Design Optimization Framework Research Articles

Reliability and Sustainability Integrated Design Optimization for Engineering Structures with Active Machine Learning Technique

Recent strategic plans from many governments share the same emphasis on sustainable development goals. The construction industry is recognized as one of the primary contributors to greenhouse gas emissions. Stimulated by such political and practical requirements, this research aims to develop a reliability and sustainability integrated design optimization (RSIDO) framework for engineering structures. Statistical modelling is conducted which provides probabilistic models for embodied carbon coefficients (ECCs). The proposed RSIDO framework contains three main stages. The first stage solves the classical reliability-based design optimization (RBDO) problem. The second stage conducts the reliability-based embodied carbon quantification to define the sustainable reliability constraint. Lastly, the new sustainable probabilistic design constraint is embedded in the design question. Furthermore, to overcome the computational difficulty, an active-learning extended-support vector regression (AL-X-SVR) technique is proposed to improve the computational efficiency of the proposed framework. The embedded regression algorithm is presented with consolidated theoretical backgrounds. Further, an active learning strategy is implemented to select the learning samples efficiently. The applicability and effectiveness of the proposed AL-X-SVR method are demonstrated using a highly nonlinear-constrained mathematical validation. Additionally, the RSIDO framework is demonstrated through three engineering applications. This research contributes a comprehensive framework that not only addresses reliability and sustainability but also enhances computational efficiency, thereby introducing an innovative approach to design optimization in building engineering.

A digital design framework for the dimensional optimization of parallel robots based on kinematic and elasto-dynamic performance

The dimensional optimization based on kinematic and dynamic performance indices is proven significant for improving the operational performance of parallel robots. As the pose-dependent performances vary with the dimensional parameters, there are still many challenges in the optimal design of parallel robots, such as the possible conflict amongst different performance indices and computational expensiveness. By considering a 4-DOF high-speed parallel robot as an illustrative example, this paper presents a framework for the optimal design of parallel robots by integrating skeleton modeling, CAD-CAE integration and multi-objective optimization techniques. In this approach, the models for kinematic and elasto-dynamic performance evaluation are first developed using the CAD and CAE techniques, respectively. After evaluating the performance distributions over the task workspace, several reference poses representative of multiple neighboring sample poses are determined to formulate corresponding local performance indices by using the Hard C-Means (HCM) clustering analysis algorithm. This consideration leads to the determination of the optimal dimensions by investigating the Pareto-optimal solutions of a multi-objective optimization problem, allowing the conflict amongst different performance indices to be appropriately handled and the computational time to be saved significantly. The results of the case study show that the proposed approach can significantly enhance the elastic dynamic performance while ensuring that the kinematic performance is not excessively compromised, when compared to the performance of existing physical prototype of the robot. A software package has been developed using the proposed framework, providing a highly accessible way for designing various types of parallel/hybrid robots based on CAD and CAE techniques.

Aerodynamic optimization of aircraft wings using machine learning

This study proposes a fast yet reliable optimization framework for the aerodynamic design of transonic aircraft wings. Combining Computational Fluid Dynamics (CFD) and Machine Learning (ML), the framework is successfully applied to the Common Research Model (CRM) benchmark aircraft proposed by NASA. The framework relies on a series of automated CFD simulations, from which no less than 160 planform variations of the CRM wing are assessed from an aerodynamic standpoint. This database is used to educate an ML surrogate model, for which two specific algorithms are explored, namely eXtreme Gradient Boosting (XGB) and Light Gradient Boosting Machine (LGBM). Once trained with 80 % of this database and tested with the remaining 20 %, the ML surrogates are employed to explore a larger design space, their optimum being then inferred using an optimization framework relying on a Multi-Objective Genetic Algorithm (MOGAO). Each ML-based optimal planform is then simulated through CFD to confirm its aerodynamic merits, which are then compared against those of a conventional, fully CFD-based optimization. The comparison is very favourable, the best ML-based optimal planform exhibiting similar performances as its CFD-optimized counterpart (e.g. a 14 % higher lift-to-drag ratio) for only half of the CPU cost. Overall, this study demonstrates the potential of ML-based methods for optimizing aircraft wings, thereby paving the way to the adoption of more disruptive, data-driven aircraft design paradigms.

Validation of a general-use high flux isotope reactor–specific metaheuristic optimization framework for isotope production target design

Currently, advanced optimization methods are limited for isotope production (IP) campaigns at the US Department of Energy’s High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL), leading to years of conservative and historical approaches with minimal innovation. Moreover, the growing demand for new and existing isotopes is beginning to challenge the capacity of HFIR. This work explores the development and integration of metaheuristic (MH) optimization techniques for more efficient target design and irradiation strategies. As a test case, the optimization framework was applied to a routinely produced isotope at HFIR, 188W, with the objective of maximizing the specific activity (SA), a key production metric. The framework includes Gnowee, a Python-based MH optimization algorithm, coupled with the Monte Carlo N-Particle version 6 (MCNP6) and Oak Ridge Isotope Generation (ORIGEN) activation/depletion/decay codes to design, simulate, and evaluate thousands of potential target design and irradiation scheme candidates. The framework relies on mock input files, design and irradiation variables for the algorithm to select, as well as a user-defined objective function to score each candidate based on the returned SA. Given the inherent complexities and computational time required when modeling and simulating the full HFIR model, a novel simplified MCNP6 model is presented in this article to increase the computational efficiency of the framework. The variables explored include irradiation location, number of cycles, and the number of W samples. Over 1,000 simplified model candidates were simulated in the same amount of time as a single full HFIR model run. By comparing the simplified model optimization’s top candidate(s) with the full HFIR model results, the framework was verified to accurately explore the design space and converge on the top performing candidates. Lastly, past experimental data was compared to the data generated by the framework/model and both show that fewer W rings return higher SA, as expected. The verified and validated techniques provide a standardized solution to increase IP efficiencies by exploring thousands of unique target designs and irradiation strategies in a similar time as that required to run a single case in the full HFIR MCNP6 model. Both the novel simplified model and the full HFIR model show a more than 30% increase in SA if all presented modifications are applied to the current design and strategy. Thus, the objective of building a general-use, computationally efficient optimization framework for HFIR IP was accomplished, and has the potential to be applied to other IP campaigns.

Ride-pooling Electric Autonomous Mobility-on-Demand: Joint optimization of operations and fleet and infrastructure design

This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to a heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.

Reactive powder concrete beam design using a mathematical software to reduce costs

AbstractReactive powder concrete is widely recognized for its remarkable mechanical characteristics and resilience, rendering it an ideal option for structural uses that include heavy loads. However, the intricate interaction of numerous material and geometric aspects makes constructing reactive powder concrete beams extremely difficult. By using MATLAB to provide an optimized design framework for beams, this research tackles these issues. Particle swarm optimization and evolutionary algorithms are two of the advanced techniques used in the study to solve the optimization problem. It also includes constraints other one as structural requirements and material limitations. Results demonstrate that MATLAB-based optimization facilitates effective design, lowering costs and material consumption, with potential for more applications in the building sector.

Science Outcome Design Parameters for Cluster-Randomized Trials Involving Teachers

Programs aiming to improve student science outcomes often involve teachers. When designing cluster-randomized trials (CRTs) to evaluate the causal effects of these programs, investigators need empirical design parameters as critical inputs in the study design phase. Using proper empirical design parameters to conduct power analysis, investigators can ensure designs have adequate statistical power to detect treatment effects and efficiently use resources. The current study uses two nationally representative samples to estimate empirical design parameters for science outcomes in settings of students (nested within teachers) nested within schools. We compile design parameters for intraclass correlation coefficients and proportions of variance explained by covariates. We illustrate how to use the results to design efficient and effective CRTs in both the conventional statistical power analysis framework and the optimal design framework that additionally considers sampling costs.

A value-of-information-based optimal sensor placement design framework for cost-effective structural health monitoring (with application to miter gate monitoring)

Structural health monitoring (SHM) involves collecting information to assess the health of a structure, typically to guide risk-informed maintenance decision-making or predict limit state behavior throughout its lifespan. Although the value of information (VoI) obtained from an SHM system can facilitate improved decision-making, it is important to estimate its overall utility by considering the costs involved in designing, developing, installing, maintaining, and operating the system. A feasible SHM system provides greater expected returns resulting from data-informed lifecycle management decisions than the cost of the SHM system design, fabrication, deployment, operation, and maintenance. That is, since data acquisition is a precursor to data-informed decision-making, the design of an SHM system governs its feasibility. Such cost-benefit analyses are a current topic of research in the SHM community. One approach that has been proposed for these analyses is a preposterior decision analysis using the VoI metric. In this paper, we propose a sensor optimization framework that maximizes the VoI over the structure’s lifecycle, constrained by a pre-decided maintenance policy. We use two different VoI metrics: the traditional expected VoI and a gambling-theory-inspired normalized expected-savings-to-investmentrisk ratio. We introduce three time-normalized, unitless VoI metrics that are valuable for evaluating the performance of an SHM design over an extended period. Furthermore, we consider the effect of different risk profiles on the overall optimal sensor design, recognizing that the cost of decision-making depends on the utility and risk perception of the decision-maker. We conduct a detailed analysis on the marginal utility gain of gathered information as we add more sensors, observing the utility to diminish in line with the law of diminishing returns as the information content increases. This framework is applied to the design of an SHM system used for monitoring miter gates.

Improving the potential of fifth-generation district heating and cooling networks through robust design and operational optimization under future energy market and demand uncertainties

Network investments and projected energy prices greatly impact the financial viability and environmental impact of fifth-generation district heating and cooling (5GDHC) networks. Compared to the previous generation, the upfront investment costs in 5GDHC networks are relatively high due to the decentralized energy demand and supply of the participating prosumers. The transition to 5GDHC is also hindered by uncertain energy markets and the fluctuating energy demands of the prosumers. It makes investors hesitant to commit, even for promising business cases. In this work, a framework is presented to assess the economic and environmental performance of a 5GDHC business case, based on a multi-objective robust optimization algorithm (MO-RDO), to identify the optimal trade-offs between environmental and economic designs. The proposed methodology consists of four steps. In the first two steps, a specific business case is modeled, and techno-economic and environmental performances are analyzed against uncertain energy markets. In the third step, the techno-economic and environmental indicator responses are used to develop a data-driven machine-learning model. This model offers better computational efficiency than a high-fidelity nonlinear model and assesses the most influential parameters driving economic and environmental performance via a global sensitivity analysis. This aids in refining the multi-objective robust design optimization (MO-RDO) framework by pinpointing key design variables and objectives amid data uncertainties. Additionally, the formulated MO-RDO provides a mix of environmentally and economically optimal network designs and operational parameters and highlights the trade-offs between them. The designs and trade-offs are evaluated using financial metrics like net present value (NPV), return on investment, and discounted payback period. These metrics are calculated over the project life, considering the initial investment and net savings. For the considered case, the environmental design has a 10% higher investment cost compared to the economically optimal solution but reduces total emissions (TE) by 13% and improves operational cost savings, which is crucial for better financial indicators. The proposed framework aligns with the EU transition plan to incorporate waste energy sources and decarbonization paths, estimating only a 3% increase in NPV as a risk for the environmentally optimal solution on a financial scale.

A Reformulated-Vortex-Particle-Method-Based Aerodynamic Multi-Objective Design Optimization Strategy for Proprotor in Hover and High-Altitude Cruise

An improved multi-objective design optimization framework is proposed for the efficient design of proprotor blades tailored to specific high-altitude mission requirements. This framework builds upon existing methods by leveraging a reformulated Vortex Particle Method (rVPM) and incorporates three key stages: (1) rapid determination of overall proprotor parameters using a semi-empirical model, (2) optimized blade chord and twist distribution bounds based on minimum energy loss theory, and (3) global optimization with a high-fidelity rVPM-based aerodynamic solver coupled with a multi-objective hybrid optimization algorithm. Applied to a small high-altitude tiltrotor, the framework produced Pareto-optimal proprotor designs with a figure of merit of 0.814 and cruise efficiency of 0.896, exceeding mission targets by over 15%. Key findings indicate that large taper ratios and low twist improve hover performance, while elliptical blade planforms with high twist enhance cruise efficiency, and a tip anhedral further boosts overall performance. This framework streamlines the industrial customization of proprotor blades, significantly reducing the design space for advanced optimization while improving performance in demanding high-altitude environments.

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Hybrid Optimization Framework for MIMO Antenna Design in Wearable IoT Applications Using Deep Learning and Bayesian Method

Multi-objective Optimization Design Framework and Flight Performance Prediction for Reentry Module Using a Rapid Analysis Program

Multi-objective Optimization Design Framework and Flight Performance Prediction for Reentry Module Using a Rapid Analysis Program

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Directly solving sophisticated partial differential equation constrained optimization problems is not only extremely time-consuming, but also very hard to find unique optimal solutions. Here, we propose stable and efficient surrogate models for seawater reverse osmosis desalination processes that enable thorough quantitative description of hydrodynamics and local transport characteristics in narrow flow channels. Without iteratively solving complex multi-physics simulation problem taking several hours, the proposed multi-scale design optimization framework significantly reduces the problem complexity by computing the surrogate models in seconds. Moreover, a fast-converging active subspace particle swarm optimization framework is proposed to address the optimal design problem. Compared to the standard particle swarm optimization algorithm, the proposed method enhances the average optimum by 14% and the standard deviation of optimum results for multiple runs is reduced by no less than ten times. The optimized desalination system achieves 9% reduction on energy consumption and 30% improvement on water production efficiency.

Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm

ABSTRACTShip form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare‐bones multi‐objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi‐objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi‐objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi‐objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low‐carbon shipping.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Multi-fidelity data mining-based design optimization framework for scramjet-based aircraft

Aircraft design faces complex multidisciplinary problems and often involves numerous design variables, making direct optimization a time-consuming task. With the development of big data and machine learning technologies, using data mining methods to assist optimization has become key to addressing the inefficiency in multidisciplinary design optimization (MDO). To address the high cost of data acquisition in current data mining, this paper proposes an aircraft design optimization framework based on multi-fidelity data mining, using a large amount of inexpensive cursory estimation data to execute the MDO process and form a multidisciplinary optimization database. Then, data mining methods are used to decouple disciplinary relationships, transforming multidisciplinary coupling problems into simpler multi-objective problems. Subsequently, under viscous CFD computation conditions, the EDGE-p-BFFD deformation method is used to fine-tune the obtained configurations, further enhancing multidisciplinary performance. Before both optimizations, data mining methods were used to analyze the impact of design variables on objective functions, forming design knowledge and completing variable selection. In the multidisciplinary optimization practice of airbreathing aircraft, it can be found that the proposed framework effectively achieves disciplinary decoupling and explores the relationship between shape and performance. In the multidisciplinary optimization of the waverider based on cursory estimations, particle swarm optimization increased the aircraft's cruising range by 8.74 %. In the multi-objective optimization of the lifting body based on viscous CFD calculations, data mining was used to transform the problem into several key objectives. The MOEA/D algorithm increased the range by 2.9 %, validating the effectiveness of the data mining-based MDO framework proposed in this paper.

A novel design optimization framework to sustain remanufacturability

The ever-increasing global carbon emissions have urged the need for environmentally conscious/sustainable product design, for which the design for remanufacturing (DfRem) is one potential approach. DfRem targets at designing products that have multiple life cycles, thus significantly reducing raw material usage, energy consumption, and carbon emissions. In this paper, we develop a three-stage framework that consists of (1) systematic design space exploration and a multi-objective optimization formulation to minimize the likelihood of failure causes (such as fatigue and wear) and environmental footprint, (2) topology optimization to further reduce material usage without significantly affecting the load-carrying capability of the product, and (3) post-topology optimization design verification to ensure the proposed design satisfies all design constraints. The environmental impact can be assessed at varying comprehensiveness levels (e.g., design and manufacturing phase, use phase) and in terms of carbon or GHG emission, energy use, and waste generation. Because the novel design framework predominantly adjusted the geometry, we focused on mass-based change and energy savings due to sustained remanufacturability. The multi-objective optimization formulation in the first step results in a Pareto optimal set of possible design solutions that the designer can use for the second step. We demonstrate the utility of this framework through a case study of an engine cylinder head subjected to thermo-mechanical loads, where we find that about 5% of the product mass can be conserved with only about a 3% increase in surface area that has a fatigue life less than 10,000 cycles.

Development of A Workshop on the Topic of the Bionically Inspired Lightweight Design Method for 3D Printed Components

Additive manufacturing methods, particularly 3D printing, are widely utilized in research and engineering for crafting lightweight yet durable materials capable of withstanding substantial forces. Leveraging insights from biomechanical structures offers a deeper understanding of reinforcement techniques and optimal design strategies to enhance strength. This paper focuses on the development of a Draisine design, historically significant in Karlsruhe, as part of the BWSplus project "Drais3D-Trinational" workshop. Through a comprehensive analysis of bionic influences on lightweight design and 3D printing parameters, this study aims to create an optimal design framework for the workshop. Utilizing Computer-Aided Design (CAD) software such as SolidWorks and Creo Parametric, along with Finite Element Analysis using ANSYS R2023 Workbench, deformation and stress analysis are conducted. Investigation into 3D printing parameters, including infill patterns, temperature, support systems, and orientation, seeks to identify optimal solutions considering factors like processing time, robustness, and filament wastage. The objective is to explore the biomechanical influence on construction methods, concept design, and parameter construction in preparation for the Drais3D-Trinational workshop in March 2023. The expected outcomes include the presentation of two main designs with varied parameters, alongside analyses such as Finite Element Analysis and optimization of 3D printing parameters, emphasizing the role of bionic structures in defining the optimal Draisine design.

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.

Theoretical investigation of stacked supercapacitors under extreme mechanical impact

Power sources for microsystems in extreme mechanical environments are crucial. Supercapacitors, as a common power source for microsystems, may experience failures, fires, and explosions in extreme mechanical impact scenarios such as smart helmets, vehicle collisions, and high-speed aircraft. It is rare to have supercapacitors specifically designed for extreme mechanical impacts, as well as research on their physical processes when subjected to impacts. This research introduces a multi-physics model to describe the energy storage and voltage fluctuations of the proposed stacked supercapacitors under extreme mechanical impacts. With the help of finite element simulation software and interpretable machine learning, we analyzed the influence of various parameters on stacked supercapacitors under extreme mechanical impacts. Furthermore, a prototype of the non-separator stacked supercapacitor, resisting extreme mechanical impacts has been developed. The impact resistance of such supercapacitor was tested using a machete hammer, verifying the consistency between theoretical analysis and experiments. Finally, an iterative framework has been proposed to optimize the energy storage and impact resistance of proposed supercapacitors. This study provides a design prototype, theoretical model, and optimization framework for supercapacitors intended for impact resistance, enhancing the potential application of micro-supercapacitors in extreme mechanical environments.