Design Optimization Strategy Research Articles

Actuation system modelling and design optimization for an assistive exoskeleton for disabled and elderly with series and parallel elasticity.

The aim of a robotic exoskeleton is to match the torque and angular profile of a healthy human subject in performing activities of daily living. Power and mass are the main requirements considered in the robotic exoskeletons that need to be reduced so that portable designs to perform independent activities by the elderly users could be adopted. This paper evaluates a systematic approach for the design optimization strategies of elastic elements and implements an actuator design solution for an ideal combination of components of an elastic actuation system while providing the same level of support to the elderly. A multi-factor optimization technique was used to determine the optimum stiffness and engagement angle of the spring within its elastic limits at the hip, knee and ankle joints. An actuator design framework was developed for the elderly users to match the torque-angle characteristics of the healthy human with the best motor and transmission system combined with series or parallel elasticity in an elastic actuator. With the optimized spring stiffness, a parallel elastic element significantly reduced the torque and power requirements up to 90% for some manoeuvres for the users to perform ADL. When compared with the rigid actuation system, the optimized robotic exoskeleton actuation system reduced the power consumption of up to 52% using elastic elements. A lightweight, smaller design of an elastic actuation system consuming less power as compared to a rigid system was realized using this approach. This will help to reduce the battery size and hence the portability of the system could be better adopted to support elderly uses in performing daily living activities. It was established that parallel elastic actuators (PEA) can reduce the torque and power better than series elastic actuators (SEA) in performing everyday tasks for the elderly.

Low-cost photoreactors for highly photon/energy-efficient solar-driven synthesis

Low-cost photoreactors for highly photon/energy-efficient solar-driven synthesis

Identification of CDRH3 loops in the B cell receptor repertoire that can be engaged by candidate immunogens.

A major goal for the development of vaccines against rapidly mutating viruses, such as influenza or HIV, is to elicit antibodies with broad neutralization capacity. However, B cell precursors capable of maturing into broadly neutralizing antibodies (bnAbs) can be rare in the immune repertoire. Due to the stochastic nature of B cell receptor (BCR) rearrangement, a limited number of third heavy chain complementary determining region (CDRH3) sequences are identical between different individuals. Thus, in order to successfully engage broadly neutralizing antibody precursors that rely on their CDRH3 loop for antigen recognition, immunogens must be able to tolerate sequence diversity in the B cell receptor repertoire across an entire vaccinated population. Here, we present a combined experimental and computational approach to identify BCRs in the human repertoire with CDRH3 loops predicted to be engaged by a target immunogen. For a given antibody/antigen pair, deep mutational scanning was first used to measure the effect of CDRH3 loop substitution on binding. BCR sequences, isolated experimentally or generated in silico, were subsequently evaluated to identify CDRH3 loops expected to be bound by the candidate immunogen. We applied this method to characterize two HIV-1 germline-targeting immunogens and found differences in the frequencies with which they are expected to engage target B cells, thus illustrating how this approach can be used to evaluate candidate immunogens towards B cell precursors engagement and to inform immunogen optimization strategies for more effective vaccine design.

Study on Camping Behavior Patterns for Thermal Comfort at Riverside Parks

People-oriented landscape experiences have become the focus of park design with the increasing demand of outdoor activities from urban residents, whereas thermal-comfort-guided landscape design has attracted more attention in academic circles. Based on the investigation of the microclimate of typical subregions in riverside parks, this paper uses AI recognition to analyze the spatiotemporal distribution of camping crowds, and considers the correlation between landscape morphological parameters, microclimates, and crowd behavior. Finally, we built a model to raise the number of landscape optimization strategies for landscape design. The results show that landscape morphological parameters, such as tree height, crown canopy, and sky visibility factor (SVF), can significantly affect the air temperature (Ta), relative humidity (RH), and physiological temperature (PET) in an environment, while hardly affecting wind speed (WS). For microclimate parameters, Ta has a moderate correlation with camping behavior, with a correlation coefficient of −0.145 and a p-value of 0.040, while the corresponding correlation with PET was non-significant with a p-value of 0.622. The temporal distribution of the number of campers per day show a linear upward trend with a reasonable goodness-of-fit, with an adjusted R2 above 0.789 for all subregions. The model based on landscape morphological parameters has a good fit, with coefficients of tree height and crown canopy of −0.195 and 1.316, respectively. This study provides theoretical support and design suggestions for the design of riverside parks based on crowd behavior patterns.

Multi-objective Design Optimization and Control Strategy for Digital Hydraulically Driven Knee Exoskeleton

This article presents a multi-objective design optimization strategy to determine an optimal design of digital hydraulically driven knee exoskeleton. To satisfy the overall goal of compact and lightweight design, four key design objectives are defined. Via genetic algorithm based multi-objective optimization technique, the pareto-optimal set of designs is determined and the trade-offs between the design objectives are analysed. Via decisions based on component availability and user-comfort, the dimensionality of the pareto-front is reduced to two and an exoskeleton design is selected that offers a good compromise between the design objectives. For the actuation of the exoskeleton, an energy efficient control strategy is proposed which consists of using passive control during the stance phase and simplified model predictive control during the swing phase. The operation of the chosen knee exoskeleton design and the control strategy is investigated via numerical simulations. The results indicate that the exoskeleton successfully tracks the desired knee motion and delivers the required knee torque.

Investigation of Economical Design Strategies for the Solid Oxide Fuel Cell Afterburner Based on Computational Fluid Dynamics Simulation

This study simulates the computational fluid dynamics (CFD) to explore the design optimization strategies of the solid oxide fuel cell (SOFC) afterburner. The CFD modeling with combustion is carried out with the eddy dissipation concept (EDC), applying a three-step rate-dependent reaction mechanism to predict the determining step of the combustion reactions by changes of the control and design parameters. The implemented CFD model is validated by comparison of the precedent experimental data and CFD studies. The simulation results examine the temperature distribution and reactivity of the fuel inside the SOFC afterburner with design parameters at start-up operation, such as the equivalence ratio and swirl number. In particular, the results of the parametric study show the possibility of design optimization to minimize the wall temperature and constraints to reduce the emission of toxicants (i.e., CO and NOx). The alternative burner design derived from the parametric study, when starting the SOFC system, has been proven to keep on the stable thermal stress and fuel reactivity, despite lowering the maximum wall temperature to 80.9%, compared to the conventional burner design.

Recent Approaches to the Modification of Collagen Biomatrix as a Corneal Biomatrix and Its Cellular Interaction

Over the last several decades, numerous modifications and advancements have been made to design the optimal corneal biomatrix for corneal epithelial cell (CECs) or limbal epithelial stem cell (LESC) carriers. However, researchers have yet to discover the ideal optimization strategies for corneal biomatrix design and its effects on cultured CECs or LESCs. This review discusses and summarizes recent optimization strategies for developing an ideal collagen biomatrix and its interactions with CECs and LESCs. Using PRISMA guidelines, articles published from June 2012 to June 2022 were systematically searched using Web of Science (WoS), Scopus, PubMed, Wiley, and EBSCOhost databases. The literature search identified 444 potential relevant published articles, with 29 relevant articles selected based on inclusion and exclusion criteria following screening and appraising processes. Physicochemical and biocompatibility (in vitro and in vivo) characterization methods are highlighted, which are inconsistent throughout various studies. Despite the variability in the methodology approach, it is postulated that the modification of the collagen biomatrix improves its mechanical and biocompatibility properties toward CECs and LESCs. All findings are discussed in this review, which provides a general view of recent trends in this field.

Design, Control, and Experimental Evaluation of A Novel Robotic Glove System for Patients with Brachial Plexus Injuries.

This paper presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of Linear Series Elastic Actuators (LSEA), Rotary Series Elastic Actuators (RSEA), and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from 3 subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.

Robust Design Optimization of Expensive Stochastic Simulators Under Lack-of-Knowledge

Abstract Robust design optimization of stochastic black-box functions is a challenging task in engineering practice. Crashworthiness optimization qualifies as such problem especially with regards to the high computational costs. Moreover, in early design phases, there may be significant uncertainty about the numerical model parameters. Therefore, this paper proposes an adaptive surrogate-based strategy for robust design optimization of noise-contaminated models under lack-of-knowledge uncertainty. This approach is a significant extension to the robustness under lack-of-knowledge method (RULOK) previously introduced by the authors, which was limited to noise-free models. In this work, it is proposed to use a Gaussian Process as a regression model based on a noisy kernel. The learning process is adapted to account for noise variance either imposed and known or empirically learned as part of the learning process. The method is demonstrated on three analytical benchmarks and one engineering crashworthiness optimization problem. In the case studies, multiple ways of determining the noise kernel are investigated: (1) based on a coefficient of variation, (2) calibration in the Gaussian Process model, (3) based on engineering judgment, including a study of the sensitivity of the result with respect to these parameters. The results highlight that the proposed method is able to efficiently identify a robust design point even with extremely limited or biased prior knowledge about the noise.

Concurrent multi-material and multi-scale design optimization of fiber-reinforced composite material and structures for minimum structural compliance

With the development of additive manufacturing technology for fiber-reinforced composite materials, topology optimization of fiber-reinforced composite laminates involving multiple materials and variable stiffness is gaining increasing attention. This study proposes an effective methodology for a Multi-scale and Multi-material Composite Anisotropic Penalization (MMCAP) model to investigate multi-scale and multi-material design optimization of a fiber-reinforced variable stiffness (VS) composite structure to minimize structural compliance. In the concurrent MMCAP model, the macroscopic multi-material structural topology and microscopic discrete fiber laying angle selection are introduced as independent design variables and optimized simultaneously. The modified Solid Isotropic Material with Penalization (SIMP) and the Discrete Material Optimization (DMO) approaches are utilized at the macro- and micro-scales, respectively, to realize a clear macroscopic multi-material structural topology and microscopic specific discrete fiber laying angle selection. Multi-material fiber-reinforced plastic (FRP) materials, such as carbon fiber–reinforced plastic (CFRP) and glass fiber–reinforced plastic (GFRP), are considered two types of solid materials in terms of structural volume cost. Sensitivity analysis of the structural compliance concerning the variables of the two geometrical scales is performed using the analytical sensitivity analysis method. The DMO approach is utilized to couple two geometrical scales: macroscopic topology and microscopic material selection. The capabilities of the proposed MMCAP are demonstrated by concurrent multi-material and multi-scale design optimization of composite panels. The influence of the number of discrete fiber laying angles on the structural compliance and optimized topology configuration is also been discussed. Numerical studies showed that the proposed MMCAP scheme can effectively realize multi-material and multi-scale design optimization of fiber-reinforced composite structure with achieving a clear macroscopic multi-material structural topology and microscopic fiber laying angle. The proposed MMCAP scheme provides a new implementation strategy for lightweight, multi-material, and multi-scale design optimization of composite materials, considering the design and manufacturing collaboration through additive manufacturing technology.

Multi-fidelity robust design optimization of an ORC turbine for high temperature waste heat recovery

The ORC technology is subject to manifold sources of uncertainty that can have a severe impact on the thermodynamic and economic efficiency of plant components, particularly when the system is operated at off-design conditions. In this contribution we focus on the development of ORC turbines with stable performance under uncertainty: a novel multi-fidelity robust design optimization (RDO) strategy is used to design the first nozzle of an ORC turbine for high temperature waste-heat recovery. For this kind of application, the turbine inlet and outlet conditions may vary randomly over a large range. The RDO strategy combines parsimonious uncertainty quantification techniques with a multi-objective genetic algorithm optimizer based on surrogate models. The multi-fidelity approach allows to estimate with high accuracy and with a low computational cost the statistical moments of the probability distribution function of the quantity of interest, which here is the entropy generation within the cascade. To improve the accuracy of the surrogate model coupled with the optimizer, the multi-objective expected improvement criterion is adopted. The optimization converges to an efficient optimum solution, ensuring improved and stable performance over the whole considered range of uncertain operating conditions and with a computational cost that is significantly lower than other RDO approaches proposed in literature.

Research on Architectural Design Innovation from a Low Carbon Perspective

With the improvement of living standards, people's requirements for residential functions are also increasing, that is, residential buildings should not only provide a safe and comfortable living environment for people, but also meet the low-carbon and energy-saving requirements of construction projects. Based on this, the article elaborates on the advantages of architectural design under the low-carbon concept, introduces the current development status of architectural design under the low-carbon concept, and proposes optimization strategies for architectural design under the low-carbon concept. It is necessary to use low-carbon environmental protection energy and building materials, optimize the surrounding environment of buildings, and fully utilize renewable energy, aiming to make strong contributions to the construction of ecological environment in China.

Optimization Strategies for Removable Partial Denture Design: A Literature Review

Optimization Strategies for Removable Partial Denture Design: A Literature Review

Comparative Research of Chinese and American Game Mechanism Design

The video game industry is one of the most influential entertainment industries, which has been generating high revenue and developing rapidly in recent years. However, in recent years, social problems caused by video games have emerged in China. This article proposes that game mechanism is an important kernel of game design and has a crucial influence on the social significance of games. By comparing China and the United States, the article analyzes the history of game mechanism design in both sides, draws the reasons for the healthy development of the game industry in America and uncovers the problems of the game industry in China, and proposes optimization strategies for game mechanism design in China in combination with the current situation.

Design Optimization Strategy for Service Shared Communities Based on Citespace Application

Design Optimization Strategy for Service Shared Communities Based on Citespace Application

A novel Kriging-model-assisted reliability-based multidisciplinary design optimization strategy and its application in the offshore wind turbine tower

In Reliability-based Multidisciplinary Design Optimization (RBMDO), the key performance functions of wind turbine are usually implicit, which means the performance response can only be obtained through time-consuming Physics Experiment (PE) or Finite Element Analysis (FEA). However, for practical engineering, the computational cost of repeatedly using PE or FEA is prohibitive. To tackle this challenge, in this study, an adaptive Kriging-model-assisted RBMDO strategy is proposed. The novel updated-strategy for performance function in RBMDO is discussed to find effective training samples of active learning for Kriging model. Also, a powerful decoupling strategy of RBMDO is introduced and combined with the proposed method to enhance computational efficiency further. Two case studies, including a mathematic example and a hydraulic turbine rotor bracket design example, are utilized to illustrate the advantage of the given strategy. Finally, the proposed method is applicated into an engineering design of 5 MW offshore wind turbine tower to ensure its reliability and safety.

Hollow multi-shell SiC@C@PANI nanoparticles with broadband microwave absorption performance

The hollow multi-shell structure has been used as a basic concept for the design and preparation of microwave absorbing (MA) materials. Herein, we prepared novel hollow SiC@C@PANI (SCP) nanoparticles by a two-step in situ polymerization method, template method and carbon thermal reduction method. By coating different proportions of polyaniline (PANI) on the surface of SiC@C microspheres to regulate the impedance matching and reduce the escape of electromagnetic waves, the suitable dielectric loss and impedance matching reach an optimal balance, the multilayer heterogeneous interface significantly improves the polarization relaxation behavior, and the hollow structure enhances the multiple reflections and scattering of electromagnetic waves. Finally, the effective absorption bandwidth (EAB) of SCP-1:1.5 reached 5.61 GHz at only 1.88 mm. Moreover, we designed and optimized the macroscopic structure of the MA metamaterial through gradient modulation, and successfully broadened the EAB to 10 GHz by introducing multiple interferences, which completely covers the X, Ku bands. Simultaneous optimization strategies for microscopic design and macroscopic regulation of hollow multi-shell absorbers provide ideas for breaking the performance boundaries of MA materials.

A Reliability-Based Multidisciplinary Design Optimization Strategy considering Interval Uncertainty Based on the Point-Infilled Kriging Model

Generally, traditional uncertainty design optimization (UDO) methods are based on probability density distribution function or fuzzy membership function. In this situation, a large amount of uncertain information is necessary to construct the UDO model accurately. While, the interval UDO methods require less design information. Only the upper and lower bounds of interval uncertainty are utilized to construct the optimization model. In this study, to enhance the efficiency and accuracy of UBDO considering interval uncertainty, a reliability-based multidisciplinary design optimization (RBMDO) strategy using the point-infilled Kriging model is proposed. In the given method, a double-nested RBMDO model considering interval uncertainty is established. The collaborative optimization is utilized to deal with coupling relationships among complex systems. Then, the point-infilled Kriging response surface strategy is introduced to approximate the RBMDO model. The procedure of the interval multidisciplinary collaborative optimization method based on the Kriging model is discussed. Two examples are given to illustrate the application of the proposed method.

A Design Methodology for the Seismic Retrofitting of Existing Frame Structures Post-Earthquake Incident Using Nonlinear Control Systems

A structural design methodology for retrofitting weakened frame systems following earthquakes is developed and presented. The design procedure refers to frame systems in their degraded strength and stiffness states and restores their dynamic performance using nonlinear control systems. The control law associated with the employed systems regards the gains between the negative state feedback and the control force, which consists of linear, nonlinear, and hysteretic portions. Structural optimization is introduced in designing the nonlinear control systems, and the controller gains are optimized using the fixed-point iteration to improve the frame system’s dynamic performance. The fixed-point iteration method relates to first-order PDE equations; hence, a new state-space formulation for weakened inelastic frame systems is developed and presented using the frame system’s lateral force equilibrium equation. The design scheme and optimization strategy differ from designing passive control systems, given that the nonlinear control system’s force consists of linear, nonlinear, and hysteretic portions. The utilization of the fixed-point iteration in the structural design area is by itself a novel application due to its robustness in addressing the gains of any type of nonlinear control system. This paper’s nonlinear control system chosen to exhibit the application is Buckling Restrained Braces (BRBs) since force consists of linear and hysteretic portions. The implementation of hysteretic control force is rare in structural control applications. In the case of BRBs, the fixed-point iteration optimizes the cross-sectional areas. Two system optimization examples of 3-story and 15-story inelastic frames are provided and described. The examples demonstrate the fixed-point iteration’s applicability and robustness in optimizing control gains of nonlinear systems and regulating the dynamic response of weakened frame structures.

Multi-physics modeling of piezoelectric energy harvesters from vibrations for improved cantilever designs

This study investigated piezoelectric cantilevers for energy harvesting using multi-physics modeling. The cantilevers with multiple degree-of-freedoms (DOFs) were designed with higher potential to match multiple-frequency vibrations of structures. Finite element models (FEMs) of cantilevers with different design parameters were built and verified with laboratory measurements. As results, multiple vibration modes with different bending conditions were captured by FEM outputs under resonant frequencies. The parametric analysis was performed to analyze the effects of cantilever length, width, thickness and mass. It was found that adjusting the cantilever design acquired the resonant frequencies in a wide range of 5 Hz–35 Hz, which potentially fitted the vibration scenario of a typical bridge structure. Under the cantilever design principle proposed in this study, as the DOF was increased, the maximum voltage outputs or the power outputs still remained the same level, respectively at 30 V or 5 mW, with no extra coverage area required from the multiple-DOF cantilever. All trends of resonant frequency changes via design parameter adjustments captured in this study will also serve as references for achieving specific design optimization strategies on the multiple-DOF cantilever designs, which will be varied based on the structure vibration scenarios in the field.