Related Design Variables Research Articles

HULL SHAPE OPTIMIZATION OF SMALL UNDERWATER VEHICLE BASED ON KRIGING-BASED RESPONSE SURFACE METHOD AND MULTI-OBJECTIVE OPTIMIZATION ALGORITHM

Small underwater vehicles have unique advantages in ocean exploration. The resistance and volume of a vehicle are key factors affecting its operation time underwater. This paper aims to develop an effective method to obtain the optimal hull shape of a small underwater vehicle using Kriging-based response surface method (RSM) and multi-objective optimization algorithm. Firstly, the hydrodynamic performance of a small underwater vehicle is numerically investigated using computational fluid dynamics (CFD) method and the value range of related design variables is determined. The mesh convergence is verified to ensure the accuracy of the calculation results. Then, by means of the Latin hypercube sampling (LHS) design of simulation, the Kriging-based RSM model is developed according to the relation between each design variable of the vehicle and the output parameters applied to the vehicle. Based on the Kriging-based RSM model, the optimal hull shape of the vehicle is determined by using Screening and MOGA. As results, the vehicle resistance reduces and volume increases obviously.

Self-supporting structure design with feature-driven optimization approach for additive manufacturing

In this work, a topology optimization approach is developed for additive manufacturing (AM) of 2D and 3D self-supporting structures. Three important issues, i.e., overhang angle control, avoidance of the so-called V-shaped areas and minimum length scale control are addressed. 2D solid polygon and 3D polyhedron features are introduced as basic design primitives that are capable of translations, deformations and intersections to drive topological changes of the structure. The overhang angle control is realized in a straightforward way only by imposing upper bounds to related design variables without introducing any nonlinear constraint. The V-shaped area is avoided by simply limiting the positions of solid features. Minimum length scale control is controlled by a robust formulation. Numerical examples in 2D and 3D demonstrate the effectiveness of the proposed approach for various build directions, critical overhang angles and minimum length scales considered in AM.

Identifying Entangled Physics Relationships Through Sparse Matrix Decomposition to Inform Plasma Fusion Design

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulation codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g., neutron yield, neutron velocity, areal density) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield, such as picket power and laser energy, variables that represent a dramatic change of the ICF design, such as number of pulse steps, are also very important. The obtained sparse components are then used to train a random forest (RF) regression surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF trained using all the design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulation results.

Balancing and Simulation of a Double Crank-Rocker Engine Model for Optimum Reduction of Shaking Forces and Shaking Moments

In this paper, a new configuration of Crank-Rocker (CR) model has been proposed by duplicating its mechanism. The method has been implemented to overcome vibration problem on a single-piston Crank-Rocker engine caused by system unbalance. The new method suggests combining conventional method of adding counterweights to reduce shaking forces and eliminating the inertial moments on system by implementing the new layout. A dynamic study of the new model is presented, then the objective function is derived and implemented to perform the optimization process. Related design variables and system constraints are introduced to determine attached counterweights optimized characteristics. For results validation, the simulation, dynamic analysis, and optimization process were conducted using ADAMS VIEW® software. The output results were presented and discussed to verify the validity of the suggested method. It was noticed that the method was very effective and has managed to reduce the total shaking forces by about 91%, shaking moment by about 66%; and the driving torque by 27%.

A new water wave optimization algorithm for satellite stability

Scientific and efficient design is the foundation requirement for stable operation of satellites. While a satellite is composed of a number of subsystems with independent functions. These subsystems interact with each other and related design variables are also coupled with each other. Thus, the overall design process needs to comprehensively weigh the relationship among multiple design objectives and design constraints of different subsystems, in order to obtain the optimal balance of the overall framework. Here we propose a novel Powell-Water Wave Optimization (POWWO) algorithm and apply it to the overall optimization design of earth observation satellites. Compared with previous works, this algorithm shows more excellent optimization properties, via combining the global and local search ability. Its effectiveness can also be supported by the well-known benchmarks and satellite optimization design experiments.

An Evaluation Grid Method System for Extracting Diverse User Requirements and Related Design Variables

An Evaluation Grid Method System for Extracting Diverse User Requirements and Related Design Variables

Correlation Between Density of Residential Areas and Solar Energy Potential in Xining City

In urban scale, solar energy utilization potential is closely related to residential density. Taking Xining City as an example, this paper explored how density of urban residential area affects solar energy utilization potential of urban housing. By changing density related design variables , including building layout, density rate, floor-site area ratio and the number of floors, 36 general models of residential areas with low, medium and high density are Abstracted. The results show that solar energy utilization potential of buildings varies greatly with different density related design variables. Comparison of a number of different scenarios reveals how density related variables affect solar energy utilization potential, based on which suggestions for optimization of solar energy potential for urban residential areas in their initial planning and design stages are proposed.

SUV Front View Design Method Selected by Multiple Schemes

Based on the parametric sensibility engineering and computer-aided industrial design basis, the SUV forward-looking contour is used as a target to explore a preferred selection model for multiple design schemes under specific images. Samples of the SUV front view were collected, the design variables were defined using the Bézier curve, and representative samples were selected using multivariate analysis and cluster analysis. Through the questionnaire survey, the sensory image of SUV front view contour was screened to obtain pairs of representative images. Using semantic difference method to conduct perceptual image research and analysis of representative samples. According to the analysis results, the correlation models of perceptual images and related design variables are obtained. The effectiveness of the model is verified by the existing model design of the market. The conclusion proves that there is a correlation between the consumer’s emotional image and the position of the SUV front-view contour feature point. The coordinates of the forward-looking contour control point of the design scheme are brought into the relevant consumer image correlation model, and the optimal image is obtained under the image. The design scheme improves the success rate of the front face design of the SUV.

Time dependent sheet metal forming optimization by using Gaussian process assisted firefly algorithm

For a sheet metal forming optimization problem, time related design variables are seldom considered in practice. The purpose of this work is to handle time dependent sheet metal forming problems. Because it is difficult to investigate all time points during the entire forming procedure, some key time points should be extracted. Therefore, the number of design variables should be significantly increased due to introduce auxiliary time design variables. However, curse of dimensionality is a formidable difficult issue to be solved. To solve such medium-scale problems, Gaussian Process Assisted Firefly Algorithm (GPFA) is suggested. The main idea of the suggested method is to construct a surrogate model-aware search mechanism with Firefly Algorithm (FA) for simulation-based optimization efficiently. Compared with other FAs, the distinctive characteristic of GPFA is to generate new sample points adaptively based on maximum Expected Improvement (EI) criterion, so that the local and global search can be well balanced, and a small promising area can be quickly focused on. Numerical studies on benchmark problems with 20 variables and a real-world application of time dependent sheet metal forming optimization reveal that the GPFA is capable to solve such similar problems.

A methodological framework for the development of feasible CO2 conversion processes

Converting captured CO2 feedstock into valuable chemical products is viewed as one of the potential ways to reduce atmospheric CO2 emission. To this end, a methodological framework is suggested to support the development of feasible CO2 conversion processes that can contribute to the CO2 reduction by replacing non-CO2 utilizing processes or non CO2-based products. The framework encompasses several execution and decision steps and uses three main criteria, which are the demand availability, CO2 reduction feasibility, and economic feasibility. As an illustrative example, a methanol plant employing combined reforming (CR) of methane reaction is developed. To supply the CO2 feedstock, the aMDEA-based CO2 capture applied to a SMR-based H2 plant is considered. A baseline process is developed and is compared with a non-CO2 utilizing conventional methanol plant (process substitution) and a gasoline production process (product substitution) in terms of the established criteria. For the former, it is verified that the methanol production via combined reforming leads to cheaper unit production cost as well as lower net CO2 emission compared to the conventional methanol plant. For the latter, it is shown that the feasibility of the CO2-based methanol as an alternative fuel to gasoline highly depends on the type and price of the raw materials. To improve the developed baseline CO2 conversion process further, (1) some of the combined reforming reaction related design variables are fine-tuned using a sensitivity analysis and an equilibrated syngas plot, and (2) utilization of various renewable energy resources for the internal electricity demand is examined.

Computational Design for Sport Buildings

The design of sport buildings has great impact on top-sport as well as on recreational sport-activities. It implies challenging tasks in meeting the performance-requirements. This includes the control of factors like daylight/lighting, air flow, thermal conditions, just to name a few. Such factors impact the performance of athletes and are hard to control in large sport halls; their control is even harder when the public/audience is located within the halls and require different climate conditions. While mechanical installations are often needed during competitions in order to guarantee constant conditions, relaying on mechanical installations during the daily and recreational use of the venues challenges their medium/long term sustainability. Computational form finding approaches can favour the achievement of high-performing and sustainable sport buildings. In this light, the paper tackles the use of Multi-objective and Multidisciplinary design optimization. The paper presents the concept of Multi-objective Multidisciplinary design optimization techniques to support trade-off decisions between multiple conflicting design objectives and interdisciplinary design methodology, during the conceptual design of sport buildings. The proposed method is based on parametric modelling, performance simulation tools and algorithms for computational optimization, for which the paper tackles three specific aspects. First of all, due to the complexity of large sport buildings, the formulation of the optimization and the screening of the related design variables is crucial in order to obtain a meaningful design space, which helps reducing unnecessary computational burden. Secondly, assessing performance based on measurements and analyses is crucial and can be supported by performance simulations tools; however effectively integrating performance simulations tools in the early phase of the design requires new tools. In this light, a customized computational process for the rapid assessment of temperature and airflow patterns is presented. Thirdly, the process requires the combination of design optimization and design exploration, while searching for well-performing solutions. The importance of design exploration is emphasized also for sub-optimal solutions. In order to facilitate the design exploration, the combination of optimization algorithms, multi-variate analysis algorithms and options for exploring design solutions via an interactive dashboard connected to a database are presented. To exemplify the method, specific case studies are developed as collaboration between Delft university of Technology and South China university of Technology.

The effects of the cement thickness on the failure probability of the cemented hip prosthesis

Finite element analysis has been widely used to determine the behavior of implants placed in a body. However, uncertainties in loading acting on the implant, material properties and implant geometry are not taken into account during these analyses. Thus, it can result in either an unreliable design or unnecessarily overdesigned implants. Therefore, probabilistic design methods, in which uncertainties related to design variables are taken into account during the solution, have been recently considered as an important approach to design more reliable and long-lasting implant. Since the failure of the cemented hip prostheses mostly occurs at the cement mantle, determining the failure probability becomes more important. Therefore, in this study, the failure probability of the cement mantle which bonds the hip prosthesis into bone was calculated using the reliability methods by considering the uncertainties in the related design variables. In order to calculate the failure probability, three-dimensional solid models of the cement mantle, femur and hip prosthesis was obtained. The same model was used to obtain the maximum principal stress from the deterministic stress analysis in order to compare the failures state of the cemented hip prosthesis using the both approaches. The deterministic stress analysis carried out considering the mean values of the design parameters does not indicate any failure for the cement mantle thickness considered in this study. On the other hand, the probabilistic analysis yields a failure probability up to 0.3. Therefore these results indicate that the probabilistic analysis should be considered to design a more reliable and long-lasting implant.

Reliability-based optimum design for mechanical problems using genetic algorithms

A reliability oriented design approach for mechanical or structural components is implemented primarily based on strength—stress interference (SSI) theory. This paper demonstrates a principle for combining SSI theory and an optimization technique for developing a reliability-based optimum design for mechanical problems. The independently paired information (strength and stress distributions) are basic while progressing reliability design. For a complex system, the independently paired information sometimes is not easily clarified due to the structural complexity or the coupled relationship of the loads. To treat these problems, the paper proposes to express the independently paired information from the viewpoint of supply-requirement of a design in performance. The supply (provided by a design) is analogized to the strength as well as the requirement (requested by the customer) to the stress. Based on the viewpoint of supply-requirement, the paper presents four types of performance-related reliability measurement to fulfil reliability design for mechanical problems. The reliability measurements are derived according to the related design variables that formulate the performance indexes. Next, the designed problem expressed with probabilistic formulation is transformed into an unconstrained minimization problem subjected to the constraints of the performance needs and its reliability target. Genetic algorithms (GAs) are used to find the optimal solution for the reliability design problem. The related theories and an example of design are reported in this paper to depict the proposed method.

Preferences for Car-restrained Residential Areas

The purpose of this paper is to assess the effects of restrained car access on preferences for new residential areas and to investigate how expected negative effects can be compensated for by other transport-related characteristics of residential areas. In this study, restrained car access is operationalized by means of concentrated parking facilities, implying that occupants cannot park their cars near their homes. The outcome of this research is relevant for designers and developers of residential areas. Stated choice experiments are used to measure the effects of six transportation related design variables. Data are collected in four Dutch cities. The results indicate that most people prefer to live in non-car-restrained residential areas. However, it appeared that negative effects of concentrated parking facilities can be compensated for, at least partly, by providing secured parking facilities, good non-motorized transport facilities and access to public transport at a short distance from home.

The Relationship of Neighbourhood Built Environment Features and Adult Parents' Walking

Research on urban design and walking often emphasizes macro-scale features of the physical environment, such as block length and number of intersections, that can be measured remotely using GIS and or aerial photographs. In contrast, urban designers emphasize the importance of micro-scale features in individuals' use and experience of neighbourhood environments. This paper moves beyond examining correlations of individual built environment features and walking, to begin to test proposals about which composite characteristics of the built environment (safety, comfort, etc.) may have the greatest impact on walking. Several urban design characteristics of 11 neighbourhoods throughout California were observed. Self-report, adult walking data on the number and types of walking trips were obtained from surveys administered to parents of 3rd–5th graders. Urban design features related to both accessibility and safety are associated with the amount of walking that adults do in their neighbourhoods. Grouping related urban design variables into indices provides some clarity as to how the built environment may impact walking. Safety emerges as the most important built environment characteristic (of those tested), related to both destination and recreational walking.