Engineering Design Applications Research Articles

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Improved marine predators algorithm for engineering design optimization problems

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.

Numerical Simulation Study on the Impact of Excavation on Existing Subway Stations Based on BIM-FEM Framework

Building information modeling (BIM) and finite element method (FEM) models have a wide range of applications in underground engineering design, construction, and operation and maintenance. This study employs a BIM-FEM framework to numerically simulate the impact of excavation on existing subway stations, using the Yanjiang New City Station TOD project as a case study. This framework simplifies the smooth integration of BIM and FEM models, automating functions such as assigning material properties, conducting construction simulations, and generating high-quality meshes. Simulation results reveal significant horizontal and vertical displacements in diaphragm walls, support structures, and subway station structures, with the greatest impacts occurring closest to the excavation site. The BIM-FEM framework is validated as an effective tool for designing foundation pit support structures, enhancing numerical modeling accuracy and efficiency in underground engineering. The findings contribute to a better understanding of the dynamic interactions between excavation and underground structures, informing the development of construction strategies and protective measures to ensure structural safety.

Assessing text-image patent datasets with text-based metrics for engineering design applications

AbstractImages provide concise representations of design artifacts and emerge as the primary mode of communication among innovators, engineers, and designers. The advanced of Artificial Intelligence tools which integrates image and textual information can significantly support the Engineering Design process. In this paper we create 5 different datasets combining both images and text of patents and we develop a set of text-based metrics to assess the quality of text for multimodal applications. Finally, we discuss the challenges arising in the development of multimodal patent datasets.

An improved wall boiling model for numerical simulation of subcooled flow boiling on a new hybrid micro/nanostructured surface

Previous studies have demonstrated that micro/nanostructured surfaces have great potential for heat transfer enhancement. However, simulating subcooled flow boiling on such surfaces is difficult owing to the lack of proper bubble characteristic parameter models, because most models used in flow boiling simulations were developed based on smooth surface conditions, which may limit their applications in engineering design. In this study, we improved upon one validated bubble characteristic parameter model suitable for subcooled flow boiling on smooth surfaces to adapt to the new hybrid micro/nanostructured surfaces proposed by Huang et al. [“Experimental investigation of a new hybrid structured surface for subcooled flow boiling heat transfer enhancement,” Appl. Therm. Eng. 192, 116929 (2021)]. The new bubble characteristic parameter model incorporates both basic correction terms to account for boiling bubble behaviors and ad hoc parameters to account for other unknown effects. Through sensitivity analysis and detailed calibration, the model was simplified to a set of correlations and only one constant parameter. With this improved model, subcooled flow boiling heat transfer simulations were conducted for three target surface specifications under conditions of 4–10 MW/m2 incident heat flux and 1–5 m/s flow velocity and the related heat-transfer mechanisms were further compared and discussed. The maximum error between the simulation and experimental results remains less than 3.5%, indicating that the established model has considerable accuracy in predicting the heat transfer performance for this type of micro/nanostructured surface in high-heat-flux engineering design applications. The heat transfer enhancement for subcooled flow boiling on this type of hybrid micro/nanostructured surface is greatly beneficial owing to its proper organization of convection, evaporation, and quenching heat transfer.

Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application

Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design application

Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

Multi-Strategy Fusion Improved Dung Beetle Optimization Algorithm and Engineering Design Application

Multi-Strategy Fusion Improved Dung Beetle Optimization Algorithm and Engineering Design Application

An improved vibrating particles system method for many-criteria engineering design applications

Optimization is getting more and more important due to its application in real engineering problems. Recently, the vibrating particles system algorithm has been developed as an efficient method for mono-objective optimization. However, in multi- and many-objective design problems, the vibrating particles system method is unable to handle simultaneously the conflicting objectives. The second drawback of the vibrating particles system algorithm is the~variability of the obtained results at each independent test, due to its inability to balance exploitation and exploration capabilities. To address these issues, this paper proposes an enhanced vibrating particles system algorithm called the many-objective vibrating particles system algorithm. The proposed many-objective vibrating particles system algorithm uses the Pareto principle to store the non-dominated solutions of multiple conflicting functions. Moreover, the implementation of the particle position enhancement mechanism to boost this algorithm's exploitation and exploration capabilities is another distinctive aspect of the suggested method. A variety of high-dimensional test functions and engineering design problems are used to evaluate the efficiency of the many-objective vibrating particles system algorithm. The obtained results show that the proposed algorithm outperforms other popular methods in terms of convergence characteristics and global search ability.

Self-Assembling Peptides with Insulin-Like Growth Factor Mimicry.

Growth factor (GF) mimicry involves recapitulating the signaling of larger molecules or cells. Although GF mimicry holds considerable promise in tissue engineering and drug design applications, difficulties in targeting the signaling molecule to the site of delivery and dissociation of mimicking peptides from their target receptors continue to limit its clinical application. To address these challenges, we utilized a self-assembling peptide (SAP) platform to generate synthetic insulin-like growth factor (IGF)-signaling, self-assembling GFs. Our peptide hydrogels are biocompatible and bind target IGF receptors in a dose-dependent fashion, activate proangiogenic signaling, and facilitate formation of angiogenic microtubules in vitro. Furthermore, infiltrated hydrogels are stable for weeks to months. We conclude that the enhanced targeting and long-term stability of our SAP/GF mimicry implants may improve the efficacy and safety of future GF mimic therapeutics.

Mixed-Variable Global Sensitivity Analysis for Knowledge Discovery and Efficient Combinatorial Materials Design

Abstract Global Sensitivity Analysis (GSA) is the study of the influence of any given input on the outputs of a model. In the context of engineering design, GSA has been widely used to understand both individual and collective contributions of design variables on the design objectives. So far, global sensitivity studies have often been limited to design spaces with only quantitative (numerical) design variables. However, many engineering systems also contain, if not only, qualitative (categorical) design variables in addition to quantitative design variables. In this paper, we integrate Latent Variable Gaussian Process (LVGP) with Sobol’ analysis to develop the first metamodel-based mixed-variable GSA method. Through numerical case studies, we validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Furthermore, while the proposed GSA method is general enough to benefit various engineering design applications, we integrate it with multi-objective Bayesian optimization (BO) to create a sensitivity-aware design framework in accelerating the Pareto front design exploration for metal-organic framework (MOF) materials with many-level combinatorial design spaces. Although MOFs are constructed only from qualitative variables that are notoriously difficult to design, our method can utilize sensitivity analysis to navigate the optimization in the many-level large combinatorial design space, greatly expediting the exploration of novel MOF candidates.

A numerical study of tiny particles experiencing thermal and reaction migration in a micropolar fluid over a stretching sheet with Newtonian heating

ABSTRACT Substantial research on a micropolar fluid is currently being done. These novel materials offer various applications in bionic engineering and prosthetic design. The influences of cross-diffusive (diffusion-thermo and thermo-diffusion) on the stream of micropolar fluid via permeable stretching sheet under Newtonian heating are investigated in this study. The assumed mathematical model is then transformed using appropriate similarity transformations into its non-dimensional system. The resulting ordinary derivative formulation is solved through the spectral Chebyshev scheme with collocating integration. Graphically, the impacts of all critical factors on dimensionless velocity, microrotation, thermal distribution, and mass transfer are investigated. Tables are created to depict the interaction of various factors on engineering quantities. The outputs are compared with the accessible literature to demonstrate the effectiveness of the spectral Chebyshev schema, and excellent agreement is obtained.

Decision support in engineering design: the ELIGERE open source software platform

In engineering design, the selection of the optimal design solution represents a critical phase for the development of successful products. In this paper, we present ELIGERE, an open source decision support system targeted at engineering design applications. It allows to rank multiple design solutions with respect to different evaluation criteria according to the evaluations provided by a group of experts. ELIGERE is composed by three main modules: (1) a distributed web application, for generation and participation to the decision making session; (2) a mathematical engine, based on the fuzzy analytical hierarchy process, to quantify the results of the decision making session according to the evaluation of the experts; (3) a relational database, to collect and store data. The most important contribution of this paper is introducing a practical and effective software tool that facilitates decision-making analysis based on the fuzzy analytical hierarchy process, thereby allowing better-informed choices on concept selection, as it has been designed with a specific focus on the engineering field. In this paper we describe the key concepts of ELIGERE and its modalities of use in several real use cases. Finally, we compare ELIGERE with the widely used general purpose decision support software based on the fuzzy analytical hierarchy process.

Cyclic Reconfigurability of Deployable Ring Structures With Angulated Beams

Abstract Deployable ring structures have been useful concepts for engineering design applications due to their smooth transformation from an initially compact configuration to a substantially larger deployed state. As a result, over the past few decades, various computational and kinematic models have been introduced to analyze the behavior of such deployable structures. Here, we propose a type of deployable ring structure designed based on a transformable concept known as the Swivel Diaphragm. In particular, the geometry of the deployable ring structure is introduced, including different structural configurations with fixed pivots and angulated beams. Then, taking a group-theoretic approach, we establish appropriate constraint equations and perform a symmetry-adapted kinematic analysis. In the next step, the mobility and self-stress states of three example structures are studied, including a simple ring structure with C3 symmetry, a C6-symmetric ring with a hexagonal Swivel Diaphragm structure, and a general Cn-symmetric ring structure with inner hoops. The usefulness and effectiveness of the utilized group-theoretic approach are examined and validated through the study of these examples. We show that the kinematic behavior of the numerical models developed in this study agrees well with the finite element results obtained using abaqus. Importantly, the illustrated motion trajectories of the reconfigurable structures demonstrate that they retain a single degree-of-freedom as well as a cyclic symmetry. Moreover, it is shown that the angulated members necessarily rotate around the fixed pivots, which could be practically desirable in designing transformable structures for various applications in engineering and architecture.

LCF behaviour of high strength aluminium alloys AA 6110A and AA 6086

The proposed research presents the comprehensive investigation of the Low Cycle Fatigue (LCF) behaviour of two high-strength aluminium alloys of series AA 6xxx: the conventional alloy AA 6110A and the newly developed alloy AA 6086. Both alloys were characterised in the as-cast condition after homogenisation, extrusion, and T6 heat treatment.The quasi-static strength and hardness of the aluminium alloy AA 6086 were found to be significantly higher if compared to the AA 6110A alloys, while the ductility was a little bit smaller. The LCF tests showed that the AA 6086 alloy is more suitable for the high-cycle fatigue regime. On the other hand, the engineering advantage of the AA 6110A alloy is only for low-cycle fatigue applications if less than 100 loading cycles are expected in the service life of the analysed structure. The fatigue cracks formed predominantly on the α-AlMnSi intermetallic particles in both alloys, and, during LCF tests, exhibited small crack propagation. The area of the fatigue crack growth was much smaller than the area of the forced fracture. At smaller amplitude strains the fatigue striations were present at the fracture surface, while, at higher amplitude strains, they were not present. The obtained experimental results represent a good basis for engineering design applications of the analysed alloys AA 6086 and AA 6110A.

Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization

A computationally efficient dehomogenization technique was developed based on a bioinspired diffusion-based pattern generation algorithm to convert an orientation field into explicit large-scale fluid flow channel structures. Due to the transient nature of diffusion and reaction, most diffusion-based pattern generation models were solved in both time and space. In this work, we remove the temporal dependency and directly solve a steady-state equation. The steady-state Swift-Hohenberg model was selected due to its simplistic form as a single variable equation and intuitive parameter setting for pattern geometry control. Through comparison studies, we demonstrated that the steady-state model can produce statistically equivalent solutions to the transient model with potential computational speedup. This work marks an early foray into the use of steady-state pattern generation models for rapid dehomogenization in multiphysics engineering design applications. To highlight the benefits of this approach, the steady-state model was used to dehomogenize optimized orientation fields for the design of microreactor flow structures involving hundreds of microchannels in combination with a porous gas diffusion layer. A homogenization-based multi-objective optimization routine was used to produce a multi-objective Pareto set that explored the trade-offs between flow resistance and reactant distribution variability. In total, the diffusion-based dehomogenization method enabled the generation of 200 unique and distinctly different microreactor flow channel designs. The proposed dehomogenization approach permits comprehensive exploration of numerous bioinspired solutions capturing the full complexity of the optimization and Swift-Hohenberg design space.

Enumeration and Identification of Unique 3D Spatial Topologies of Interconnected Engineering Systems Using Spatial Graphs

Abstract Systematic enumeration and identification of unique 3D spatial topologies (STs) of complex engineering systems (such as automotive cooling systems, electric power trains, satellites, and aero-engines) are essential to navigation of these expansive design spaces with the goal of identifying new spatial configurations that can satisfy challenging system requirements. However, efficient navigation through discrete 3D ST options is a very challenging problem due to its combinatorial nature and can quickly exceed human cognitive abilities at even moderate complexity levels. This article presents a new, efficient, and scalable design framework that leverages mathematical spatial graph theory to represent, enumerate, and identify distinctive 3D topological classes for a generic 3D engineering system, given its system architecture (SA)—its components and their interconnections. First, spatial graph diagrams (SGDs) are generated for a given SA from zero to a specified maximum number of interconnect crossings. Then, corresponding Yamada polynomials for all the planar SGDs are generated. SGDs are categorized into topological classes, each of which shares a unique Yamada polynomial. Finally, within each topological class, 3D geometric models are generated using the SGDs having different numbers of interconnect crossings. Selected case studies are presented to illustrate the different features of our proposed framework, including an industrial engineering design application: ST enumeration of a 3D automotive fuel cell cooling system (AFCS). Design guidelines are also provided for practicing engineers to aid the application of this framework to different types of real-world problems such as configuration design and spatial packaging optimization.

A stabilized nonconforming Crouzeix–Raviart finite element method for the elasticity eigenvalue problem with the pure traction boundary condition

The linear elasticity problem has important applications in structural analysis and engineering design. It is shown that the classical Crouzeix–Raviart finite element method is unstable to solve the linear elasticity eigenvalue problem with the pure traction boundary condition. In this paper, first we propose a new scheme with the stabilization terms. Then we prove the a priori error estimates for approximate eigenpairs. Finally, we carry out numerical experiments using the stabilized scheme. Theoretical analysis and numerical results show that this scheme is locking free and efficient.

Intercomparison of hydrostatic and nonhydrostatic modeling for tsunami inundation mapping

Nonhydrostatic modeling has emerged as an effective tool for seismological and tsunami research for over a decade, but its general application in hazard mapping and engineering design remains a topic of discussion. The approach incorporates the depth-averaged vertical velocity and nonhydrostatic pressure in the nonlinear shallow-water equations that provide a Poisson-type equation via the conservation of mass for quasi-three-dimensional flows. After the 2011 Tohoku tsunami, the State of Hawaii augmented the existing inundation maps to account for probable maximum tsunamis from Mw 9.3 and 9.6 Aleutian earthquakes. The use of both hydrostatic and nonhydrostatic modeling with a common set of telescopic computational grids covering 1330 km of shorelines facilitates a thorough intercomparison under distinct extreme events over a range of tropical island terrain and bathymetry. Including vertical flow dynamics can enhance the formation of a slowly attenuating trough behind the leading crest across the ocean as well as drawdown of receding water over steep nearshore slopes. The nonhydrostatic approach consistently gives lower predictions of the offshore tsunami amplitude due to frequency dispersion but can produce more severe coastal surges from resonance of the leading crest and trough over insular slopes as well as trapping of tsunami waves by wide shelves. Despite the potential for underestimating coastal surges, the lack of vertical inertia in hydrostatic models can result in substantially larger runup over steep terrain. The tsunami processes leading to inundation are complex with a strong dependence on the waveform and topography that can be well elucidated by the nonhydrostatic approach.

Diffusion Models Beat GANs on Topology Optimization

Structural topology optimization, which aims to find the optimal physical structure that maximizes mechanical performance, is vital in engineering design applications in aerospace, mechanical, and civil engineering. Recently, generative adversarial networks (GANs) have emerged as a popular alternative to traditional iterative topology optimization methods. However, GANs can be challenging to train, have limited generalizability, and often neglect important performance objectives such as mechanical compliance and manufacturability. To address these issues, we propose a new architecture called TopoDiff that uses conditional diffusion models to perform performance-aware and manufacturability-aware topology optimization. Our method introduces a surrogate model-based guidance strategy that actively favors structures with low compliance and good manufacturability. Compared to a state-of-the-art conditional GAN, our approach reduces the average error on physical performance by a factor of eight and produces eleven times fewer infeasible samples. Our work demonstrates the potential of using diffusion models in topology optimization and suggests a general framework for solving engineering optimization problems using external performance with constraint-aware guidance. We provide access to our data, code, and trained models at the following link: https://decode.mit.edu/projects/topodiff/.