Computational Design Optimization Research Articles

Computational Flow Prediction and Design Optimization of Vortex Generators for a Conventional Passenger Car

Vehicle aerodynamics is a vital physics in the design of an automotive vehicle. The main purpose of the automotive aerodynamics is to reduce drag force, wind noise, minimizing noise emission and preventing the undesired lift forces to avoid aerodynamic instability at high speeds. The shape of the vehicle body is to be designed in such a way that drag force caused by the pressure and viscous effects is less. The present analysis focuses on the drag reduction technique used for a passenger car \ using vortex generator. The main purpose of using Vortex Generators (VGs) is to disturb the airflow running over the car body and hence to produce a swirl of air in-between the high energy and low energy streams. In this paper, different shapes of vortex generators are considered and numerically investigated using Computational Fluid Dynamics (CFD). The flow characteristics around the car body with and without vortex generators are predicted and compared. The drag forces obtained using CFD simulations are compared with various vortex generator configurations. The present analysis also investigates the combined effect of spoiler with vortex generators and the change in aerodynamic characteristics of the car body is presented. The flow interference over the spoiler due to the presence of vortex generators is predicted to compare the changes in negative lift produced during high speeds. From the CFD analysis it is observed that there is less co-efficient of drag in ramped type VGs in comparison with other VGs.

Review of effects the rear spoiler aerodynamic analysis on ground vehicle performance

Recently reduce in fuel consumption and concern regarding the ground vehicle safety, automotive engineers are faced with the immediate task of introducing more efficient aerodynamic performance vehicles. Hence, the rear spoiler attached on the ground vehicle was presented. Rear spoiler is a device commonly found on road vehicle for improving their aerodynamic performance. Review on the research effects of the rear spoiler aerodynamic analysis on ground vehicle performance is reported in this paper. This review intends to enhance the stability and safety by attaching the rear spoiler on ground vehicle. The review mainly focus on aerodynamic analysis and control of the rear spoiler for different types ground vehicle in term of improving vehicle performance. Researches carried out by a number of researchers with regard to analyze and control the rear spoiler of ground vehicle and their effect on ground vehicle performance. Aerodynamic analysis i.e. computational fluid dynamics(CFD), design optimization, drag coefficient (Cd ) and lift coefficient (Cl ) and control methods i.e. passive control and active control had been reviewed. Meanwhile, progressive research on the rear spoiler aerodynamic analysis was observed due to its flexibility for wide range of application in the different type vehicles.

Multi-material structural topology optimization with decision making of stiffness design criteria

A new methodology for making design decisions of structures using multi-material optimum topology information is presented. Multi-material analysis contributes significant applications to enhance the bearing capacity and performance of structures. A method that chooses an appropriate material combination satisfying design stiffness requirement economically is currently needed. An alternative method of making design-decision is to utilize a multi-material topology optimization (MMTO) approach. This study provides a new computational design optimization procedure as a guideline to find the optimal multi-material design by considering structure strain energy and material cost. The MMTO problem is analyzed using an alternative active-phase approach. The procedure consists of three design steps. First, steel grid configurations and composite with material properties are defined as a given structure for automatic design decision-making (DDM). And then design criteria of the steel composites structure is given to be limited strain energy by designers and engineers. Second, topology changes in the automatic distribution of multi-steel materials combination and volume control of each material during optimization procedures are achieved and at the same time, their converged minimal strain energy is produced for each material combination. And third, the strain energy and material cost which is computed based on the material ratio in the combinations are used as design decision parameters. A study in constructional steel composites to produce optimal and economical multi-material designs demonstrates the efficiency of the present DDM methodology.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders.

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

A statistical design optimization study of a multi-chamber reactive type silencer using simplex centroid mixture design

This study aims to optimize the acoustic performance of a silencer with baffles having extension tubes. It considers the position, the number and the extension geometry of the baffles as design variables and sound transmission loss as the response variable to be optimized. The finite element analysis software ABAQUS is used to compute the response values for different combinations of design variables. The statistical design of the experiments provides a mathematical framework for such computer design optimization studies with multiple design variables. Yet, it has not been used for design optimization of silencers in the literature. In this study, simplex centroid mixture designs, a type of response surface method, are used in the statistical design of experiments. They can provide faster convergence on the optimization problem. The design involves one, two and three baffles with different positions and extension tube lengths. The outcome of this study indicates that obtaining ABAQUS software solutions at design points for each baffle number allows constructing nonlinear regression equations expressing the response variable as a function of the design variables. The equations obtained are then used to compute optimal values. Further evaluation of these equations indicates that better sound transmission loss values are obtained when the baffle number is increased, and the lengths of the extension tubes are set at high values. Moreover, it is possible to use the statistical experimental design approach implemented in this study for other types of silencers with different baffle geometries and design variables.

Computational Design Optimization: A Combined Darrieus-Savonius Water Turbine Design with a Modified Blade Position on Buckets

The aim of this study was to determine the optimization of a combined Darrieus-Savonius water turbine design with a modified blade position on buckets. To determine the right position of the blade on the buckets, an analysis of the cut plot pressure on the combined turbine was required. The analysis of the cut plot pressure usually uses the embedded analysis in SolidWorks software to improve the combined water turbine design. This research seeks to propose an additional analysis of the cut plot pressure using an image-processing approach. The image processing approach in the analysis of the cut plot pressure was done by clustering image-based segmentation using the Fuzzy C-Means algorithm. The combined water turbine design used the NACA 0018 blade profile and buckets. Modification of the blades’ position on the buckets can improve the turbines’ performance. Three variations of the blades’ position modifications were employed, and three variations of the turbine design were simulated by SolidWorks software to obtain the cut plot pressure, which, in the form of a digital image on each variation, was analyzed using the Fuzzy C-Means algorithm. Variations of the combined water turbine design were analyzed and tested using SolidWorks and MATLAB software. The results of the study indicate that the variations of the blades’ position on the buckets can increase the pressure of the combined water turbine.

Computational investigation and design optimization of a duct augmented wind turbine (DAWT)

Abstract Wind energy technologies are most emerging energy in the world because it is clean and available freely in the atmosphere and also it won’t produce any greenhouse gases. The present study aims to extract more power from natural source wind, for that Duct Augmented Wind Turbine (DAWT) techniques used. This technical analysis focuses on design and optimization of duct for horizontal axis wind turbine. For the analysis three diffuser ducts were chosen as follows, straight wind-lens, curved wind-lens and Vortex Generators (VGs) wind-lens. And also effect of brimmed wind-lens analyzed computationally. The present analyses shows improvement in power and mass flow-rate, particularly the curved wind lens and VGs assisted wind-lens produce more waves behind wind turbine and hence low pressure regions formed. From the CFD analysis it is observed there is more power generation in curved diffuser wind turbine.

Computational modelling of foot orthosis for midfoot arthritis: a Taguchi approach for design optimization

Evaluation of the internal biomechanics of the foot-and-ankle complex is challenging for the prescription of orthosis particularly for midfoot arthritis patients in which the joint condition is crucial. Using computational modeling and design optimization techniques, the objective of this study was to compare the biomechanical functions among different combinations of design factors using computer simulation. A finite element foot model was reconstructed from a midfoot arthritis patient. Orthotic designs with 3 levels for each of the 3 design factors (arch height, lateral wedge angle, and insole stiffness) contributed to 9 configurations using a fractional factorial design were tested. An increase in peak plantar stress of the midfoot was facilitated by a medium arch height and wedge angle, and stiffest insole material, notwithstanding the combination neither reduced the peak plantar stress of other foot regions nor was consistent with the combination that minimized the stress of the articular cartilage. Insole with high arch (H = 30 mm), low stiffness (E = 1.0 MPa), and medium wedge angle (A = 5°) could minimize the stress of the cartilage at the arthritic joint (primary outcome) and could be beneficial to the patients. Also, insole stiffness predominantly influenced cartilage stress. However, secondary outcomes including the stress of the navicular and medial cuneiform and the regional plantar stress did not produce the same solution. Future studies can consider a patient-specific loading profile to further the investigation on the stabilizing effect and the attenuation of load transfer induced by the insole.

Computational fluid dynamics analysis and design optimization of a porous annular powder‐liquid mixer

AbstractAn annular powder‐liquid mixer is a new type of jet pump used to mix powder. This mixer is mainly used to mix fracturing fluids in oil and natural gas exploitation processes. Free from powder pellets, the mixer has characteristics, such as high mixing efficiency and great suction. The structural parameters, such as hose diameter, exit section diameter, falloff angle, divergence angle, hose length‐diameter ratio, and annular nozzle diameter, may influence this new type of mixer. By using an orthogonal experiment to optimize the structure of an annular ring‐type powder mixer and performing a comparative analysis of the results of numerical simulations, this paper finally determines the mixer's structure. According to experiments, the mixer's negative suction can reach 0.09 MPa, there are no powder pellets during the mixing process, and the mixing effect is very good.

Fracture modeling of brittle biomaterials by the phase-field method

Biomaterials have been extensively used in prosthetic applications for their proven biocompatibility and osseointegration characteristics. Nevertheless, one of the critical issues of some synthetic biomaterials is brittleness prone to experience fracture failure due to low tensile strength and low fracture toughness. This study aims to employ a recently-developed phase-field model to simulate the crack propagation in brittle biomaterials. Unlike discrete fracture modeling methods, the phase-field approach allows simulating crack path in a continuous manner, thereby avoiding remeshing that may not be trivial for complicated fracture surfaces and facilitate iterative procedure commonly required for structural optimization. The phase-field model is formulated to treat the fracture path as a localized region of diffusive damage that can be described in terms of a phase-field function, in which the discreteness in cracked materials is assumed to be smeared. In this study, three representative case studies from the biomedical context, namely a zirconia-based dental bridge (or namely fixed partial denture (FPD)), a ceramic tissue scaffold and an analog saw-bone femur, are employed as illustrative examples. The phase-field modeling results are compared with the in-house experimental tests, demonstrating the effectiveness of the phase-field technique for predicting brittle fracture failure in several typical biomedical case scenarios. The phase-field model provides a useful tool for the computational fracture analysis and design optimization of other brittle biomaterials.

Computational design optimization of a smart material shape changing building skin tile

The development and evaluation of a computational approach for optimal design of a smart material shape changing building skin is presented and numerically evaluated. Specifically, a unique shape-based approach is utilized to create an optimization approach to identify the activation and actuation mechanisms to minimize the difference between a desired shape and the estimated morphed shape. Three potential metrics of shape difference are considered and their capability to facilitate an efficient optimization process leading to accurate shape matching is evaluated. Details of the optimal design framework are presented, particularly focusing on the shape difference metrics as well as the strategy to parameterize the activation of the smart material. In particular, the parameterization strategy is a unique approach to easily integrate controllable localized activation within a smart material structure in a generally applicable way that does not limit the design search space. A series of numerical design examples are presented based on the concept of a smart material (e.g., shape memory polymer) shape changing tile that can be activated and actuated in a variety of ways to achieve desirable surface wrinkle patterns. These numerical design examples are applied to both 2D and 3D problems and consider a variety of parameterizations and target shapes. Results indicate that the shape-based approach can consistently determine the mechanisms of morphing needed to accurately match a target shape. Furthermore, it is shown that localized material activation can lead to not only a more accurate shape but also requires less energy and actuation devices to do so.

Protein Structure Analysis: Introducing Students to Rational Drug Design

We describe a series of engaging exercises in which students emulate the process that researchers use to efficiently develop new pharmaceutical drugs, that of rational drug design. The activities are taken from a three- to four-hour workshop regularly conducted with first-year college students and presented here to take place over three to four class periods. Although targeted at college students, these activities may be appropriate at the high school level as well, particularly in an AP Biology course. The exercises introduce students to the topics of bioinformatics and computer modeling, in the context of rational drug design, using free online resources such as databases and computer programs. Through the process of learning about computational drug design and drug optimization, students also learn content such as elements of protein structure and protein–ligand interactions. Based on our assessment, students enjoy the exercises, become more interested in bioinformatics and computer modeling, and demonstrate an increase in content knowledge relevant to the topics.

Digital to physical development of a reconfigurable modular formwork for concrete casting and assembling of a shell structure

This paper demonstrates an integrated computational design and fabrication workflow, implemented in actual construction scale, in order to test its feasibility towards a productive and customizable manufacturing process. The project suggests the computational development and the semi-automated fabrication of a free-form shell structure that consists of customized concrete members. The development process is based on two pillars of investigation that are bi-directionally connected; the computational design optimization and the fabrication. The first part of investigation refers to the form-finding of the overall shell structure, as well as its structural members by using topology optimization principles. The second part deals with the development of a reconfigurable modular formwork, which adapts to all shape alternations of the structural components, enabling their effective fabrication by using a single mechanism. Within this framework, decisions taken in the optimization stage are evaluated based on limitations and potentials of the suggested formwork, whose reconfigurability influences fabrication outcomes and vice versa. • Review on computational design optimization and real scale fabrication • Reconfigurable formwork development for concrete casting of customized elements • Strategy for computational design of shell structures based on topology optimization • Evaluation of structural efficiency and material minimization • Structural elements prototyping to validate the suggested workflow

A structural and data-driven approach to engineering a plant cytochrome P450 enzyme.

Functional manipulation of biosynthetic enzymes such as cytochrome P450s (or P450s) has attracted great interest in metabolic engineering of plant natural products. Cucurbitacins and mogrosides are plant triterpenoids that share the same backbone but display contrasting bioactivities. This structural and functional diversity of the two metabolites can be manipulated by engineering P450s. However, the functional redesign of P450s through directed evolution (DE) or structure-guided protein engineering is time consuming and challenging, often because of a lack of high-throughput screening methods and crystal structures of P450s. In this study, we used an integrated approach combining computational protein design, evolutionary information, and experimental data-driven optimization to alter the substrate specificity of a multifunctional P450 (CYP87D20) from cucumber. After three rounds of iterative design and evaluation of 96 protein variants, CYP87D20, which is involved in the cucurbitacin C biosynthetic pathway, was successfully transformed into a P450 mono-oxygenase that performs a single specific hydroxylation at C11 of cucurbitadienol. This integrated P450-engineering approach can be further applied to create a de novo pathway to produce mogrol, the precursor of the natural sweetener mogroside, or to alter the structural diversity of plant triterpenoids by functionally manipulating other P450s.

Multi-Objective Design Optimization of a Shape Memory Alloy Flexural Actuator

This paper presents a computational model and design optimization strategy for shape memory alloy (SMA) flexural actuators. These actuators consist of curved SMA wires embedded within elastic structures; one potential application is positioning microcatheters inside blood vessels during clinical treatments. Each SMA wire is shape-set to an initial curvature and inserted along the neutral axis of a straight elastic member (cast polydimethylsiloxane, PDMS). The elastic structure preloads the SMA, reducing the equilibrium curvature of the composite actuator. Temperature-induced phase transformations in the SMA are achieved via Joule heating, enabling strain recovery and increased bending (increased curvature) in the actuator. Actuator behavior is modeled using the homogenized energy framework, and the effects of two critical design parameters (initial SMA curvature and flexural rigidity of the elastic sleeve) on activation curvature are investigated. Finally, a multi-objective genetic algorithm is utilized to optimize actuator performance and generate a Pareto frontier, which is subsequently experimentally validated.

High-Fidelity Design-Allocation Optimization of a Commercial Aircraft Maximizing Airline Profit

Traditionally, computational design optimization of commercial aircraft is performed by considering a small number of representative operating conditions. These conditions are based on the design Mach number, altitude, payload, and range for which the aircraft will be flown. However, the design also influences which routes and mission parameters are optimal, and so there is coupling that is ignored when using the traditional approach. Here, the aircraft design, mission profiles, and the allocation of aircraft to routes in an airline network are simultaneously optimized. This is a mixed-integer nonlinear programming problem that is reformulated as a nonlinear programming problem because of the large number of design variables. The reformulated problem is solved using a gradient-based optimization approach with a parallel computational framework that facilitates the multidisciplinary analysis and the derivative computation. A surrogate model is used for the computational fluid dynamics analysis that is retrained in each optimization iteration given the new set of shape design variables. The resulting optimization problem contains over 4,000 design variables and close to 14,000 constraints. The optimization results show a 2% increase in airline profit compared with the traditional multipoint optimization approach. The wing area increases to the upper bound, enabling a higher cruise altitude that improves propulsive efficiency. This study finds that simultaneously optimizing the allocation, mission, and design to maximize airline profit results in a different optimized wing design from that resulting from the multipoint optimization approach.

Computational Design Optimization for S-Ducts

In this work, we investigate the computational design of a typical S-Duct that is found in the literature. We model the design problem as a shape optimization study. The design parameters describe the 3D geometrical changes to the shape of the S-Duct and we assess the improvements to the aerodynamic behavior by considering two objective functions: the pressure losses and the swirl. The geometry management is controlled with the Free-Form Deformation (FFD) technique, the analysis of the flow is performed using steady-state computational fluid dynamics (CFD), and the exploration of the design space is achieved using the heuristic optimization algorithm Tabu Search (MOTS). The results reveal potential improvements by 14% with respect to the pressure losses and by 71% with respect to the swirl of the flow. These findings exceed by a large margin the optimality level that was achieved by other approaches in the literature. Further investigation of a range of optimum geometries is performed and reported with a detailed discussion.

Optimization of Backward-Facing Step Flow Control Using Dielectric Barrier Discharge Plasma Actuators

In the present paper, a numerical study on the flow control over a backward facing step using dielectric barrier discharge (DBD) plasma actuators has been conducted by means of high-fidelity computational fluid dynamic and multi-objective design optimization (MDO) based on surrogate-assisted evolutionary algorithms. The main objectives of this study are minimizing total pressure loss and reattachment length while maximizing the flow uniformity index. It employs four decision variables including input voltage, frequency, width of generated plasma and distance from the flow inlet to the start of the generated plasma. Sensitivity analysis has been performed with the aid of surrogate modeling; the results have been evaluated through MDO with evolutionary algorithms. It has revealed major impact of the DBD plasma actuator on the behavior of the flow and major improvements on the objective functions in relation to the decision variables. In particular, flow separation has been suppressed considerably while maintaining reasonable levels of flow uniformity and total pressure loss.

Multi-disciplinary and multi-objective optimization problem re-formulation in computational design exploration: A case of conceptual sports building design

Abstract The benefits of applying multi-objective optimization (MOO) in building design have been increasingly recognized in recent decades. The existing or traditional computational design optimization (CDO) approaches mostly focus on optimization problem solving (OPS), as they often conduct optimizations directly by assuming the optimization problems in question are good enough. In contrast, the computational design exploration (CDE) approaches defined in this research mainly focus on optimization problem formulation (OPF), which are considered more essential and aim to achieve or ensure appropriate optimization problems before conducting optimizations. However, the application of the CDE is very limited especially in conceptual architectural design. The necessity of re-formulating original optimization problems and its potential impacts on optimization results are often overlooked or not emphasized enough. This paper proposes a new CDE approach that highlights the knowledge-supported re-formulation of a changeable initial optimization problem. It improves upon the traditional CDO approach by introducing a changeable initial OPF and inserting a CDE module. The changeable initial OPF allows expanding the dimensionality of an objective space and design space being investigated, and the CDE module can re-formulate the changeable optimization problem using the information and knowledge extracted from statistical analyses. To facilitate designers in achieving the proposed approach, an improved computational platform is used which combines parametric modeling software (including simulation plug-ins) and design optimization software. Assisted by the platform, the proposed approach is applied to the conceptual design of an indoor sports building that considers multi-disciplinary performance criteria (including architecture-, climate- and structure-related criteria) and a wide range of geometric variations. Through the case study, this paper demonstrates the use of the proposed approach, verifies its benefits over the traditional method, and unveils the factors that may affect the behaviour of the proposed approach. Besides, it also shows the suitability of the computational platform used.

Methanol activation catalyzed by Pt7, Pt3Cu4, and Cu7 clusters: A density functional theory investigation

Metal clusters were considered as excellent catalysts for methanol dissociation. In this work, two main decomposition mechanisms of methanol on Pt7, Pt3Cu4, and Cu7 clusters were investigated by the density functional theory. One was methanol direct dehydrogenation, and the other was non‐CO‐involved oxidation. Stable adsorption configurations, elementary reaction barriers, the potential energy surface (PES), and the charge analysis were elucidated. The results showed that on Pt7 cluster, methanol was favorable for direct decomposition. On Pt3Cu4 and Cu7 clusters, methanol was inclined to the pathway of non‐CO‐involved oxidation. All the transition‐state energies and the final‐state energies were related in a linear, including those for the clusters. The results may be useful for computational design and catalysts optimization.