimizing Performance Deviations of Complex Vehicle Door Systems Using Virtual Manufacturing Big Data and Axiomatic design

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2025年 1-25页

作者： Lee, Sang Hyun Yoon, Bumyong Kwon, Hyerin Seo, Chang Man Suhr, Jonghwan Hyundai Motor Co R&D 150 HyundaiYeonguso Ro Hwaseong 18280 South Korea Sungkyunkwan Univ Sch Mech Engn Suwon 16419 South Korea Sungkyunkwan Univ Ctr Composite Mat & Concurrent Design Suwon 16419 South Korea DRB 39 Sanmakgongdanbuk 2 Gil Yangsan 50567 South Korea

This study introduces an innovative framework aimed at minimizing performance deviations in complex vehicle door systems by leveraging the principles of axiomatic design and virtual manufacturing big data. Utilizing the independence axiom of axiomatic design theory, an optimal design sequence is established for a vehicle door system. Analytical models for door opening and closing are developed, and surrogate models are constructed for weatherstrips in conjunction with machine learning techniques. Monte Carlo simulations are performed, enabling the generation of virtual manufacturing data and thereby facilitating a comprehensive analysis. The application of genetic algorithms with information content as the objective function can minimize vehicle performance variability, offering a promising approach for design optimization. This methodology not only demonstrates the potential for significantly reducing performance deviations but also highlights the effectiveness of integrating computational techniques with axiomatic design principles to enhance system predictability and quality control.

关键词： Axiomatic design Machine learning Automotive door Closure design optimization Genetic algorithm

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design optimization of a 3-PUU Parallel Machine for Friction Stir Welding Robots

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IEEE ACCESS 2025年 13卷 23888-23901页

作者： Marliana, Eka Nurahmi, Latifah Wahjudi, Arif Batan, I. Made Londen Wei, Guowu Inst Teknol Sepuluh Nopember Dept Mech Engn Surabaya 60111 Indonesia Univ 17 Agustus 1945 Surabaya Dept Mech Engn Surabaya 60118 Indonesia Univ Salford Sch Sci Engn & Environm Salford M5 4WT England

Friction Stir Welding (FSW) has recently emerged as an effective solid-state joining technique for welding high-strength aluminum alloys and light metals, particularly in the railway industry. However, a limitation of using FSW in this industry is the restricted workspace available. To address this issue, a 3-PUU (Prismatic and Universal Joints) parallel machine with 3-DoF (Degrees of Freedom) translational movement is utilized to support the FSW robot's tool head, allowing the robot to operate over a larger horizontal workspace. This paper focuses on the design and optimization of a 3-PUU parallel machine to achieve an optimal configuration based on performance indices, specifically the Local Conditioning Index (LCI) and the dynamic isotropy index ( d(1) ). Initially, a design space atlas was created, mapping both LCI and d(1) were identified. A Genetic Algorithm (GA) was then used to determine a single optimal design within this region. A cuboid workspace was defined for this optimal design, and two test trajectories were generated for the robot to follow. A prototype of the optimal design was built, and a series of experiments were conducted to verify and validate the kinematic and dynamic models within the defined cuboid workspace, which provides foundations for further industrial applications of the proposed machine in FSW.

关键词： Parallel machines Robots Kinematics Welding Manipulators Manipulator dynamics Service robots Rigidity optimization Jacobian matrices Friction stir welding parallel machine design optimization performance indices genetic algorithm

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design optimization of polymer composites for lower suspension arms of automotive vehicles

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INTERNATIONAL JOURNAL OF INTERACTIVE design AND MANUFACTURING - IJIDEM 2025年第2期19卷 1013-1023页

作者： Hussain, Farhaan Manikanta, K. S. B. Ahmed, Naved Wasim Vinoth, A. Roy, Sandipan Datta, Shubhabrata SRM Inst Sci & Technol Coll Engn & Technol Dept Mech Engn Kattankulathur 603203 Tamil Nadu India

Lower suspension arms deal with the movement of the wheel during knock, turning, and breaking. To replace the metallic suspension arms by laminated polymer composites, suspension arms are designed in this work. The thickness and the fiber orientations for different layers in the laminates are optimized to achieve improved performance. The suspension arms of fiber reinforced composite are designed and analyzed through finite element analysis (FEA) method. The simulated data generated from the FEA is used for developing metamodels by artificial neural network (ANN), which in turn is used as the objective functions or the optimization process using genetic algorithm (GA). The objective of the optimization process is to reduce the stress concentration through better stress distribution and thus increasing the service life. The design optimization problem is handled in multi-objective optimization mode and the evolved Pareto solutions lead to a guideline for developing automotive suspension arm using composites. The FEA based validation of the optimum solutions show variation of the result well within acceptable limit.

关键词： Lower suspension arm Stress analysis design optimization Finite element analysis Metamodeling Artificial neural network Genetic algorithm Pareto front

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design optimization and performance characteristics of a hemostatic ultrasonic scalpel

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INTERNATIONAL JOURNAL OF INTERACTIVE design AND MANUFACTURING - IJIDEM 2025年 1-12页

作者： Jahromi, Mohammad Saber Abedini, Rezvan Iran Univ Sci & Technol Sch Mech Engn POB 15875-4413 Tehran Iran

In a hemostatic ultrasonic scalpel, high-frequency ultrasonic vibrations generate localized heat, leading to simultaneous cutting and coagulation of the area in contact with the tissue. Optimizing the design parameters results in more precise cutting and better tissue coagulation. This research involves optimization of the design and evaluation of the performance of an ultrasonic surgical scalpel. The design was carried out using an electromechanical equivalent circuit method and modal and harmonic analysis in finite element software, along with optimization of geometric parameters using response surface methodology. The objective of optimization is to achieve a resonance frequency of 55.5 kHz in the longitudinal mode and to optimize the vibration amplitudes in the important areas of the ultrasonic scalpel. To evaluate the scalpel, impedance analyzing tests, displacement amplitude measurement of the horn tip, and experimentation on a sheep's vessel have been conducted. Cutting and sealing the vessel using the ultrasonic scalpel demonstrates that the frequency and amplitude (40 mu m) of the surgical tool are suitable for vessel cutting and sealing. A comparison of simulation results and experimental testing indicates an error of less than 4 percent in the simulations. Through modeling and optimization, it became possible to achieve an efficient ultrasonic scalpel and conduct a precise evaluation of its characteristics.

关键词： Ultrasonic scalpel Finite element method (FEM) design optimization Electromechanical equivalent circuit

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Hysteresis performance and design optimization of rubber concrete anti-collision layer of concrete bridge piers

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STRUCTURAL CONCRETE 2025年

作者： Yang, Shuzhen Zou, Zhongliang Wang, Mansheng Song, Bo Univ Sci & Technol Beijing Sch Future Cities Beijing Peoples R China Beijing Int Cooperat Base Sci & Technol Aseism Res Rail Transit Engn Strong Mot Area Beijing Peoples R China Beijing Sci & Technol Inst Housing & Urban Rural D Beijing Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China

Rubber concrete, with characteristics of light weight, remarkable toughness, impact resistance, and durability, has been gaining increasing application in engineering. This study explored the mechanical properties of rubber concrete, analyzed its performance as anti-collision layers, and optimized the layers' thickness. First of all, static and hysteresis experiments were implemented to obtain the compressive strength, elastic modulus, and energy dissipation factor of rubber concrete. Rubber concrete specimens were made by replacing the fine aggregate of C30 concrete with rubber particles of equivalent volume. The rubber particles were categorized into two size groups: 1.5-3.0 mm and 3.0-5.0 mm. Within each size category, the rubber particles were incorporated into the concrete fine aggregates at replacement volumes of 5%, 10%, and 20%. Then truck-bridge collision models with different thicknesses of anti-collision layers were conducted to assess the metric variations in anti-collision performance, including impact force, top displacement of the bridge abutment, Von Mises stress, and damping dissipation energy. Finally, a recommendation on cost-effective thickness for the anti-collision layer was given by the indicator weights generated from the triangular fuzzy analytic hierarchy process and the metric evaluation scores of anti-collision performance. It is found that the compressive strength and elastic modulus of rubber concrete decrease with the increase of rubber particle size and rubber content. The loss factor increases with the increase of rubber content and the amplitude of the loading force. With the increase of rubber concrete anti-collision layers from 0 to 250 mm, the peak impact force and peak Mises stress decrease, while the peak displacement at the top of the bridge abutment and peak damping dissipation energy decrease and then increase at the thickness of 250 mm. For the collision scenario in this study, the cost-effective thickness of the rubber con

关键词： design optimization hysteresis performance rubber concrete anti-collision layer

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Modeling and design optimization of the performance of stone mastic asphalt (SMA) mixtures containing Sasobit and GTR using the response surface methodology

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CONSTRUCTION AND BUILDING MATERIALS 2025年 471卷

作者： Amini, Amir Riahi, Mohammad Taj Mir Hasanpoori, Maryam Islamic Azad Univ Dept Civil Engn Najafabad Branch Najafabad Iran Univ Isfahan Sch Civil Engn Esfahan Iran

In recent years, the use of recyclable materials like Ground Tire Rubber (GTR) to modify binder and enhance the performance of asphalt mixtures has gained significant attention. A primary concern with using GTR is the increased production temperature and higher binder content required, which often leads to the necessity of incorporating a viscosity-reducing agent such as warm mix additives (WMA). By utilizing WMA materials, it is possible to lower both the mixing temperature and binder consumption, particularly in rubberized asphalt. This study aimed to optimize the design of warm mix rubberized asphalt to reduce the proportions of Sasobit and optimal binder content (OBC), as well as the mixing and compaction temperatures, while simultaneously maximizing asphalt performance. To achieve this, response surface methodology (RSM) was employed to create the necessary models for design optimization and performance prediction. The independent variables for the design included GTR content, Sasobit content, OBC, and mixing temperature. To determine the best mix design, various binder performance tests were conducted, including evaluations at high and low temperatures, MSCR, and LAS. Additionally, the technical characteristics of the asphalt mixtures were assessed through tests for resilient modulus, indirect tensile strength, tensile strength ratio (TSR), dynamic creep, and four-point fatigue. Overall, the findings indicated that while rubberized asphalt binder (RAB) significantly improves rutting and fatigue resistance, there are limitations to its practical application in projects. Therefore, it is recommended to use Sasobit as a complementary additive to enhance both the technical and functional properties of asphalt, ensuring suitable implementation conditions. The proposed models for the output variables can predict performance with a high degree of confidence. Analysis of variance revealed that the prediction models exhibited a R2 > 0.80, an AP> 4, and a P-value< 0.05,

关键词： Stone mastic asphalt Ground tire rubber Sasobit Performance prediction design optimization Response surface methodology

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AI-driven design optimization for sustainable buildings: A systematic review

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ENERGY AND BUILDINGS 2025年 332卷

作者： Manmatharasan, Piragash Bitsuamlak, Girma Grolinger, Katarina Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada Western Univ Dept Civil & Environm Engn London ON N6A 5B9 Canada

Buildings are major contributors to global carbon emissions, accounting for a substantial portion of energy consumption and environmental impact. This situation presents a critical opportunity for energy conservation through strategic interventions in both building design and operational phases. Artificial Intelligence (AI) has emerged as a transformative approach in this context, enhancing the efficiency and precision of energy management efforts. In the operational phase, AI is extensively utilized as smart controllers for Heating, Ventilation, and Air Conditioning (HVAC) systems and passive energy gains, as well as for fault detection. In the design phase, AI is pivotal as a surrogate model, enabling rapid and accurate evaluation of design options and allowing designers to optimize building performance with minimal computational resources. As the early-stage optimization is more cost-effective than post-construction modifications, design phase optimization has a great potential. Consequently, this paper examines recent advancements in surrogate-assisted design optimization for sustainable buildings, providing a comprehensive overview of the entire optimization process, from data preparation and surrogate model training to final optimization. The review categorizes studies based on experimental approaches and methodologies, identifying trends, gaps, and opportunities in the field. Notably, it highlights how modern AI techniques can incorporate previously unexplored dimensions into surrogate-assisted optimization, broadening the scope and potential of surrogate models. Therefore, this study provides guidance for future research and practical applications of AI-driven strategies in sustainable building practices.

关键词： Machine learning Building sustainability design optimization Surrogate model

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An advanced physics-informed neural operator for comprehensive design optimization of highly-nonlinear systems: An aerospace composites processing case study

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 142卷

作者： Ramezankhani, Milad Deodhar, Anirudh Parekh, Rishi Yash Birru, Dagnachew Quantiphi Appl Res Marlborough MA 01752 USA

Deep Operator Networks (DeepONet) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations (PDEs), enhancing the generalization of traditional neural networks. However, for highly nonlinear real-world applications like aerospace composites processing, existing models often fail to capture underlying solutions accurately and are typically limited to single input functions, constraining rapid process design development. To overcome these limitations this study introduces an advanced physics-informed DeepONet tailored for highly nonlinear systems with multiple input functions. While previous works have primarily focused on single, isolated improvements, our framework is equipped with a comprehensive suite of architectural enhancements including nonlinear decoders, and effective training strategies such as curriculum learning and domain decomposition. The proposed model effectively manages high-dimensional design spaces with substantially enhanced accuracy, surpassing the standard physics-informed DeepONet by two orders of magnitude in training loss and achieving a 30% reduction in prediction error. Its zero-shot prediction capability across abroad design space makes it a powerful tool for accelerating composites process design and optimization, with potential applications in other engineering fields characterized by strong nonlinearity.

关键词： Neural operators Physics-informed deep operator networks Aerospace composites processing design optimization

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