Optimization Framework Research Articles

Additive manufacturing-driven simultaneous optimization of topology and print direction for thermoelastic structures considering strength failure

Abstract This paper presents a strength-based simultaneous optimization method for optimizing thermoelastic structural topology and print direction in the presence of anisotropy induced by additive manufacturing. The approach utilizes the bi-directional evolutionary structural optimization (BESO) framework and defines design variables including element density and print-off angle. Firstly, an anisotropic thermoelastic constitutive model is established for finite element analysis. By introducing the Tsai-Hill failure criteria, the strength constraint to evaluate the stress level of additively manufactured anisotropic components is formulated. The P-norm aggregation function is employed to approximate the maximum strength failure coefficient. Then, the aggregated strength constraint is augmented to the optimization objective through a Lagrange multiplier. Sensitivity analysis of the new objective function with respect to the elemental design variables is performed, and an analytical approach is proposed to optimize the print-off angle. To improve the stability of the optimization procedure, a series of numerical algorithms and parameter updating strategies are developed. The effectiveness of our proposed method is demonstrated through typical numerical examples, highlighting a desirable match between the structural topology and the print direction can greatly improve the structural performance.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

Toward the Emergence of Intelligent Control: Episodic Generalization and Optimization

Abstract Human cognition is unique in its ability to perform a wide range of tasks and to learn new tasks quickly. Both abilities have long been associated with the acquisition of knowledge that can generalize across tasks and the flexible use of that knowledge to execute goal-directed behavior. We investigate how this emerges in a neural network by describing and testing the Episodic Generalization and Optimization (EGO) framework. The framework consists of an episodic memory module, which rapidly learns relationships between stimuli; a semantic pathway, which more slowly learns how stimuli map to responses; and a recurrent context module, which maintains a representation of task-relevant context information, integrates this over time, and uses it both to recall context-relevant memories (in episodic memory) and to bias processing in favor of context-relevant features and responses (in the semantic pathway). We use the framework to address empirical phenomena across reinforcement learning, event segmentation, and category learning, showing in simulations that the same set of underlying mechanisms accounts for human performance in all three domains. The results demonstrate how the components of the EGO framework can efficiently learn knowledge that can be flexibly generalized across tasks, furthering our understanding of how humans can quickly learn how to perform a wide range of tasks—a capability that is fundamental to human intelligence.

A general multi-objective Bayesian optimization framework for the design of hybrid schemes towards adaptive complex flow simulations

Achieving accuracy with underresolved simulation of complex compressible flows with multiscale flow structures is a challenge. Either the numerical dissipation or the resolution and thereby the numerical cost is impractically high. Also, in the design of numerical solvers, the application of a solver for specific flow classes is balanced by robustness allowing the study of a broad range of flows. In this study, we propose a hybrid fifth-order targeted essentially non-oscillatory (TENO5)-based scheme tailored to optimally simulate compressible flows with underresolved dilatational and vortical multiscale structures. For optimal design, three data-driven objectives are defined. A novel objective that derives from the numerical dissipation rate analysis is a key element to deal with underresolved complex flows in practical applications. The optimization process employs a multi-objective Bayesian optimization framework with an expected hypervolume improvement and three flow configurations representative for a broad range of two- and three-dimensional flows with genuine and non-genuine subgrid scales. The optimized hybrid scheme is validated by comparing with shock-capturing schemes of the weighted essentially non-oscillatory (WENO)- and TENO- families with flows of complex shock interactions, Kelvin-Helmholtz instabilities, shock-vortex interactions, vortical and turbulent flows

Risk-averse electrolyser sizing in industrial parks: An efficient stochastic-robust approach

Hydrogen is called to be one of the most important energy vectors in future energy systems. Nowadays, its use in the industry sector is prominent, finding multiple applications in fertilizer production or oil refining. In this sense, some industry sectors demand a considerable amount of hydrogen for their processes. In many cases, hydrogen must be purchased externally, which supposes a challenge due to hydrogen transportation is costly and few efficient. In this context, local hydrogen production through mature electrolysis technology may suppose an attractive alternative in industrial parks. This paper focuses on this topic, in particular, a risk-averse electrolyser sizing methodology is developed. The new approach accounts for uncertainties in electricity prices as well as local renewable generation and demand through an original hybrid stochastic-robust model. The developed uncertainty modelling is integrated into a novel four-level optimization framework, whose main result is the optimal electrolyser rated power. To efficiently attain the solution, an original hybridization of the Benders' decomposition and the Column and Constraint Generation Algorithm is proposed. The developed methodology results efficient in an illustrative three-industry park, showing that installing local hydrogen generation may reduce the amount of hydrogen purchased externally (by 38%) and project costs by 2.5%. Furthermore, increasing the robustness level leads to increase the project cost by 8%, while assuming unfavourable realization of uncertainties. Moreover, the developed tool is further validated in larger parks, involving an increasing number of industries, showing that the proposed methodology scales well with the size of the park.

EXPRESS: Crowd-starting a Shared (Shuttle) Service with Customer Suggestions

Digital platforms have improved the effciency and quality of smart city operations by soliciting more customer inputs, for example, in the form of suggestions. One innovative option in urban transportation is the shared shuttle service, which lies between traditional public transportation and ride-hailing services. Platforms that offer these services can gather customer suggestions in a \crowd-starting" manner, which provides valuable insights into customer needs. However, this also presents a challenge in balancing service coverage and quality to meet customer needs implied by their suggestions. To address this issue, we introduce an optimization framework designed to maximize expected profit by leveraging customer response models which characterize how customers will respond to different service attributes and how their suggestions inform these responses. When estimating these response models, we present methods involving isotonic penalty and shrinkage tailored for handling small datasets. To demonstrate the practical implications, we apply our model to a shared shuttle service case study and discuss practical considerations, such as the value of information, the effectiveness of our estimation approaches, and the benefits of involving customers in the service design process.

Microstructure Design of Multiphase Compositionally Complex Alloys II: Use of a Genetic Algorithm and a Vanishing Cracked Particle Model

AbstractAchieving high strength, ductility, and toughness via microstructure design is challenging due to the interrelated dependencies of strength and ductility on microstructural variables. As a natural extension of the microstructure design work in Bhattacharyya and Agnew (Microstructure design of multiphase compositionally complex alloys I: effects of strength contrast and strain hardening, 2024), an optimization framework to obtain the microstructure that maximizes the toughness is described. The strategy integrates a physics-based crystal plasticity model, which accounts for damage evolution within the reinforcement through a “vanishing cracked particle” model that is governed by Weibull statistics, and a genetic algorithm-based optimization routine. Optimization constraints are imposed in the form of bounds on the microstructure parameters such that they are most likely attainable by conventional thermomechanical processing. Various matrix strain hardening behaviors are considered, as well as the strength contrast between the two phases and fracture behavior of the reinforcement. It is shown that the addition of a fine-grained (hard) reinforcing phase is preferred as is a matrix that exhibits sustained strain hardening such as is observed under TRIP/TWIP scenarios. Finally, the Pareto-optimal set of solutions for several scenarios are presented which offer new insights into the linkages between microstructure and mechanical properties.

Integrating NSGA-II and CFD for enhanced urban airflow prediction: Recalibration of closure coefficients for a nonlinear eddy viscosity model

Accurately predicting urban environmental airflows using the Reynolds-averaged Navier‒Stokes (RANS) approach presents significant challenges due to unsolved closure issues. Previous attempts to address these limitations through coefficient adjustments were constrained by isotropic assumptions, resulting in limited generalizability and reliability, especially for complex building configurations. In this study, we proposed a recalibration approach within a multi-objective optimization framework. Nondominant sorting genetic algorithm-II (NSGA-II) and computational fluid dynamics (CFD) numerical computations were utilized to optimize the closure coefficients of a nonlinear eddy viscosity (NLEV) model, with a focus on improving the accuracy of the mean velocity, turbulent kinetic energy, and airflow characteristic predictions. To ensure the robustness of the model, appropriate objective functions were carefully defined. The proposed approach was evaluated using three-dimensional (3D) building cases related to urban prototypes, and the results demonstrated the necessity of multi-objective optimization. Our findings indicated that the trade-off solution on the Pareto front consistently outperformed the baseline and single-objective optimal versions across different types of urban airflow, demonstrating superior generalizability. The simulation results of this solution exhibited closer agreement with the wind tunnel experimental data and provided more accurate distributions of the time-averaged values and airflow characteristics. The normalized root mean square errors are reduced by about 33 %, 52 %, 35 % and 9 % in velocity, and 31 %, 25 %, 24 % and 11 % in turbulent kinetic energy, respectively. These results underscore the effectiveness of the multi-objective optimization framework in addressing closure issues and improving the prediction accuracy of complex urban environmental airflows.

The Integration of Dual Evaluation and Minimum Spanning Tree Clustering to Support Decision-Making in Territorial Spatial Planning

Spatial planning, a policy instrument for creating sustainable environments that meet the needs of the current and future generations, has been implemented extensively worldwide. However, it is difficult for urban planners to thoroughly determine the spatial value of a territory and make informed decisions regarding the efficient utilization of regional resources in the real world. This study proposes a novel methodological framework for spatial pattern optimization that can guide future land use by integrating Minimum Spanning Tree (MST) clustering with a comprehensive evaluation system (dual evaluation). Furthermore, the validity of this framework is demonstrated through a case study of territorial spatial planning in Deyang, China. The findings indicate that (1) the methodological framework presented in this study offers valuable guidance for the spatial arrangement of territorial resources, especially in practical projects; and (2) the combination of dual evaluation and MST clustering can facilitate automatic regionalization to identify spatial clusters exhibiting functional similarity in terms of land use. By focusing on methodological advancements, this study concludes that the integration of dual evaluation (DE) and MST clustering not only simplifies the identification of optimal land-use patterns but also promotes a more systematic and efficient approach to support spatial planning.

PRACTICE OF USING IMPLANTS IN GYNECOLOGICAL SURGICAL INTERVENTIONS (SCIENTIFIC LITERATURE REVIEW)

Introduction. The use of implants during surgical interventions in gynecology can contribute to the anatomical restoration of the genital organs. The most commonly used implants in gynecology are polypropylene meshes, which promote tissue ingrowth, thus restoring reproductive organs. One of the reasons for using implants in gynecology is pelvic organ prolapse, including uterine prolapse, anterior or posterior vaginal wall prolapse, and vaginal vault prolapse. Synthetic implants are often utilized to reinforce surgical interventions for gynecological disorders, with better indicators of anatomical success in the long term. The aim of the research is to investigate the biomechanical properties of implants in gynecology as effective means for the surgical restoration of female reproductive organs. Materials and methods. Bibliosemantic, comparative, and systematic analysis methods were employed. The proposed recommendations were developed based on the analysis of contemporary literature, results of randomized trials, and meta-analyses dedicated to studying the impact of implants in surgical interventions in gynecological practice. Results. The analyzed results indicate that the optimal supportive framework of implants for gynecological surgery, particularly in the treatment of prolapse, should aim to facilitate the formation of a new matrix for fascial reinforcement, thereby minimizing the development of scar tissue. Conclusions. The primary objective of using implants in gynecological surgical interventions is to search for optimal designs to complement vaginal surgery. This is essential for prolonging treatment outcomes, increasing patient satisfaction, and reducing complications.

Analyzing Hydrogen Flow Behavior Based on Deep Learning Sensor Selection Optimization Framework

Abstract With tremendous potential to lower the carbon footprint of power generation and as an alternative energy carrier for many applications, hydrogen has emerged as a key potential energy carrier. Understanding of geological conditions and the injection and production changes over time for hydrogen storage are paramount, requiring in situ reservoir sensing options. Determining the flow behavior is of critical importance in order to estimate hydrogen volumes within the reservoir. A novel AI-driven methodology for hydrogen flow behavior and volume estimation was demonstrated. For determining the S1 through S3 predicted hydrogen storage quantities, the framework is linked to an uncertainty estimate framework. The Pohokura field in New Zealand served as the basis for the framework's evaluation, and it performed acceptably in terms of identifying the hydrogen flow behaviors and quantities inside the subsurface reservoir. The framework is a significant first step in assessing the amount of hydrogen that can be stored in subterranean reservoirs for long-term hydrogen storage.

Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent

AbstractThis study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness‐governed structures, size optimization is crucial for strength‐governed structures. Optimizing size, shape, and topology together consistently leads to the most weight‐efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.

Synergistic Integration of EVs and Renewable DGs in Distribution Micro-Grids

This paper proposes a multi-objective optimization framework for safe, reliable, and economic integration of electric vehicles (EVs) and renewable distributed generators (DGs) in distribution micro-grids. EV and DG coordination optimization with the use of vehicle-to-grid (V2G) technology along with system reconfiguration optimization is developed to provide collective revenues and address integrational complications that may occur by additional system loading due to EV charging and EV-DG energy exchanges. A Genetic Algorithm (GA) optimizes the EV charging/discharging in synergies with renewable DGs to maximize benefits that can be captured by their collaborative participation in electricity market and through renewable energy arbitrage. The developed EV charging/discharging optimization is implemented in a real 134-bus distribution network and is evaluated for its potential operational implications, namely, increased system losses. A system reconfiguration is then proposed to reduce the system losses by optimizing the flow of power through switching on/off the connections within the micro-grid and/or with other distribution systems. Simulation results demonstrate the efficiency of the proposed method in not only providing collective revenues, but also in enhancing the system operation by reducing the losses of the distribution grid. The collective benefits proposed by the developed optimization and validated by the simulation results facilitate transitioning to clean and eco-friendly sources of energy for generation and transportation, which in turn leads to more sustainable development of societies and communities.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Reverse engineering morphogenesis through Bayesian optimization of physics-based models

Morphogenetic programs coordinate cell signaling and mechanical interactions to shape organs. In systems and synthetic biology, a key challenge is determining optimal cellular interactions for predicting organ shape, size, and function. Physics-based models defining the subcellular force distribution facilitate this, but it is challenging to calibrate parameters in these models from data. To solve this inverse problem, we created a Bayesian optimization framework to determine the optimal cellular force distribution such that the predicted organ shapes match the experimentally observed organ shapes. This integrative framework employs Gaussian Process Regression, a non-parametric kernel-based probabilistic machine learning modeling paradigm, to learn the mapping functions relating to the morphogenetic programs that maintain the final organ shape. We calibrated and tested the method on Drosophila wing imaginal discs to study mechanisms that regulate epithelial processes ranging from development to cancer. The parameter estimation framework successfully infers the underlying changes in core parameters needed to match simulation data with imaging data of wing discs perturbed with collagenase. The computational pipeline identifies distinct parameter sets mimicking wild-type shapes. It enables a global sensitivity analysis to support the regulation of actomyosin contractility and basal ECM stiffness to generate and maintain the curved shape of the wing imaginal disc. The optimization framework, combined with experimental imaging, identified that Piezo, a mechanosensitive ion channel, impacts fold formation by regulating the apical-basal balance of actomyosin contractility and elasticity of ECM. This workflow is extensible toward reverse-engineering morphogenesis across organ systems and for real-time control of complex multicellular systems.

Influence of Surface Roughness on Symmetric Airfoil Flow Properties and Reynolds Number

Collaborative Aircraft Design Optimization: Investigating Roughness Management of Wings to Reduce Drag and Improve Performance This study investigates the potential of roughness control of aircraft wings based on joint design optimization. By adding roughness points to specific wings, our goal is to reduce drag and improve the overall performance of the aircraft. A multi-modal design optimization (MDO) framework combining aerodynamics, modelling, and performance analysis was used in this study. We measure the effect of tissue on the boundary layer, the progression of laminar flow, and the potential for weight savings due to reduced friction. This study investigates how roughness control interacts with other design factors such as flow components or blade materials to achieve optimal performance. The ultimate goal is to create a collaborative design that uses tissue management to contribute to next-generation fuel efficiency and high performance. Keywords – CFD, Surface Roughness, Wind Tunnel, Aerodynamic Efficiency.

Surrogate-model-based optimization of a nonideal polymerization reactor for producing tailored molecular weight distribution

The optimization of polymerization reactors with nonideal mixing has been greatly hindered by a tremendous computation burden. In this study, a global optimization framework to integrate full MWD calculations and detailed reactor dynamics with nonideal mixing behaviors was developed by reducing the considerable computation time required for implementing full MWD. A combination of surrogate-model-based optimization and a computational fluid dynamics (CFD)-compartment reactor model was applied to produce the desired MWD in a nonideal reactor. A neural network (NN) surrogate model was trained using 3,000 MWD data produced from the mechanistic reactor model. The results showed that the model successfully described asymmetric MWD with bimodality resulting from the nonideal mixing behaviors in the reactor. The surrogate model was used to construct operation maps for the asymmetry and bimodality factors, with SHapley Additive exPlanations (SHAP) values showing that the flow rate of the chain transfer agent had the largest impact on the asymmetry factor, whereas temperature significantly influenced the bimodality factor. Finally, the optimization strategy based on the asymmetric and bimodality factors successfully determined operating conditions to produce four tailored MWDs, reported in actual polymerization reactors with R scores of 0.988–0.997.

Optimal determination of valve setting coefficient for the multi-tank water feeder system

ABSTRACT Filling up water to several water distribution service tanks equitably needs a proper valve setting in the operation. Setting by trial and error requires considerable time which could invite more labour-level exercise. This study addresses the issue of unequal water distribution to elevated service tanks (ESTs). It is possible to achieve an optimal valve loss coefficient, which ensures equal water supply to all ESTs. Throttle and control valves play a crucial role in regulating the flow rate and pressure of water. An optimization framework for the valve setting coefficient is proposed. The genetic algorithm is used as an optimization model and hydraulic simulation is performed using EPANET 2.0 software through its toolkits. The proposed model is applied to a hypothetical system and illustrated. The obtained valve setting coefficients make equitable distribution of water to all the ESTs.

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

A thermal infrared target tracking based on multi-feature fusion and adaptive model update

Thermal infrared target tracking has attracted increasing attention from researchers due to its unique imaging advantages. In response to common issues encountered in correlation filtering algorithms during the tracking process, such as background clutter, distractors, and occlusion, a framework for improving infrared target tracking is proposed. This paper proposes to extend multi-channel stepped grayscale features and optimize the feature set, fully leveraging the complementarity between multiple features and enhancing their ability to represent tracking targets. The tracking results are evaluated using the peak energy of the position weighted average, enabling an adaptive model update that improves the algorithm's ability to resist occlusion. The proposed algorithm is evaluated on the LSOTB-TIR and PTB-TIR infrared tracking datasets in terms of precision, success rate, and processing speed. In addition, the algorithm is also experimented on a self-collected dataset. The experimental results indicate that the proposed optimization framework can be integrated into correlation filtering trackers with a certain level of applicability. The improved algorithm demonstrates effective handling of various complex infrared scenarios and interferences, achieving high-precision and real-time tracking of infrared targets.