Optimization-Assisted Building Design - Cases study of design optimization based on real-world projects

The recent years have seen a significant increase in ontology research in the field of engineering due to the needs of domain knowledge capturing, reusing and sharing between software agents and/or engineers.The increasingly available enhancements of computing resources have considerably increased the role of Computer-aided engineering applications in the product development process by saving time and costs. In this context a key role is played by the Process Integration and Design Optimization tools that facilitate and automate the integration and interoperability of all different Enterprise Applications involved in the simulation of engineering tasks. However, all these tools inherit a severe limitation about the semantic meaning of the tools they automate.This paper proposes a platform independent Process Integration and Design Optimization (PIDO) ontology that aims at providing an extensive mapping of the PIDO domain, with a special attention to its adoption in software applications. A comparison with existing similar ontologies and an explanation of the reasons that lay behind the classes and relations introduced is provided. Finally, a real application case is illustrated along with the related main benefits.Keywordsontologiesoptimizationprocess integration

A dynamic industrial design optimization requires high-quality optimization algorithms as well as adaptive representations to find the global solution for a given problem. For adapting the representation to changing environments or to new input we utilize the concept of evolvability, which in our interpretation consists of three criteria: variability, regularity, and improvement potential, where regularity and improvement potential characterize conflicting goals between exploration and exploitation. Our goal is the efficient adaptation of the representation according to a given preference weight between regularity and improvement potential. We propose a combination of two heuristics, Lloyd sampling and orthogonal least squares sampling, to initialize the adaptation process for a given preference weight. We show that this initialization improves the convergence speed of the adaptation process as well as the resulting fitness. We then realize a stepwise design optimization procedure by alternating the adaptation of the representation with optimization of the design. During the design optimization process we extract information which we exploit in the next adaptation phase. We show that an intermediate preference weight, balancing between regularity and improvement potential, allows to exploit this information and is robust to erroneous initial information. Thereby, we increase the performance of the whole design optimization process.

Preliminary designs with deterministic approaches used to solve the uncertainty related information from a ship design results preliminary design of conventional. In order to overcome the imbalance of optimization in the design process, the preliminary design is equipped with a method Collaborative Optimization (CO), which is part of the Multidisciplinary Design Optimization (MDO). Process design optimization in preliminary design with a deterministic approach based on the value subsystem optimization required by the vessel (d). Ship design requirements of this study are deadweigth requirements, volume cargo requirements and speed/resistance requirements. The preliminary design hull form general cargo for shipping Jakarta to Makassar by using deterministic approach is expected to provide an alternative principal dimension and design variations of the optimal hull form based on information dock and owner reqiurement. In addition, this study is also expected to be an alternative method used in the preliminary process design of modern ships because in the preliminary designs have efforts to optimize an element ship design.

PFIM 4.0, an extended R program for design evaluation and optimization in nonlinear mixed-effect models

Implementation and utilisation of a Grid-enabled problem solving environment in Matlab

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

Machine Learning User Preferences for Structural Design

Complex engineering design (CED) optimization problems in science and engineering commonly have large design spaces. In such design spaces, typically thousands of exact fitness evaluations are required to locate a near optimal design. Often in photonics, electromagnetic, aerospace, biomedical and microwave circuits detailed design processes, variable-fidelity analysis codes are employed to strike a balance between design cost, time and estimation accuracy. Nevertheless, in analysis and design optimization processes where high-fidelity analysis codes are used, each exact fitness evaluation requiring the simulation of analysis codes may cost hours of supercomputer time. Therefore, the overwhelming part of the total run time in CED optimization is usually taken up by the simulation of analysis codes. This often poses a serious impediment to the practical application of high-fidelity analysis codes and evolutionary algorithms to CED optimization problems.

:A robust multi-objective wing design optimization procedure using CFD is presented in this work. Instead of directly applying CFD to wing design optimization, a Kriging aerodynamic model that approximates CFD result is proposed to save computational cost. By introducing 6σ robust approach as a sub-objective function, multi-objective wing design problems can be solved with aerodynamic robustness. Non-Uniform Rational B-Spline curves (NURBS) are introduced to depict the wing geometry in design optimization process. The most important parameters of the wing geometry can then be screened by design of experiment and treated as design variables due to the local variation property of NURBS. Under this formulation, a multi-objective genetic algorithm based on non-dominated sorting is employed to handle multiple flight conditions in wing design. The optimum wing geometry is selected according to trade-off among design objects on the non-dominated front. To validate the proposed approach, two robust design optimization cases are studied for ONERA-M6-Wing. It turns out that the drag coefficient is insensitive to Mach number between Ma0.8–Ma0.9 after robust optimization. The result also verifies the effectiveness of our method in boosting performance and robustness of multiple flight conditions: the lift curve slope (Ma0.3) of optimum wing and the 6σ objective function of transonic drag (Ma0.8–Ma0.9) increase by 11.9% and 25.4%, respectively. Observations in the optimization cases are concluded as follows: 1) For wing aerodynamic design problems, the multi-objective robust optimization can both improve the performance at different flight conditions and provide robustness. 2) The Kriging aerodynamic model derived from CFD result can satisfy the precision requirement of wing design. 3) Even though the optimization result is subject to the weights of the 6σ function in sub-object, the influence is not comparable to the trade-off among design objects. 4) The optimum solution obtained by proposed approach is superior to gradient based optimization method, while the computational cost is acceptable.

<div class="htmlview paragraph">In this paper the development of statistical metamodels and statistical fast running models is presented first. They are utilized for propagating uncertainties in a multi-discipline design optimization process. Two main types of uncertainty can be considered in this manner: uncertainty due to variability in design variables or in random parameters; uncertainty due to the utilization of metamodels instead of the actual simulation models during the optimization process. The value of the new developments and their engagement in multi-discipline design optimization is demonstrated through a case study. An underwater vehicle is designed under four different disciplines, namely, noise radiation, self-noise due to TBL excitation, dynamic response due to propulsion impact loads, and response to an underwater detonation. The case study also demonstrates the value of the multi-discipline design optimization in identifying a system level optimum, and it emphasizes the importance of including uncertainties in the design optimization process.</div>

Process Integration and Design Optimization (PIDO) tools, which can connect and automate a series of computational processes forming an executable workflow with automated data input, analysis execution, and output collection, have an abundance of potential early in the life cycle of a product, but the utilization of these tools and approaches in late-phase programs can be every bit as valuable. One could even argue that the use of these tools to quickly find root causes and optimize final solutions is even more important in the late phases of a program, as the costs at these phases are much higher. PIDO tools and approaches can automate design iterations, allowing more designs to be tested in a shorter amount of time, which can help form a better understanding of the response space, and can in turn help determine the critical parameters of the system. These critical parameters can then be used to lens design verification (DV) data by drawing engineers' focus to the critical relationships of the DV data in order to make statistical observation about these influential factors controlling the system’s response. Potentially, this could be used to find process solutions to product issues without requiring expensive late-phase design changes. This article shows how this very approach was used on a prototype catheter’s hemostasis assembly to troubleshoot a leakage issue prior to locking-in the production tooling design.

Rubber isolators are mounted between a cooling module and a carrier to isolate the car body from vibration due to the rotation of the cooling fan. The isolators should be durable against fatigue loads originating from fan rotation and road disturbance. Thus, the design of rubber isolators is required to maximize both vibration isolation and fatigue life. In this study, the shapes of the rubber isolators are optimally designed using a process integration and design optimization (PIDO) tool that integrates the various computer-aided engineering (CAE) tools necessary for vibration and fatigue analyses, automates the analysis procedure and optimizes the design solution. In this study, we use CAE models correlated to the experimental results. A regression-based sequential approximate optimizer incorporating Process Integration, Automation and Optimization (PIAnO), a commercial PIDO tool, is employed to handle numerically noisy responses with respect to the variation in design variables. Using the analysis and design procedure established in this study, we successfully obtained the optimal shapes of the rubber isolators in two different cooling modules; these shapes clearly have better vibration isolation capability and fatigue lives than those of the baseline designs used in industry.

This paper presents the computer modeling, analysis, simulation and design optimization of an efficient dc-dc converter with synchronous rectification, which is important in battery charge/discharge circuits in dc uninterruptible power supply (UPS) systems. Simulation results from the models are presented for the forward mode and the backup mode. The results are compared to those of the expected ideal converter operation to confirm the validity of the simulation models. An objective function of the converter efficiency in the forward mode is derived and presented. Genetic Algorithm (GA) is used to perform a constrained optimization on the objective function and optimum results are presented. The resulting effects of GA parameters such as varying the population size and number of generations, and varying the amount of mutation and crossover fraction, are also presented. The computer modeling, analysis, simulation and design optimization (GA) are performed using software tools within the MATLAB/SIMULINK simulation environment.

Molded interconnect device (MID) technology is a key enabling technology with growing markets in automotive, communications, consumer electronics through integration with lighting and sensor technologies. The MID technology is yet to be explored for high temperature applications in automotive or consumer lighting. One of the hindering factors for such implementation in the serial production and time to market is the improper electronic and thermal packaging of the light emitting diodes (LEDs) on the MID substrates. This paper addresses the optimization of mold design, surface metallization and soldering process for the effective thermal management of the high-power LED systems. By using a simulation model, the thermal distribution and the resultant decrease in temperatures for varying forward electrical currents in high-power LEDs by design optimization is demonstrated. In addition, the optimization of solder process with respect to solder profile in context of vacuum vapor-phase soldering for void-free solder connections is discussed.

Abstracts: Computational fluid dynamics (CFD) is a feasible tool to examine and troubleshoot different types of equipment utilized in the pharmaceutical industry or healthcare. As a large number of fluids are processed by unit operations, even some increments in performance and efficiency may escalate profits and reduce costs. CFD methods are primarily used in the automobile and aerospace industries, but in the current era, this technology is extensively applied in the pharmaceutical and chemical industries and has become an important tool for process design scaleup and optimization. CFD is a numerical approach utilizing CFD software to solve equations numerically. This review focuses on the diverse utilization of CFD in the pharmaceutical field and current applications in the COVID 19 pandemic, a recent health crisis that is intimidating the world.