An Integrated AHP–CRITIC–VIKOR Decision Framework for Engineering Design and Evaluation of Children’s Scooters

Shape design sensitivity analysis (DSA) and optimization of spatially rotating objects is presented in this paper. Design sensitivity expressions are derived using a continuum DSA method for spatial objects rotating with angular velocity and angular acceleration, based on three definitions of the finite element mass matrix: consistent, lumped, and diagonalized. The design sensitivity expression derived using a diagonalized element mass matrix, which is consistent with the finite element analysis (FEA) method used in ANSYS, is implemented, although the method can work with other FEA codes, such as MSC/NASTRAN or ABAQUS. Since the continuum DSA method is used, sensitivity information can be computed outside the FEA codes by postprocessing finite element data. Rotating block and turbine blade examples are presented to validate the proposed DSA method. The turbine blade example is optimized using an integrated optimization module of the Design Sensitivity Analysis and Optimization (DSO) tool developed at the University of Iowa. The integrated module consists of ANSYS, MSC/NASTRAN, or ABAQUS for FEA; Design Optimization Tool (DOT) for nonlinear programming; and DSA and design model update programs developed in DSO.

PurposeMajor changes of an aircraft configuration are conducted during the early design stage. It is important to include the airworthiness regulations at this stage while there is extensive freedom for designing. The purpose of this paper is to introduce an efficient design framework that integrates airworthiness guidelines and documentation at the early design stage.Design/methodology/approachA new design and optimization process is proposed that logically includes the airworthiness regulations as design parameters and constraints by constructing a certification database. The design framework comprises requirements analysis, preliminary sizing, conceptual design synthesis and loads analysis. A design certification relation table (DCRT) describes the legal regulations in terms of parameters and values suitable for use in design optimization.FindingsThe developed framework has been validated and demonstrated for the design of a Federal Aviation Regulations (FAR) 23 four-seater small aircraft. The validation results show an acceptable level of accuracy to be applied during the early design stage. The total mass minimization problem has been successfully solved while satisfying all the design requirements and certification constraints specified in the DCRT. Moreover, successful compliance with FAR 23 subpart C is demonstrated. The proposed method is a useful tool for design optimization and compliance verifications during the early stages of aircraft development.Practical implicationsThe new certification database proposed in this research makes it simpler for engineers to access a large amount of legal documentation related to airworthiness regulations and provides a link between the regulation text and actual design parameters and their bounds.Originality/valueThe proposed design optimization framework integrates the certification database that is built of several types of legal documents such as regulations, advisory circulars and standards. The Engineering Requirements and Guide summarizes all the documents and design requirements into a single document. The DCRT is created as a summary table that indicates the design parameters affected by a given regulation(s), the design stage at which the parameter can be evaluated and its value bounds. The introduction of the certification database into the design optimization framework significantly reduces the engineer’s load related for airworthiness regulations.

A parallel finite-element framework for large-scale gradient-based design optimization of high-performance structures

Design Sensitivity Analysis and Optimization tool (DSO) for shape design applications

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

Morphing mechanisms, inspired by biological fliers, offer a lucrative option for controlling aircraft geometry for mitigating adverse aeroelastic phenomena, and potentially using the aircraft flexibility of the aircraft to our advantage. But, in order to enable morphing aircraft, there is a need to determine suitable morphing mechanisms; develop materials and actuation techniques for practical application; and develop a design optimization framework for detailed structural design (with aeroelastic considerations) for a select vehicle concept. We have been developing AMuBA (Aeroservoelastic Multifidelity Design of Biomimetic Aircraft) – a tool for efficient Multidisciplinary Design Optimization (MDO) of aeroelastic aircraft. AMuBA enables aeroservoelastic analysis with Finite Element Analysis (FEA)-based structural sizing within the conceptual design environment. In order to facilitate the coupling between medium-fidelity aerodynamics and high-fidelity structures, a parametric geometry modeling capability has also been developed. These capabilities are being integrated in an MDO environment. The initial target for application is the alleviation of aileron control reversal. A controllable camber morphing mechanism concept has been developed for this purpose, and the mechanism is being implemented in hardware. Additive Manufacturing (AM) is used to develop the complex mechanisms, which will be actuated by Pneumatic Artificial Muscles (PAMs). This paper describes our ongoing efforts in development of the software architecture (including analyses, automation, and optimization framework).

Ashapedesignoptimizationprocedureforhyperelasticstructuresisdevelopedusing ameshlessmethodforanalysis and a continuum-based design sensitivity analysis (DSA) method. The meshless method greatly reduces the mesh distortion or entanglement encountered in using the e nite element method for large deformation nonlinear analysis and structural shape design optimization. The DSA method of Grindeanu et al. (Grindeanu, I., Chang, K.-H., Choi, K. K., and Chen, J.-S., “ Design Sensitivity Analysis of Hyperelastic Structures Using a Meshless Method,” AIAA Journal, Vol. 36, No. 4, 1998, pp. 618 ‐627) is extended by using a pressure projection method to avoid volumetric locking for nearly incompressible materials without the need for large support sizes for the meshless shape functions and to reduce the CPU time. The Lagrange multiplier method is used to impose the essential boundary conditions. An engine mount is employed as an example to demonstrate the feasibility of the proposed optimization method. The mass is minimized subject to constraints on hydrostatic pressure and stiffness characteristics of the component. The design velocity e elds corresponding to theshape design parameters are obtained using the Design Sensitivity Analysis and Optimization tool. Shape design optimization is carried out using the modie ed feasible direction of the Design Optimization Tool.

Computational design optimization has been widely considered a promising technique to help designers address complex design challenges regarding building performance. However, a barrier to applying it to real-world projects is the difficulty in incorporating functional requirements and constraints into the design optimization process. In response, this study presents an optimization-assisted design approach for early-stage architectural design. The approach combines the application of EvoMass, an integrated building mass design generation and optimization tool, and the soft constraint strategy. The combination allows designers to integrate various design requirements and constraints into the optimization, which makes it produce results with higher practical values. To demonstrate the efficacy of the approach, two case studies are presented, which show that the application of optimization facilitates designers to better formulate the design problem and rapidly investigate different design directions for exploration and information extraction.

With the rapid pace of modern urbanization, indoor landscape design has become increasingly important for enhancing the quality of indoor environments and overall user experience. Traditional aesthetic evaluation methods, which often rely on subjective human judgment, lack objectivity and efficiency. To address these limitations, this study proposes a deep learning-based framework for indoor landscape aesthetic evaluation. The proposed approach integrates convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract and analyze both global and local aesthetic features from indoor landscape images. Experimental results on benchmark indoor landscape datasets demonstrate that our method achieves an accuracy of 97.74%, improving by 7.54% points compared to conventional approaches. In addition, the proposed model provides a 14.21% higher aesthetic score and a 10.6-point improvement in functional evaluation metrics. These findings highlight the potential of this CNN-GNN framework as a robust, objective, and efficient tool for indoor landscape aesthetic evaluation and design optimization.

In many areas of design search and optimisation one needs to utilize Computational Fluid Dynamics (CFD) methods in order to obtain numerical solution of the flow field in and/or around a proposed design. From this solution measures of quality for the design may be calculated, which are required by optimisation methods. In large models the processing time for the CFD computatioas can very well be many orders of magnitude larger than the optimisation methods; and the overall optimisation process usually demands a combination of computational and database resources therefore this class of problems is well suited to Grid computing. The Geodise toolkit is a suite of tools for Grid-enabled parametric geometry generation, meshing, CFD analysis, design optimization and search, database, and notification tools within the Matlab environment. These grid services are presented to the design engineer as Matlab functions that conform to the usual syntax of Matlab. The use of the Geodise toolkit in Matlab introduces a flexible and Grid-enabled problem solving environment (PSE) for design search and optimisation. This PSE is illustrated here with an exemplar problem.

In the design and manufacturing of both consumer and commercial products, an important design criteria is the length of the warranty period measured in calendar time. It is very important to know the expected number of failures during the warranty period. This paper provides an engineering tool that will predict the cumulative failures over the warranty period based on laboratory life data and the usage rate data for the products that are operated for only a limited fraction of the total available time. This method uses a joint distribution of the usage rate distribution and the laboratory life distribution to translate usage time to calendar time and not the mean usage since the product use in the field also follows a broad statistical distribution. The proposed technique permits an accurate calculation of the cumulative field failure over prospective warranty periods and thus is useful as an engineers' tool in design optimization as well as in decisions on product release.

This research sheds light on the performance evaluation and design optimization of PCM-HXs for the built environment, addressing several barriers to practical issues to PCM-HX commercialization such as modeling aspects (i.e., modeling expertise and computational / time investment, etc.), manufacturing aspects (i.e., at-scale manufacturing, cost assessments, etc.) and experimental performance assessment (i.e., reliable experimental data, assessment of multiple PCM-working fluid combinations, etc.). We present a novel, comprehensive, and experimentally-validated design optimization framework for PCM-HXs capable of simulating any PCM-HX geometry with reasonable accuracy and significant computational time savings when compared to traditional CFD-based design practices. The framework was validated for a wide range of PCM-HX configurations, including a design optimization for a domestic hot water heater application where TES partially replaces electrical heating input. The resulting PCM-HXs were found to deliver 34-68% of the total daily hot water supply with only 5-10% package volume increase from the water heater, thus within U.S. DOE targets for TES systems. To identify the most promising HXs for PCM applications, first-order geometry and cost analyses were conducted based on off-the-shelf HX products. As part of this work, 9 PCM-HX prototypes were manufactured using additive and conventional manufacturing methods. Detailed economy-of-scale assessments were conducted for the most promising PCM-HXs and were found to have a good outlook for the next 5-10 years. The PCM-HX design optimization framework was validated through comprehensive in-house experimental testing using newly-developed PCM-to-fluid test facilities. In total,10 total in-house component-level experiments were conducted using these prototypes, including 9 with water and 1 with refrigerant (R410A) as the working fluid. It was found that the framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time without manual design changes, eliminating the need for time-consuming and expensive prototyping efforts as part of the design process. As part of this work, a publicly-available PCM web tool was released which includes a PCM property database (531 PCMs) and PCM-HX modeling tool to assist the design community on common PCM-HX use-cases, e.g., single/multiple flow path(s) fluid-to-PCM and air-to-fluid-to-PCM configurations (https://ceeeweb.umd.edu/pcmapp/). This work will accelerate the design and time to market for next generation PCM-HXs.

The DSO tool, a structural design software system that provides the designer with a graphics-based menu-driven design environment to perform easy design optimization for general applications, is presented. Three design stages, preprocessing, design sensitivity analysis, and postprocessing, are implemented in the DSO to allow the designer to carry out the design process systematically. A framework, including data base, user interface, foundation class, and remote module, has been designed and implemented to facilitate software development for the DSO. A number of dedicated commercial software/packages have been integrated in the DSO to support the design procedures. Instead of parameterizing an FEM, design parameters are defined on a geometric model associated with physical quantities, and the continuum design sensitivity analysis theory is implemented to compute design sensitivity coefficients using postprocessing data from the analysis codes. A tracked vehicle road wheel is given as a sizing design application to demonstrate the DSO's easy and convenient design optimization process.

This chapter addresses the need for efficient numerical stochastic techniques in the analysis and design optimization of dynamic systems. Most stochastic analysis techniques result in a heavy computational burden, the cost of which is amplified if embedded into a design optimization framework. This work seeks to alleviate the computational costs of analyzing dynamic systems by reduced order modeling techniques. The key to utilizing reduced order models for stochastic analysis and optimization lies in making them adaptable to design changes and variations in random parameters. This chapter presents an extended reduced order modeling method approximating the response of a dynamic system in the space of design and random parameters. The extended reduced order modeling technique is embedded into a stochastic analysis and design optimization framework. The accuracy and computational efficiency of extended reduced order models are verified with the stochastic analysis and design optimization of a linear structural dynamic system. Stochastic analyses are performed using Monte Carlo simulation, the first-order reliability method, and polynomial chaos expansion. The utility of the extended reduced order modeling method for design optimization purposes is illustrated by solving deterministic and reliability-based design optimization problems. Comparing the stochastic analyses and design optimization results using full and reduced order models show that the overall computational costs can be significantly diminished by the extended reduced order modeling method presented.

<div class="section abstract"><div class="htmlview paragraph">With the rapid adoption of electric vehicles (EVs), ensuring the structural integrity and thermal safety of lithium-ion battery has become a critical priority. Battery failures resulting from mechanical abuse, thermal stress, internal pressure build up or electrical faults may lead to structural failure. To address these challenges, it is essential to understand the coupled thermal and mechanical responses of battery structure under extreme conditions. Thermo-mechanical simulation serves as a powerful tool for predictive safety assessment and design optimization, particularly in addressing thermal propagation and pressure-induced failure events.</div><div class="htmlview paragraph">This study presents a comprehensive coupled thermo-mechanical simulation framework designed to evaluate the structural performance of EV battery enclosures under worst-case thermal and overpressure conditions. The methodology involves high-fidelity three-dimensional modeling of the battery pack enclosure, incorporating realistic material properties, pressure profiles, and temperature data derived from computational fluid dynamics (CFD) analyses. Boundary conditions are carefully applied, and post-processing techniques are used to extract meaningful insights into stress distribution, deformation, sealing behavior, and structural failure modes.</div><div class="htmlview paragraph">The simulation results also identify critical stress concentrations, sealing opening/closing, plastic strain, and potential rupture, offering a detailed understanding of how battery enclosures respond to thermal and mechanical loading. By performing analytical calculations to validate the initial design, the need for a simulation framework became evident to ensure predictive accuracy and support iterative refinement of design parameters. This approach enables early identification of design vulnerabilities, reduces dependence on extensive physical testing, and helps accelerate the overall development cycle.</div><div class="htmlview paragraph">In conclusion, the integration of coupled thermal and mechanical simulation not only enhances design robustness and safety but also supports regulatory compliance and cost-effective development. This study highlights the vital role of virtual validation in the advancement of battery technologies, enabling the creation of safer, more efficient, and more sustainable energy storage systems for next-generation electric mobility and beyond.</div></div>