Parametric Design Research Articles

Parametric Design Optimization of Convergent Textured Thrust Bearing in THD Behaviour

Textured thrust bearing is a technological means capable of providing higher performance compared to untextured bearing. This paper presents a parametric design optimization with thermohydrodynamic (THD) behaviour of textured thrust bearing in a range of convergence ratios. The bearing consists of eight pads (stator), film fluid and a collar (rotor). The main objective of this optimization is to provide higher load capacity, to relatively minimize the maximum temperature of the pad and the friction torque. To this aim, we perform a 3D computational fluid dynamics model for convergent thrust bearing with individual angular sector shaped texturing with a flat bottom profile pad, by using ANSYS. CFX, and considered a thermal effect.

A Thermo-Structural Analysis of Die-Sinking Electrical Discharge Machining (EDM) of a Haynes-25 Super Alloy Using Deep-Learning-Based Methodologies

The most effective and cutting-edge method for achieving a 0.004 mm precision on a typical material is to employ die-sinking electrical discharge machining (EDM). The material removal rate (MRR), tool wear rate (TWR), residual stresses, and crater depth were analyzed in the current study in an effort to increase the productivity and comprehension of the die-sinking EDM process. A parametric design was employed to construct a two-dimensional model, and the accuracy of the findings was verified by comparing them to prior research. Experiments were conducted utilizing the EDM machine, and the outcomes were assessed in relation to numerical simulations of the MRR and TWR. A significant temperature disparity that arises among different sections of the workpiece may result in the formation of residual strains throughout. As a consequence, a structural model was developed in order to examine the impacts of various stress responses. The primary innovations of this paper are its parametric investigation of residual stresses and its use of Haynes 25, a workpiece material that has received limited attention despite its numerous benefits and variety of applications. In order to accurately forecast the output parameters, a deep neural network model, more precisely, a multilayer perceptron (MLP) regressor, was utilized. In order to improve the precision of the outcomes and guarantee stability during convergence, the L-BFGS solver, an adaptive learning rate, and the Rectified Linear Unit (ReLU) activation function were integrated. Extensive parametric studies allowed us to determine the connection between key inputs, including the discharge current, voltage, and spark-on time, and the output parameters, namely, the MRR, TWR, and crater depth.

Mechanical properties of additively manufactured lattice structures designed by deep learning

Lattice structures, characterized by their repetitive lightweight cellular forms, enable more effective load distribution compared to solid bodies. Designing lattice structures with tailored mechanical properties remains challenging due to the numerous design variables and their complex relationship with mechanical performance. This paper presents a novel approach employing a deep learning-based Generative Adversarial Network (GAN) model to address this engineering challenge. With its potential for creativity and innovation, GAN provides design diversity that cannot be achieved with traditional design methods or other generative design models. Distinct from previous studies, the GAN training data set consists of lattice structures with improved mechanical properties obtained using parametric design and simulated annealing method. This data set enables the GAN model to create lattice structures with high strength-to-weight ratio. These lattice designs were fabricated using a commercial Material Jetting Additive Manufacturing (MJ-AM) machine, allowing for the production of complex structures. The mechanical performance of the 3D-printed unit cell samples was evaluated through Finite Element Analysis (FEA), compression, and impact testing. The results reveal that the lattice structures generated using the GAN model demonstrated improved mechanical strength (i.e. up to 108 % and 150 % improved strength and elongation performance, respectively). This study shows AI's potential to widen lattice structure design space and create tailored parts with improved mechanical properties. The research also paves the way for future exploration of deep learning techniques in revolutionizing the design and fabrication of parts with tailored mechanical properties.

Comparison of fast daylighting climate-based simulation methods for parametric design: two-phase, three-phase method and path tracing

ABSTRACT Computer modeling and simulation are essential for predicting architectural solutions performance. When integrated into the design process; they are known as performance-based design. During the design process, fast analytical cycles are required. However, daylighting simulations are challenging due to computational processing and time requirements. This paper presents a comparative study of fast daylight simulation methods, two-phase, three-phase and path tracing, in a hypothetical commercial building located in Miami, Florida. Five types of static glass and one electrochromic glass were tested with six parametric models of a translucent horizontal shading device. Daysim was used as reference. The two-phase method produced results similar to the reference results with faster processing times for static facades. For dynamic facades, the path tracing method with the tested interface enabled faster simulations and parametric variations. However, the three-phase method yielded a lower average error.

A conic programming approach to the wrinkling of pneumatic membranes using convex potentials

This paper presents a novel conic programming approach for modeling the wrinkling of isotropic hyperelastic membranes subject to nonlinear loading and finite strains, employing convex potentials derived from tension field theory. The incompressible neo-Hookean strain energy is recast as a minimization problem over a set of cones, with a semidefinite constraint on the deformed surface metric. To address pneumatic membranes, a linearized volumetric potential is introduced, reestablishing convexity for follower forces, and Boyle's law is expressed as a minimization problem over the exponential cone.The primal–dual interior point method solves the total potential energy minimization problem, with convergence verified using the analytical solution of a sheared planar membrane. The proposed model, applied to examples of increasing complexity, reveals intriguing membrane behaviors rarely discussed in existing literature. The method is shown to be robust, even when handling highly wrinkled membranes, which are challenging to address using direct nonlinear equilibrium analysis or nonlinear interior point methods.Implementation using high-level automatic code generation tools (FEniCS) results in concise, extensible code. The findings point to several avenues for future research, such as exploring complex material models, mesh refinement, dynamics, and parametric design. Additionally, the linearization process implies potential applicability of the methods to nonlinear problems beyond membrane mechanics.

The Laser Selective Sintering Controlled Forming of Flexible TPMS Structures.

Sports equipment crafted from flexible mechanical metamaterials offers advantages due to its lightweight, comfort, and energy absorption, enhancing athletes' well-being and optimizing their competitive performance. The utilization of metamaterials in sports gear like insoles, protective equipment, and helmets has garnered increasing attention. In comparison to traditional truss and honeycomb metamaterials, the triply periodic minimal surface lattice structure stands out due to its parametric design capabilities, enabling controllable performance. Furthermore, the use of flexible materials empowers this structure to endure significant deformation while boasting a higher energy absorption capacity. Consequently, this study first introduces a parametric method based on the modeling equation of the triply periodic minimal surface structure and homogenization theory simulation. Experimental results demonstrate the efficacy of this method in designing triply periodic minimal surface lattice structures with a controllable and adjustable elastic modulus. Subsequently, the uniform flexible triply periodic minimal surface lattice structure is fabricated using laser selective sintering thermoplastic polyurethane technology. Compression tests and finite element simulations analyze the hyperelastic response characteristics, including the element type, deformation behavior, elastic modulus, and energy absorption performance, elucidating the stress-strain curve of the flexible lattice structure. Upon analyzing the compressive mechanical properties of the uniform flexible triply periodic minimal surface structure, it is evident that the structure's geometric shape and volume fraction predominantly influence its mechanical properties. Consequently, we delve into the advantages of gradient and hybrid lattice structure designs concerning their elasticity, energy absorption, and shock absorption.

Teaching parametric design: fostering algorithmic thinking through incomplete recipes

PurposeAlgorithmic and computational thinking are necessary skills for designers in an increasingly digital world. Parametric design, a method to construct designs based on algorithmic logic and rules, has become widely used in architecture practice and incorporated in the curricula of architecture schools. However, there are few studies proposing strategies for teaching parametric design into architecture students, tackling software literacy while promoting the development of algorithmic thinking.Design/methodology/approachA descriptive study and a prescriptive study are conducted. The descriptive study reviews the literature on parametric design education. The prescriptive study is centered on proposing the incomplete recipe as instructional material and a new approach to teaching parametric design.FindingsThe literature on parametric design education has mostly focused on curricular discussions, descriptions of case studies or studio-long approaches; day-to-day instructional methods, however, are rarely discussed. A pedagogical strategy to teach parametric design is introduced: the incomplete recipe. The instructional method proposed provides students with incomplete recipes for parametric scripts that are increasingly pared down as the students become expert users.Originality/valueThe article contributes to the existing literature by proposing the incomplete recipe as a strategy for teaching parametric design. The recipe as a pedagogical tool provides a means for both software skill acquisition and the development of algorithmic thinking.

Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow

Though building-scale energy demand and indoor thermal comfort have been extensively covered by recent studies, the automation of middle- and larger-scale outdoor microclimate evaluation in parametric design is less covered. The relatively slow computation and the need for sophisticated expertise are some of the current issues. This paper proposes a Rhino–Grasshopper custom script to automatically compute spatial indicators for a quick thermal comfort estimation. The Galapagos evolutionary algorithm is used to optimize thermal comfort and select the best combinations of spatial indicators. In a summer case study located in Shantou, China, the proposed workflow was three times faster than a non-automated indicator calculation in ArcGIS, while the optimization method achieved 25% to 33% reduction in land areas under extreme heat stress. This automated process applies to existing states and new urban designs. It is adaptable to customized prediction models under different climatic zones.

The Role of Technology in Shaping the Future of Architectural Education

This research paper explores the transformative impact of technology on architectural education, examining how digital tools, virtual environments, and collaborative platforms are reshaping the learning experience for students and influencing the practice of architecture. The paper delves into the integration of technologies such as BIM, VR, AR, parametric design, digital fabrication, online learning platforms, and sustainable design analysis in architectural education. By examining the current landscape and future prospects, this research aims to provide insights into the evolving role of technology in preparing architects for the challenges and opportunities of the digital age. Key Words: architectural education, parametric design, digital fabrication, digital age, virtual environments.

Parametric Design and Optimization of a Knee-Joint Biomechanical Energy Harvester to Maximize Power Generation With Minimal Metabolic Cost

Reliable supplies of electric energy for the electrical devices carried by field workers is important for work and survival, while the hand-crank generator commonly used is toilsome. Using knee-joint biomechanical energy harvesters to harness dissipated biomechanical energy and generate electric power is an ingenious way to solve this problem. However, the lack of a parametric design method for the harvesters has caused power generation with large bioenergy consumption. In this work, we proposed an optimal design method for the parameters of the critical harvester components, including the generator, gearbox, and load resistor. Power generation and the total metabolic cost of the harvesting (TCOH) were set as the optimization objectives, and the equations to calculate both objectives were established. The optimal electric parameters of the generator were obtained by deduction and analysis of the equations, and the gear rate of the gearbox and resistance of the load resistor were optimized by a dynamic programming. Prototypes built with the optimized component parameters were tested in walking experiments. The prototypes can generate 6.16±1.47 W electric power with 11.9 W metabolic cost during 5 km/h walking. More generated power with much less metabolic cost was achieved compared with other knee-joint harvesters.

Electromagnetic and Mechanical Topology Optimization for SynRM Rotors Considering High Dimensional Constraints

The knowledge-based parametric design process of the rotor topology of the synchronous reluctance motor (SynRM) requires a tedious parameter definition, selection, and searching process until an optimal design can be obtained. This research proposes a topology optimization approach based on solid isotropic material with penalization method for the SynRM rotor topology design. The aim is to pursue a more intelligent and automatic design process. Specifically, the proposed method aims to generate the SynRM rotor design of high output torque profile. Meanwhile, the mechanical constraints including structural compliance and stress etc. are also considered for promising the structural reliability of the rotor. Considering the high dimensional constraints caused by the multi-physics performances, the augmented Lagrangian method based optimization framework is developed to solve the minimization problem in a compact scheme. To prove the effectiveness of the developed method, simulations and analysis on the topology optimization of SynRMs are performed in this research. Prototyping and experiment are also conducted for verifying the performance of the optimized structure.

Study on the temperature field distribution of the interface and process parameter optimization for material extrusion of PLA

AbstractDespite the significant development and wide application of 3D printing technology, there are rarely reports on the influence of the build plate temperature upon the manufactured products. By conducting transient thermal analysis and 3D printing experiments, researchers optimized the build plate temperature for reducing defects in printed samples formed through material extrusion (MEX). The temperature distribution near the bottom interface was analyzed using ANSYS parametric design language (APDL) numerical calculation, and the temperature variation at the key monitoring node was examined for different build plate temperatures. Standard samples were then prepared to study warping and tensile properties. The results indicated that sagging would occur when the key monitoring node's temperature exceeded the glass state temperature of the PLA filament, particularly at build plate temperatures of 55 and 60°C. This conclusion was verified through MEX experiments. Ultimately, without sagging, an optimal build plate temperature of 50°C was determined based on APDL calculations, warping measurements (0.42 mm), and tensile yield strength (39.63 MPa). Thus, research on this parameter of temperature field distribution will contribute to the realization of high‐quality and high‐precision 3D printing.Highlights Optimized the build plate temperature to reduce defects of MEX. The temperature distribution of the interface near the bottom was analyzed by APDL and monitored. Optimal temperature was discussed by combining the results of APDL and experiment data.

Parametric Automated Design and Virtual Simulation of Building Machine Using BIM

With the continuous development of the construction technology of the main structure of high-rise buildings, traditional construction techniques have been widely used in high-rise building projects. However, these technologies have problems such as low safety, low intelligence, and poor integrity. In order to solve these challenges, the Third Engineering Bureau of China Construction independently developed a new type of construction technology—a high-rise building integrated work construction platform (referred to as a building machine). Building machines has been gradually applied to high-rise building projects due to its intelligence, integration, safety, and other advantages. However, with the increase in the number of high-rise building projects, the traditional way of designing building machine layout programs is inefficient and the design program is complicated to change. To solve these problems, this paper proposes a building machine parametric design and layout and virtual simulation method. This method uses Blender open-source software to model the building machine parametrically and quickly using its visual programming tool Geometry Nodes (GN) and simulates the building machine in the virtual scene through the Unity3D platform. The results show that, compared with the traditional design mode, the method proposed in this paper can quickly complete the scheme design in the design phase of the building machine and display it through the 3D model, which has a better visualization effect, improves the design efficiency, and reduces the design cost, and the Unity simulation platform can also provide the construction personnel with pre-shift education and simulation of the operation of the building machine. This method provides a theoretical basis and guidance for the digital construction of the building machine.

Optimizing the shapes of concert halls through parametric design

Building acoustic simulation tools have often been used to ensure a room's acoustic performance, but they are rarely used in the early design stage. In this study, an acoustic simulation plug-in of Grasshopper for Rhino, Pachyderm, was combined with parametric design and a multi-objective optimization process to study the acoustic performance of a shoe-box-shaped concert hall in the early design stage. Three different factors of the sound field, sound pressure level A-weighting (SPL(A)), reverberation time (RT), and clarity 80 (C80), were discussed. One of the most famous shoe-box-shaped concert halls, Großer Musikvereinssaal, was selected as the comparison model. Two thousand results were generated through a multi-objective process in 36 hours. Compared with Großer Musikvereinssaal, the Pareto front of the 2000 results showed better performance in various factors of the sound field according to what it optimized.

CFD Prediction of Ship Propeller-induced Underwater Radiated Noise

As the negative impact of man-made underwater radiated noise (URN) on marine life is recognized by the scientific community, guidelines and initiatives are proposed by port authorities and international organizations for monitoring and reducing URN from ship operations. In medium to large-size ships, propellers are often the dominating URN source as compared to on board machinery and flow turbulence. Within this paper, URN from cavitating flow around a ship propeller in behind-hull condition is predicted by a high-fidelity CFD solver, coupled with an acoustic analogy method. The noise sources are hydrodynamic flow fields around propeller and rudder models, simulated by CFD with a multi-phase flow model. Far-field acoustic noise is calculated by the permeable Ffowcs-Williams Hawkings (FWH) acoustic analogy method. Unsteady cavitation patterns and URN levels from the numerical predictions are validated against experimental results from a cavitation tunnel test. The propeller blade shape is optimized for minimum cavity volume by parametric design variations and performance evaluations based on steady CFD simulations with a simplified hull wake model. The URN predictions from unsteady CFD-FWH simulations demonstrate that the cavity volume reduction for the optimized blade design can lead to considerable decrease of low-frequency tonal noise.

Parametric design, life cycle assessment, and optimization of a discrete product with application to an electric traction motor

Humans are surrounded and even dependent on discrete products for their daily activities. Product design processes have been vastly studied towards a healthy economic prosperity and other technical objectives (e.g., low risk). However, recent studies show that these methods have been lacking in terms of the environmental challenges humanity faces in the present and future. This study proposes a novel design framework that incorporates a parametric life cycle assessment (PLCA) as part of the optimization-based design model of a discrete product at early stage. Electric traction motor design applied to an electric vehicle is used as a case study. The results of performing a multi-objective design problem and an uncertainty analysis using Monte Carlo simulation show the usefulness of incorporating the PLCA in a transparent manner, as required by the International Standards Organization. Future work suggests investigating the expansion of this technique to support a broader range of objective functions.

A kind of numerical model combined with genetic algorithm and back propagation neural network for creep-fatigue life prediction and optimization of double-layered annulus metal hydride reactor and verification of ASME-NH code

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample.

Fast Optimization Method of Flexible Support Structure Based on Mathematical Model

In order to realize the rapid analysis and optimization of the flexible support structure, the parametric design method is used to establish the three-dimensional model. Basic dimension parameters of the flexible support structure are linked with external data sources, and the functional relationship between dimensions is established to ensure the rationality of the model. It only needs to change the data in the data source to realize the automatic update of the 3D model when the model needs to be reconstructed. Compared with the finite element method, the optimization model improves the efficiency of simulation analysis and optimization design. The model proposed in this paper can obtain the optimal solution by weighing the mass and deformation displacement of the structure.

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue.

Light-responsive kinetic façade system inspired by the Gazania flower: A biomimetic approach in parametric design for daylighting

Through a multidisciplinary approach, this research proposes a design solution for a light-responsive kinetic biomimetic system, inspired by the functional principles of the Gazania flower. The adaptive movements of the Gazania flower were studied through a morphological-functional analysis and then used in the abstraction phase for the design of the biomimetic system, through parametric modelling. Climate-based daylight metrics and luminance metrics were evaluated for the different kinetic alternatives developed. The results of the parametric simulations, carried out for different occupant positions in an office building in a temperate Mediterranean climate show that the biomimetic kinetic system is well suited to provide the office space with variable natural daylight between 87,5 % and 100 %, promoting energy efficiency and user comfort. The results correspond to the optimal ranges of climate-related daylight metrics that prevent glare and overheating by shielding direct sunlight. The study also highlights the importance of further research, including material's prototyping, to validate and improve the proposed design and its translation into technology. Overall, this study demonstrates the potential of combining principles from biology, materials science and architecture to develop adaptable and sustainable design solutions that address sunlight and indoor comfort challenges.