Iterative Design Optimization Research Articles

Design optimization of lightweight automotive seatback through additive manufacturing compression overmolding of metal polymer composites

With the growing demand for enhanced automotive fuel efficiency and environmental sustainability, there is a need for lightweighting automotive components through innovative design and manufacturing processes. This study leverages a combination of numerical iterative design optimization and hybrid additive manufacturing–compression molding (AM-CM) technique for metal polymer composites to lightweight an automotive seatback. The AM-CM process enables robust mechanical interlocking between metals and composites, boasting high stiffness and strength with low overall density. Replacing metallic components with such metal polymer composites allows for comparable mechanical performance while significantly reducing the overall weight. First, the automotive seatback design space is reduced to critical load carrying regions using topology optimization and high stress concentration areas are identified using finite element analysis. Next, a lightweight metal polymer subcomponent is designed for a high stress concentration region. The full seatback frame with spatially heterogeneous material-specific design is then iteratively optimized to enable enhanced stiffness with minimal weight. Overall, the automotive seatback frame designed with location-specific metal, polymer, and metal polymer composite materials weighs 20% less than the metal-only design while exhibiting similar stiffness.

Conditional generative adversarial networks for image-based sunlight analysis of residential blocks

Sunlight simulation is often used to analyze whether buildings receive sufficient sunlight in complex design cases. However, it is time-consuming to establish geometrical and physical models, and non-negligible computation time (from a few seconds to minutes) leads to high time cost of iterative design optimization. Therefore, this study builds surrogate models to instantaneously detect problematic buildings (those receiving insufficient sunlight) using images of residential blocks. Different conditional generative adversarial networks (CGANs), including Pix2Pix and conditional attention GAN (CAGAN), are employed to train surrogate models. The attention mechanisms and the multi-scale vision give CAGAN an edge in capturing the global structure of buildings and learning high-level knowledge related to the occlusion relationships between buildings. In addition, this study also proposes a stepwise strategy to construct composite surrogate models. The first sub-model provides more informative images to help the second sub-model learn the features of problematic buildings. This study focuses on evaluating the models based on the detection performance in addition to image structural similarity and pixel similarity, which have been used in most studies. The CAGAN models outperform the Pix2Pix models in the average detection accuracy (ADA) and the 100 % successful detection probability (SDP) on the same dataset. Moreover, the stepwise strategy increases the ADA and SDP to 89.88 % and 71.07 %, respectively. The proposed methods can be extended to other urban environmental performance diagnoses, such as thermal comfort or the wind environment.

StressD: 2D Stress estimation using denoising diffusion model

Finite element analysis (FEA), a common approach for simulating stress distribution for a given geometry, is generally associated with high computational cost, especially when high mesh resolution is required. Furthermore, the non-adaptive nature of FEA requires the entire model to be solved even for minor geometric variations creating a bottleneck during iterative design optimization. This necessitates a framework that can efficiently predict stress distribution in geometries based on given boundary and loading conditions. In this paper, we present StressD, a framework for predicting von Mises stress fields based on the denoising diffusion model. The StressD framework involves two models, a U-net-based denoising diffusion model and an auxiliary network to generate and predict stress distribution in structures. The denoising diffusion model generates a normalized stress map based on the given geometry, boundary conditions and loading condition, while the auxiliary network is used to determine the scaling information needed to un-normalize the generated stress map. We evaluate the StressD framework on cantilever structures and show that it is able to accurately predict von Mises stress fields while significantly reducing computational cost compared to traditional FEA.

Study of the Hull Structural Deformation Calculation Using the Matrix Displacement Method and Its Influence on the Shaft Alignment

The analysis of the influence of hull deformation on shaft alignment is predominately conducted using the finite element method (FEM), which is time-consuming, labor-intensive, and challenging to use for iterative hull design optimization. In this paper, hull deformation is separated into two parts—global deformation and local deformation, simplified to a single-span beam model and a grillage beam model, respectively—then solved using the matrix displacement method (MDM). Compared to FEM, the proposed method has a small calculation error, proving its correctness, while the calculation time is greatly reduced. The proposed method has been used to calculate the hull deformation of a ship under various conditions and evaluate its influence on shaft alignment. The results indicate that under certain conditions, the bearing reaction forces are constant, whereas the bearing pressure changes as a consequence of the change in shaft-to-bearing angle. The comparison between local rotation and shaft-to-bearing angle reveals that bearings in various positions follow distinct laws. We suggest that the shaft-to-bearing angle be used as an additional parameter in the evaluation of shaft alignment calculations. Moreover, when optimizing bearing pressure, bearings in different positions are affected differently by global and local deformation, and their optimization priorities are distinct.

Optimized rotor structural design methodology for high-speed electrical machines based on mechanical characteristics

In high-speed electrical machines (HSEM), mechanical vibration associated with critical speeds of rotor systems can be detrimental to the machine operational stability, especially for vibration near resonances. The structural sizing of a rotor system is often constrained to its mechanical parameters, such as its overall weight, maximum torque, and rated power, which complicate the design, necessitating an iterative design optimization process. This work presents a systematic and iterative rotor structural sizing optimization methodology that incorporates semi-analytical and numerical modeling, specifically taking into account multiple mechanical design parameters from the static, rotordynamic, and electromagnetic-thermal evaluation. The proposed methodology provides an effective design optimization procedure for a general application of HSEM to accelerate its design process, by which the power density and the dynamics stability during high-speed operation are optimized simultaneously. A parametric analysis was carried out where a 24-kW, 12,000-rpm aviation starter-generator was optimized. It was demonstrated that the power density of the machine could be increased by 14.7% from 2.3 to 2.6 kW/kg, while the critical speed was increased by 128.6% from 1,400 to 3,200 Hz.

A Computationally Efficient High-Fidelity Multi-Physics Design Optimization of Traction Motors for Drive Cycle Loss Minimization

Continuous improvement in performance of interior permanent magnet (IPM) machines is critical for electric vehicle traction applications. However, due to the cross-coupling and saturation effects, a significant amount of time-consuming finite element analysis (FEA) simulations are required to accurately estimate machine performance. Moreover, iterative design optimization will take significantly longer. In this article, an improved rapid performance estimation technique utilizing surrogate models is developed and coupled with a design optimization algorithm. The proposed framework has significantly less computational cost than alternative surrogate-based approaches, and efficiently employs drive cycle loss minimization for a multi-physics, multi-objective traction motor design optimization. Simulation and experimental results suggest the proposed optimization framework yields optimal designs more efficiently than existing methods while maintaining accuracy.

An Unsupervised Microwave Filter Design Optimization Method Based on a Hybrid Surrogate Model-Assisted Evolutionary Algorithm

In resonator-coupled bandpass filter 3D design, it is a routine that the filter optimization methods are guided/supervised by designers’ experience to carry out an iterative design optimization process. To realize automated or unsupervised filter 3D design optimization, a new method, called hybrid surrogate model-assisted evolutionary algorithm for filter optimization (H-SMEAFO), is proposed. H-SMEAFO aims to automatically obtain a highly optimal filter 3D design without designers’ interaction (i.e., unsupervised) and is also not restricted to certain kinds of filter structures. In H-SMEAFO, the key innovations include a hybrid response feature-based objective function and a hybrid surrogate model-assisted global optimization algorithm; both are designed bespoke for filter design landscape characteristics. The performance of H-SMEAFO is demonstrated by an 8th-order dual-band waveguide filter with four transmission zeros and a 6th-order waveguide filter with two transmission zeros, for which, unsupervised design optimization does not appear to be possible using existing methods. Numerical results show the effectiveness and advantages of H-SMEAFO.

Development of a Transient Synchronization Analysis Tool for Line-Start PM Motors

With more stringent IEC energy efficiency standards, electrical machine industry increasingly focuses on new motor technologies. Amongst others, the line-start permanent magnet synchronous machine (LSPMSM) is considered as an attractive alternative to induction machine, especially for low power and fixed-speed applications. However, the design of LSPMSMs is rather complex as both steady-state and transient synchronization performances need to be considered. The synchronization capability determination of a LSPMSM design usually relies on time-consuming transient finite-element simulations, which is impractical for use in an iterative design optimization process. This paper compares and evaluates various existing analytical synchronization analysis methods in an attempt to identify most suitable equations and methods for fast synchronization analysis. Using the selected methods, a software tool is developed that can seamlessly work with ANSYS Electronics Desktop to perform rapid transient synchronization analysis. Given its ability to quickly determine the critical inertia factor of a LSPMSM design, the software tool is further adapted for use in a highly iterative, multi-objective design optimization procedure. It shows that the developed software tool can be successfully used in the design of LSPMSMs.

A DT-RSDM Model

With rapid development of Digital Twin (DT), it seems that DT is vital to realize Cyber-Physical System (CPS) with the aid of dynamic interaction between digital twin and physical counterpart. In radar applications, digital twin can solve inherent shortfalls in radar structure design including ideal design description, poor dynamic interaction and limited implementation extension. Thus, a new DT-driven 5S Radar Structure Design Model (DT-RSDM) is proposed in this paper, focusing on improving radar structure design effectiveness and efficiency by employing iterative design optimization, design knowledge management.

Iterative Optimal Design of Special Moment Resisting Devices for Steel Frames

The present paper proposes an iterative procedure devoted to reaching the optimal design of an innovative, recently proposed, moment resisting device. This special device, called Limited Resistance Plastic Device (LRPD), can be utilized, as an example, to equip a steel frame when it is required that the frame must be designed to substitute a masonry panel, i.e., it must be characterized by a structural behaviour as close as possible to the one of the replaced masonry wall. This purpose can be reached by designing the relevant frame imposing appropriate constraints on the elastic stiffness and on the limit resistance. The result can be obtained just by ensuring that the elastic stiffness and the limit resistance be independent of each other. To this aim it is necessary to suitably equip the steel frame by the previously cited LRPD. In particular, these moment resisting connections ensure that in a prefixed portion of the given beam element, the limit bending moment reduces without any variation of the global elastic stiffness. In order to reach this goal, the LRPD is substantially constituted by an inner portion, devoted to exhibit the desired reduced limit bending strength and to receive the plastic deformations, and two outer portions, devoted to guarantee the invariance of the elastic bending stiffness. The proposed iterative procedure allows to design a device respecting all the required features avoiding the presence of any dangerous local instability phenomenon. To this aim, the design will contain appropriate constraints ensuring that the device cross sections appertain to class 1 or class 2 I-cross section, as prescribed in the more recent standard codes. Some examples, validated by 3D solid tetrahedral elements analysis in ABAQUS environment, prove the good reliability of the proposed procedure and show the easy applicability of the computational approach.

A novel smart model for high-end equipment structure design

Cyber-Physical System (CPS) serves the key to realize smart manufacturing, while Digital Twin (DT) acts as the key to realize CPS. The evolving and maturing Digital Twin concept has several inherent shortfalls. In this paper, a new RSDM model enabled by Digital Twin is proposed to address these problems with iterative design optimization and efficient design knowledge management. The results show that RSDM model is helpful for enhancing radar structure design efficiency and design optimization, and is a promising framework for implementing smart design.

Numerical and experimental justification of transcatheter aortic valve prosthesis design

Objective: to justify the design of a self-expanding transcatheter aortic valve prosthesis based on a biomaterial stabilized with ethylene glycol diglycidyl ether using numerical simulation and a series of field experiments with working prototypes to determine the consistency of the proposed design solutions.Material and methods. Numerical computer models of a developed aortic valve prosthesis intended for transcatheter implantation, as well as prototypes of the most promising concepts for a series of field tests, were used in the work. Computer 3D models were subjected to numerical analysis in the Abaqus/CAE environment (Dassault Systemes, France) based on the finite element method with iterative design optimization and repeated numerical experiments. Physical prototypes of the transcatheter prosthesis were subjected to a series of mechanical tests for axial and radial compression, as well as tests on a Vivitro hydrodynamic stand (Vivitro Labs, Canada) under simulated normal flow. All studies were carried out in a comparative aspect with a similar transcatheter aortic valve prosthesis (control), the CoreValve™ bioprosthesis (Medtronic, Inc., USA).Results. Computer simulation demonstrates the stress-strain state values that do not significantly exceed the critical levels (628 and 756 MPa versus the threshold value 1080 MPa) for two basic concepts of support frames. The fatigue strength based on the calculation of the mean and alternating stresses corresponding to normo- and hypertensive states based on the Goodman diagrams, did not reveal any evidence that the threshold values (destruction area after 200 million cycles) were exceeded. The hydrodynamic characteristics of working prototypes made on the basis of computer models correspond to the testing data of CoreValve™ clinical bioprosthesis: the effective orifice area was 1.97 cm2, the mean transprosthetic gradient was 8.9 mm Hg, the regurgitant volume was 2.2–4.1 mL per cycle depending on the prototype model.Conclusion. Generally, experiments carried out showed the consistency of the concepts, including from the point of view of implementation of the leaflet apparatus based on xenogeneic tissues treated with ethylene glycol diglycidyl ether.

Optimal Design of Mega-Frame Core Wall Structures Equipped with Viscous Damped Outriggers for Human Comfort Performance under Wind Loading

Megatall and supertall buildings often adopt megastructure systems characterized by secondary structure systems, and the serviceability problem caused by wind-induced vibrations often becomes the dominant factor in the structural design. Because the deformation of a supertall building usually presents bending characteristics, a viscous damped outrigger can reduce the wind-induced vibration of a supertall building with the installation of a small number of viscous dampers. However, time history analysis of the prototype model considering the nonlinear characteristics of viscous dampers is time-consuming, which is not conducive for iterative design optimization. Additionally, the conventional simplified model composed of one cantilever beam cannot be used for the analysis and design of a viscous damped outrigger. In this study, a simplified wind-induced vibration prediction model is proposed based on the mechanical characteristics of megastructures. This simplified model is a plane model that includes both core walls and frames whose member size can be extracted from the original structure. Parametric analysis shows that the simplified model has high acceleration prediction accuracy. An optimal design method combined with the simplified model, which aims to minimize the damped outrigger system cost, is proposed. A 600-m supertall building is presented as a case study. The accuracy and effectiveness of the simplified model and the optimal design method proposed in this study are illustrated. Thus, applying this optimal design method in combination with the simplified model can save significant analysis and design time and is conducive to the application of viscous damped outriggers in practical engineering.

Reliability-based design optimization of a spar-type floating offshore wind turbine support structure

The application of reliability-based design optimization (RBDO) methods to offshore wind turbine systems is highly relevant regarding economic efficiency and for considering prevailing uncertainties within the design process. Furthermore, RBDO is a very promising approach in optimizing systems when classification and standardization are not fully available. The level of difficulty of design optimization already increases when including the reliability aspect, but becomes even more challenging when dealing with the highly complex system of floating wind turbines (FWTs), which has not yet been applied. Thus, this paper presents for the first time an integrated framework for RBDO of FWTs, combining concepts of optimization with reliability-based design and advanced modeling, requiring reasonable computational effort and time expenditure. In preprocessing, environmental conditions, limit states, and uncertainties are specified, an appropriate reliability assessment approach is elaborated, and response surfaces for various system geometries in the optimization design space are generated ahead of the RBDO execution. These are finally used by means of an interpolation approach for the reliability calculation integrated in the iterative design optimization. On the example of a spar-buoy FWT system, the application of the presented methodology and the feasibility of coupling FWT design optimization with reliability assessment are shown.

Recursive Gaussian Process-Based Adaptive Control, With Application to a Lighter-Than-Air Wind Energy System

This brief presents a nonmodel-based adaptive control technique that combines principles from machine learning and iterative design optimization with those of continuous-time, falsification-based adaptive control. At the crux of the proposed control strategy are two core elements. First, the recursive Gaussian Process (RGP) modeling is used to maintain an online characterization of the system at hand without the need to maintain a complete database of previously collected measurements (which is required in traditional GP modeling). Second, an adaptation strategy is employed that falsifies candidate controllers from a continuous candidate design space based on desired performance specifications and statistical hypothesis testing. In specific, the control parameter design space is explored by selecting points associated with high uncertainty. Through the use of statistical hypothesis testing, regions of the design space determined to be suboptimal at a user-specified level of confidence are rejected in order to converge to an optimal set of control parameters. The RGP-based adaptation is validated through simulations and laboratory-scale experiments using an airborne wind energy case study. Through these studies, the RGP-based adaptation approach is shown to be effective and is shown to exhibit favorable convergence times when compared with a mature adaptive control technique, extremum seeking (ES).

Mass-producible micro-optical elements by injection compression molding and focused ion beam structured titanium molding tools.

We demonstrate mass production compatible fabrication of polymer-based micro Fresnel lenses by injection compression molding. The extremely robust titanium-molding tool is structured with high precision by focused ion beam milling. In order to achieve optimal shape accuracy in the titanium we use an iterative design optimization. The inverse Fresnel lens structured into the titanium is transferred to polymers by injection compression molding, enabling rapid mass replication. We show that the optical performance of the molded diffractive Fresnel lenses is in good agreement with simulations, rendering our approach suitable for applications that require compact and high-quality optical elements in large numbers.

Improvable models as scaffolds for promoting productive disciplinary engagement in an engineering design activity

AbstractBackgroundThere is a need to find ways of productively engaging K‐12 students with engineering design. I investigate a new class of physical models, referred to as Improvable Models, as scaffolds for helping middle school students productively engage in engineering design practices.Purpose/HypothesisThe purpose of this study is to answer two questions: (a) How do students use Improvable Models? and (b) How does the use of Improvable Models as scaffolds support students' design of engineering solutions?Design/MethodDesign‐based research was conducted in a sixth‐grade classroom. Students used a type of Improvable Model, specifically the Suboptimal System (SS) seed model. Improvable Models make the entire design space accessible and enable iterative design optimization. An SS seed model represents inefficient design decisions and is used as an initiator for iterative design optimization. The study spanned 10 lessons, each 45 min long, totaling 8 days of instructional activity spread across 1 month. Data sources included student conversations, design work, and presentations. The productive disciplinary engagement framework was used to analyze the data.ResultsStudents used the seed model for productively engaging with five disciplinary practices: decomposing the design challenge into individual design parameters, considering the relationship between design parameters, reasoning through multiple design parameters and making trade‐offs, weighing multiple solutions, and using design heuristics for ideation and design optimization. The seed model scaffolded students' productive engagement in various disciplinary practices such as informed decision‐making and systematic exploration.ConclusionThis study provides an approach for designing learning environments for productively engaging K‐12 students in engineering design practices using a suboptimal model.

Convergence Analysis and Experimental Validation of a Fused Numerical/Experimental Active System Optimization Framework

This paper presents a convergence analysis and experimental validation of an iterative design optimization framework that fuses numerical simulations with experiments. At every iteration, a G-optimal design generates a set of simulations and experiments that are used to characterize response surfaces. A subset of the experiments termed as the training points are used to fit a combined numerical/experimental response. This numerical response is obtained as a result of numerical model correction via experiments. The quality of fit for this combined response is evaluated using the remaining validation points. Based on the quality of fit, the feasible design space is reduced for a given confidence interval using hypothesis testing. A convergence analysis of the framework quantifies the closeness of the corrected numerical model to the true system as a function of response estimation error. This design optimization framework, along with the convergence result, is validated through an airborne wind energy (AWE) application using a lab-scale water channel setup. The quality of flight is greatly improved by optimizing the center of mass location, pitch angle set point, horizontal and vertical stabilizer areas using an effective experimental infusion as compared to a pure numerically optimized design.

Structurally Consistent Class/Shape Transformation Equations for Morphing Airfoils

Camber morphing of the wing outer mold line may improve aerodynamic performance and reduce noise emissions for some aircraft. However, not all outer mold lines are obtainable from a given reference configuration as the extent to which the outer mold line can deform is restricted by both the reference airfoil shape and wing internal structure. In this Paper, the class/shape transformation parameterization method is modified for the first time to consider the kinematics of a morphing airfoil with rigid internal structure, providing structurally consistent class/shape transformation equations. Considering as an example the use of skin-based actuators and certain assumptions regarding the internal structure, accurate kinematically feasible deformed configurations are rapidly calculated without the need for structural analysis or consideration of actuation technology, enabling rapid iterative preliminary design optimization. The kinematic relations are accurate for moderate deformations (strains less than or equal to 4%) while still providing good estimates for larger deformations when comparing to finite-element analysis results.

Design of a novel instrument for active neutron interrogation of artillery shells.

The most common explosives can be uniquely identified by measuring the elemental H/N ratio with a precision better than 10%. Monte Carlo simulations were used to design two variants of a new prompt gamma neutron activation instrument that can achieve this precision. The instrument features an intense pulsed neutron generator with precise timing. Measuring the hydrogen peak from the target explosive is especially challenging because the instrument itself contains hydrogen, which is needed for neutron moderation and shielding. By iterative design optimization, the fraction of the hydrogen peak counts coming from the explosive under interrogation increased from % to % (statistical only) for the benchmark design. In the optimized design variants, the hydrogen signal from a high-explosive shell can be measured to a statistics-only precision better than 1% in less than 30 minutes for an average neutron production yield of 109 n/s.