Design Optimization Techniques Research Articles

Dual-Task Optimization Method for Inverse Design of RGB Micro-LED Light Collimator

Abstract: Miniaturized pixel sizes in near-eye digital displays lead to pixel emission patterns with large divergence angles, necessitating efficient beam collimation solutions to improve the light coupling efficiency. Traditional beam collimation optics, such as lenses and cavities, are wavelength-sensitive and cannot simultaneously collimate red (R), green (G), and blue (B) light. In this work, we employed inverse design optimization and finite-difference time-domain (FDTD) simulation techniques to design a collimator comprised of nano-sized photonic structures. To alleviate the challenges of the spatial incoherence nature of micro-LED emission light, we developed a strategy called dual-task optimization. Specifically, the method models light collimation as a dual task of color routing. By optimizing a color router, which routes incident light within a small angular range to different locations based on its spectrum, we simultaneously obtained a beam collimator, which can restrict the output of the light emitted from the routing destination with a small divergence angle. We further evaluated the collimation performance for spatially incoherent RGB micro-LED light in an FDTD using a multiple-dipole simulation method, and the simulation results demonstrate that our designed collimator can increase the light coupling efficiency from approximately 30% to 60% within a divergence angle of ±20° for all R/G/B light under the spatially incoherent emission.

Multi-Objective Optimization and Intelligent Algorithm Study for 1.5mw Doubly Fed Wind Turbine Blades

Background: The demand for renewable energy has catapulted wind power to the forefront of sustainable energy options. Designing wind turbine blades is a multi-objective optimization problem with conflicting objectives. Optimization methods such as the weighted sum or the goal programming method fail in this context of conflicting objectives: minimum weight, cost, and strength. The study discusses the integration of artificial intelligence algorithms with conventional design approaches for optimal blades of a 1.5MW DFIG wind turbine. Advanced computational methods are used to integrate aerodynamic efficiency, structural durability, and economic feasibility. Methods: This paper uses a broad meta-analytical approach with the integration of finite element analysis (FEA)and artificial intelligence optimization algorithms. This paper will consider three critical optimization techniques: genetic algorithms (GA), particle swarm optimization (PSO), and gray wolf optimization (GWO). The blade performance will be considered for various operating conditions, including aerodynamic efficiency, structural integrity, and cost. This analysis will be performed based on IEC 61400 standards and new frontiers of innovative design optimization techniques. Results: Blade design optimization showed considerable improvement in using AI-driven approaches. For the GWO algorithm, better convergence is around 20% faster than the one obtained with traditional methods. This optimized design reduces weight by 8% and improves structural durability by an increase of 25% in fatigue life. For combined blade design, the maximum value of the power coefficient is 0.27, thus showing considerable improvement compared to the conventional designs. Besides, innovative material selection and design optimization could reduce the cost index by 17.6%. Conclusion: These results highlight that incorporating AI algorithms with traditional methods has significantly enhanced wind turbine blade optimization. This methodology was developed in such a way that it achieved a balanced solution for multi-objective designs, including computational efficiency. This study stresses industrial feasibility by using advanced optimization techniques for real applications, thus demonstrating their impact on the future of wind energy technological development.

Impact of Aerodynamic Design on Vehicle Performance and Vortex Wake Flow

This thesis examines vortex and wake formation mechanisms in automotive aerodynamics and their impact on vehicle performance, emphasizing strategies to mitigate adverse airflow effects through optimal design and computational techniques. It demonstrates that vortices and wake turbulence elevate drag, diminish fuel efficiency, and compromise high-speed vehicle stability. Streamlined body design, optimized rear-end shape, and underbody airflow management can effectively reduce wake turbulence and vortex intensity, enhancing aerodynamic performance. This paper also introduces a variety of computational techniques, including theoretical analysis, wind tunnel experiments and Computational Fluid Dynamics (CFD), and explores the application of these methods in the real-world design process. CFD simulation demonstrates its advantages in evaluating airflow characteristics and optimising design solutions, enabling engineers to carry out efficient iterative design. Future research directions include the application of active aerodynamic systems and the integration of intelligent design algorithms for more efficient airflow control and vehicle performance enhancement.

A comprehensive review of flexible alternating current transmission system (FACTS): Topologies,applications, optimal placement, and innovative models.

This paper is a comprehensive reference for researchers interested in flexible AC alternating current transmission systems (FACTS) technologies. This study investigates modified UPFC models. Besides UPFC, an overview of DPFC will be presented, and the critical differences between these advanced power flow control technologies will be discussed. In particular, we will do a comparative evaluation of three well-known DPFC models: Distributed Series Reactor (DSR), Distributed Static Series Compensator (DSSC), and Distributed Unified Power Flow Controller (DUPFC). This work also discusses the latest models of FACTS technology, such as Carbon Nanotubes (CNT), Multi-winding transformers (MWT), and Line-to-Line Compensators (LLC). Finally, this review paper introduces advanced optimization techniques for optimal placement and design of FACTS devices.

Exploring Architectural Units Through Robotic 3D Concrete Printing of Space-Filling Geometries

The integration of 3D concrete printing (3DCP) into architectural design and production offers a solution to challenges in the construction industry. This technology presents benefits such as mass customization, waste reduction, and support for complex designs. However, its adoption in construction faces various limitations, including technical, logistical, and legal barriers. This study provides insights relevant to architecture, engineering, and construction practices, guiding future developments in the field. The methodology involves fabricating closed architectural units using 3DCP, emphasizing space-filling geometries and ensuring structural strength. Across three production trials, iterative improvements were made, revealing challenges and insights into design optimization and fabrication techniques. Prioritizing controlled filling of the unit’s internal volume ensures portability and ease of assembly. Leveraging 3D robotic concrete printing technology enables precise fabrication of closed units with controlled voids, enhancing speed and accuracy in production. Experimentation with varying unit sizes and internal support mechanisms, such as sand infill and central supports, enhances performance and viability, addressing geometric capabilities and fabrication efficiency. Among these strategies, sand filling has emerged as an effective solution for internal support as it reduces unit weight, simplifies fabrication, and maintains structural integrity. This approach highlights the potential of lightweight and adaptable modular constructions in the use of 3DCP technologies for architectural applications.

Optimization Design for Support Points of the Body-Mounted Solar Panel

Body-mounted solar panels are extensively utilized in satellite construction due to their simple structure and robust vibration resistance. The quantity and arrangement of support points on the body-mounted solar panel significantly affect its natural frequency. Thus, the design of these support points is a crucial aspect of the design process for body-mounted solar panels. This study presents a method for determining the support points of body-mounted solar panels, enabling rapid and precise identification of the quantity and positioning of these points based on the stated natural frequency in the design. First, a new algorithm is proposed, based on the finite element method, to optimize the positioning of support points on the body-mounted solar panel without the need for remeshing. Utilizing this algorithm, the distinct impacts of support point positioning and stiffness on the natural frequency of the solar panel are investigated, and the practical principles are proposed for quickly and accurately identifying the optimal locations of support points to maximize the natural frequency of the solar panel, given a predetermined number of support points. Subsequently, based on Courant–Fischer’s theorem, a method to ascertain the least quantity of support points through two modal analyses is presented. By integrating the aforementioned principles and method, a two-step procedure for identifying the quantity and positioning of support points is developed. Ultimately, the proposed two-step procedure is implemented in the design of the solar panel of the Jilin-1XXX satellite. The modal test reveals that the natural frequency of the solar panel surpasses the design index criteria, hence validating the efficacy and feasibility of the optimal design technique for the support points of the body-mounted solar panel presented in this study.

A systematic review of energy storing dynamic response foot for prosthetic rehabilitation.

The purpose of this paper is to undertake a systematic review on various mechanical design considerations, simulation and optimization techniques as well as the clinical applications of energy storing and return (ESAR) prosthetic feet used in amputee rehabilitation. Methodological databases including PubMed, EMBASE, and SCOPUS were searched till July 2022, and the retrieved records were evaluated for relevance. The design, mechanism, materials used, mechanical and simulation techniques and clinical applications of ESAR foot used in developed and developing nations were reviewed. 61 articles met the inclusion criteria out of total 577 studies. A wide variety of design matrices for energy- storing feet was found, but the clinical relevance of its design parameters is uncommon. Definitive factors on technical and clinical characteristics were derived and included in the summary tables. To modify existing foot failure mechanisms, material selection and multiple experiments must be improved. Gait analysis and International Organization for Standardization (ISO) mechanical testing standards of energy-storing feet were the methods for integrating clinical experimentation with numerical results. To meet technological requirements, various frameworks simulate finite element models of the energy-storing foot, whereas clinical investigations involving gait analysis require proper insight. Analysis of structural behavior under varying loads and its effect on studies of functional gait are limited. For optimal functional performance, durability and affordability, more research and technological advancements are required to characterize materials and standardize prosthetic foot protocols.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Integrating Recycled and Alternative Materials in 3D Concrete Printing for Sustainable Development

3DCP changes the face of construction. It also increases design flexibility, independence from labor, and environmental sustainability. This paper presents research based on some 3DPCs replacing conventional materials with green concrete materials, including aggregates from waste, plastic and rubber materials, recycled glass, geopolymers, and supplementary cementitious materials. These materials reduce carbon emissions and resource depletion, with properties such as tensile strength and durability similar to or better than those of ordinary concrete. Several barriers still need to be overcome, such as material variability, while mechanical anisotropy, scalability constraints, and high equipment costs are other significant factors. For 3DPC to play its full role in achieving the goals of sustainable construction, these barriers should be reduced to a minimum by mixing design optimization, standardization processes, and innovative production techniques.

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

Reinforcement Learning-Based Optimal Hull Form Design with Variations in Fore and Aft Parts

Abstract With recent advancements in artificial intelligence technology, various studies are being conducted in the shipbuilding industry. Traditionally, hull form variation methods have relied on the intuition and expertise of designers, leading to inconsistent results and unintended changes in the ship's main dimensions depending on the designer's competence. Moreover, the iterative process of design variation and analysis to derive the optimal hull form is both costly and time-consuming. To address these issues, this study proposes an optimal hull design technique utilizing reinforcement learning, a type of unsupervised learning in machine learning. Reinforcement learning allows the model to learn from past policies by recording and accumulating the rewards associated with various actions taken by an agent in a specific environment. In this study, after calculating the main parameters of the ship, the agent defines a state representing hull information and performs local transformations of the bow and stern. The reward of reinforcement learning is defined as the change in total resistance due to the hull deformation, constrained by limiting the tolerance of the ship's prismatic coefficient (CP) and longitudinal center of buoyancy (LCB). In this study, the problem is solved by comparing the proximal policy optimization (PPO) algorithm and the deep deterministic policy gradient (DDPG) algorithm to find the best deep reinforcement learning (DRL) model for the hull optimization problem. The results were compared with the genetic algorithm and speed-constrained multi-objective particle swarm optimization (SMPSO), and the optimal hull resistance values were less different, but the time of the reinforcement learning model was five times shorter.

An efficient framework for design optimization using parametric reduced order modeling in a virtual prototyping phase

Virtual Prototype Assembly is a tool enabling the virtual assembly of components, the prediction of noise and vibration performance, and the evaluation of component modification scenarios. FRF-based substructuring is the technique used to assemble the components, either obtained from experimental measurements or numerical simulation models. However, numerical simulations of components can be time-consuming, especially when exploring several component design modifications of large models. The paper proposes a framework combining Virtual Prototype Assembly with a parametric Model Order Reduction technique for vehicle design optimization. The proposed parametric Model Order Reduction technique generates a Reduced Order Model of a component maintaining an explicit dependency on material parameters and properties, such as: Young's Modulus, density, Poisson coefficient, connection stiffness, etc.. This enables fast and efficient simulation of components designs without the need of recomputing numerical models, thus streamlining the component design optimization process. The effect of component design modifications will be assessed at the predicted target vibration levels on an academic application case.

Statistical analysis and optimization of mechanical-chemical electro-Fenton for organic contaminant degradation in refinery wastewater

Refinery wastewater (RWW) poses significant environmental challenges due to its high concentration of organic contaminants, necessitating effective treatment solutions. This study employs the mechanical design of the Electro-Fenton (EF) technique as a feasible alternative for treating RWW. The anode and cathode electrodes, made of stainless steel and iron, respectively, were utilized to conduct organic removal (OR) from RWW. To maximize performance, two metric optimization design techniques were examined: Box Behnken Design (BBD) and response surface methodology. The analysis of variance revealed a high coefficient of determination value (R2 = 0.9685), indicating a second-order provable relationship between the response and self-governing variables. Additional statistical analysis was performed to assess the validity and reliability of the suggested approach. A predictive regression model was developed based on unresolved values, demonstrating a high level of agreement with experimental values. This model identified the optimal equation for the empirical model to predict OR based on predetermined parameters. Based on the BBD, the percentage of OR reached 93.45 % at pH 3, temperature, and electrolysis time of 60 °C, and 30 minutes, respectively. The implications of this study suggest that the optimized Electro-Fenton technique could serve as a robust solution for addressing the environmental impact of RWW, contributing to sustainable wastewater management practices.

Statistical models and implant customization in hip arthroplasty: Seeking patient satisfaction through design

ObjectivesThis study conducts a systematic literature review to explore the role of statistical models and methods in the design of orthopedic implants, with a specific focus on hip arthroplasty. Through a comprehensive analysis of the scientific literature, it aims to understand the relevance and applicability of these models in implant development and research trends in the field of design. MethodsData analysis and co-occurrence mapping techniques were employed to investigate the statistical models used as predictors of satisfaction in hip arthroplasty and in implant design. This approach facilitated a detailed and objective assessment of existing literature, revealing key trends and identifying gaps in current knowledge. Key findingsThe review's findings underscore a burgeoning interest in implant customization, with a significant emphasis on leveraging statistical techniques for optimal design. The logistic model methodology was applied to analyze a survey of hip surgery specialists, revealing that the physician's age does not influence the decision to use a customized implant. Furthermore, the review highlighted a knowledge gap at the intersection of statistics and design discipline concerning implant customization. SignificanceDespite the recognized importance of customization in implant design, there remains a dearth of contributions from the design discipline perspective in the existing literature, indicating substantial room for improvement and the need for interdisciplinary integration. ConclusionThe integration of statistical methods in implant design is crucial, emphasizing the need for multidisciplinary approaches and customization to enhance patient satisfaction. This study provides a foundation for future research that could transform the field of hip arthroplasty through more personalized and effective solutions.

Macro-element modelling for lateral response of monopiles with local scour hole via hyperbolic hardening relation

Monopile is a popular choice in the foundation supporting offshore wind turbines (OWTs), with local scour significantly impacting their lateral responses. Macro-element model, which encapsulates the response between the monopile and the surrounding seabed soils into a force-displacement relation, has been extensively developed to describe offshore foundations. However, such kind of models specifically targeting monopiles subjected to lateral loading in local scour remain underdeveloped. This work proposes a macro-element model with a succinct hyperbolic hardening relation for laterally loaded monopiles in local scour conditions, using the evolutionary polynomial regression (EPR) machine learning technique for easy and optimal design. First, the finite element model is verified and extended to generate force-displacement responses considering the monopile geometries, soil characteristics, and local scour geometries. These results are then utilised to determine the optimal hyperbolic hardening relation of the macro-element model. Next, the EPR technique is employed to determine the relationship between the hyperbolic hardening relation parameters and the influencing factors. Finally, the macro-element model is successfully evaluated by comparing with measurements from centrifuge tests and numerical solutions by finite element analysis, demonstrating its applicability in practical design and the ability to reproduce FEA results with a significant reduction in computational cost.

Failure Probability-Based Optimal Seismic Design of Reinforced Concrete Structures Using Genetic Algorithms

Artificial intelligence (AI) has enabled several optimization techniques for structural design, including machine learning, evolutionary algorithms, as in the case of genetic algorithms, reinforced learning, deep learning, etc. Although the use of AI for weight optimization in steel and concrete buildings has been extensively studied in recent decades, multi-objective optimization for reinforced concrete (RC) and steel buildings remains challenging due to the difficulty in establishing independent objective functions and obtaining Pareto fronts. The well-known Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is an efficient genetic algorithm approach for multi-objective optimization. In this work, the NSGA-II approach is considered for the multi-objective structural optimization of three-dimensional RC buildings subjected to earthquakes. For the objective of this study, two function objectives are considered: minimizing total cost and the probability of structural failure, which are obtained via several nonlinear seismic analyses of the RC buildings. Beams and columns’ cross-sectional dimensions are selected as design variables, and the Mexican Building Code (MBC) specifications are imposed as design constraints. Pareto fronts are obtained for two RC-framed buildings located in Mexico City (soft soil sites), which demonstrate the efficiency and accuracy of NSGA-II for structural optimization.

Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Optimization of crashworthiness design of foam-filled crash boxes under oblique loading for electric vehicles

To increase the energy-absorbing capability of frontal collision management systems and improve vehicle crash safety, foam-filled crash boxes should be optimized. On the basis of a double tubular construction, a novel foam-filled crash box with a different design is developed. The energy absorption capacity, initial peak force, and deformation modes of the original and improved crash boxes were examined using impact models. As opposed to the full-filling design, it is demonstrated that the filling design may utilize less foam while increasing specific energy absorption. The stability of continuing deformation after the first buckling is determined by the foam-filled crash box. For the foam-filled crash box, a better-optimized design technique is suggested using the Taguchi method and principal component analysis (PCA). Compression tests are used to validate the design concept. Therefore, the optimal design technique of the crash box is suitable and practical for the crashworthiness design of crash boxes, considering the combined effect of significant indicators for electric vehicles.

Influence of pneumatic tire enveloping behavior characteristics on the performance of a half car suspension system using multi-objective optimization algorithms

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design.

Research Progress of Advanced Design Method, Numerical Simulation, and Experimental Technology of Pumps in Deep-Sea Resource Exploitation

Amid the escalating global demand for raw materials, the gradual exhaustion of terrestrial mineral resources, and the rise in extraction costs and energy consumption, the development of deep-sea mineral resources has become a focal point of international interest. The pipeline lifting mining system, distinguished by its superior mining efficiency and minimized environmental impact, now accounts for over 50% of the total energy consumption in mining operations. Serving as the “heart” of this system, the deep-sea lifting pump’s comprehensive performance (high pressure tolerance, non-clogging features, elevated lift capacity, wear resistance, corrosion resistance, and high reliability, etc.), is critical to transport efficiency, operational stability, and lifespan of the mining system. As a mixed transport pump for solid and liquid media under extreme conditions, its internal flow structure is exceedingly complex, incorporating gas–liquid–solid multiphase flow. A precise understanding of its internal flow mechanisms is essential for breaking through the design limitations of deep-sea lifting pumps and enhancing their operational stability and reliability under various working conditions and multiphase media, thereby providing technical support for advancing global marine resource development and offshore equipment upgrades. This paper comprehensively reviews the design theory, optimization methods, numerical simulations, and experimental studies of deep-sea lifting pumps. It discusses the application of various design optimization techniques in hydraulic lifting pumps, details the multiphase flow numerical algorithms commonly used in deep-sea lifting pumps along with their modified models, and summarizes some experimental methodologies in this field. Lastly, it outlines the forthcoming challenges in deep-sea lifting pump research and proposes potential directions to promote the commercial development of deep-sea mining, thereby offering theoretical and engineering support for the development of deep-sea mining slurry pumps.