Efficient Design Optimization Research Articles

수동화재보호 재료가 적용된 구조부재의 화재하중에 대한 강도 특성

In offshore installations, fires cause the structure to lose its rigidity and it leads to structural integrity and stability problems. The Passive Fire Protection (PFP) system slows the transfer rate of fire heat and helps prevent the collapse of structures and fatality. Especially, intumescent epoxy coating is widely used in the offshore industry, and not only is the material cost expensive, but it also takes a lot of time and cost for construction. Several studies have been conducted on the efficient application and optimal design of the PFP system. However, the mechanical properties and the strength of the PFP material have not been considered. In addition, researches on the correlation between the thickness of PFP and the structural behavior were insufficient. Therefore, this study aims to analyze the thermal and mechanical effects of the PFP on the structure when it is applied to the structural member. In particular, it is intended to resolve the change in strength characteristics of the structural members as the thickness of the PFP increases.

An efficient multi-objective reliability-based design optimization method for structure based on probability and interval hybrid model

An efficient multi-objective reliability-based design optimization (MO-RBDO) method is proposed based on the probability and interval hybrid model. With probability variables coexist with interval variables in MO-RBDO problem, the original MO-RBDO problem could be considered as a three-layer nested multi-objective optimization problem. To reduce computing costs, firstly, an efficient decoupling strategy is developed to transform original three-layer MO-RBDO problem to single-layer optimization problem based on Karush–Kuhn–Tucker (KKT) necessary condition and the second-order fourth moment method. Secondly, a MO-RBDO method based on the adaptive approximation model is proposed for further improve computational efficiency. Then, the multi-objective genetic algorithm is employed to solve the MO-RBDO problem. Finally, the effectiveness and practicability of the proposed method are demonstrated by three numerical examples.

Fast Convergence Reliability-Based Design Optimization Method Considering Random and Evidence Variables

An efficient approximate reliability-based design optimization method under both aleatory and epistemic uncertainty is presented. As stated by the unified uncertainty analysis based on the first-order reliability method (FORM-UUA), it is possible to merge the probability and evidence theory to quantify the belief and plausibility of a specific performance function under mixed uncertainty. When the number of evidence variables and the number of intervals increase, the number of evaluations grows dramatically. As a result, if the hybrid reliability analysis is included in an optimization problem, usually referred to as nested hybrid reliability-based design optimization (HRBDO), it becomes unmanageable due to its high computational cost. The strategy proposed allows to avoid the computation of the subplausibilities for each focal element as required in the FORM-UUA. This strategy decouples the nested HRBDO into an iterative process with a deterministic optimization and a reliability analysis phase consisting of two separate but connected reliability analyses that handle separately the random and evidence variables. Then, the optimum design obtained is checked and adjusted through the FORM-UUA method. One analytical and one numerical problem are presented to validate the proposed method.

Boundary-Layer Ingestion Benefit for the STARC-ABL Concept

Boundary-layer ingestion (BLI) offers the potential for significant fuel burn reduction by exploiting tightly coupled aeropropulsive effects. NASA’s Single-aisle Turboelectric Aircraft with Aft Boundary-Layer propulsion (STARC-ABL) concept employs BLI on an electrically powered tail cone thruster to take advantage of the technology on what is otherwise a conventional airframe. Despite the traditional airframe of this concept, aeropropulsive integration is critical to the BLI propulsor’s performance. Therefore, it is vital to employ tightly coupled aeropropulsive models to design BLI systems. In this work, 3-D RANS simulations are used to model the aerodynamics, and 1-D thermodynamic cycle analyses are used to model the propulsion system. The two models are tightly coupled using NASA’s OpenMDAO framework, enabling efficient design optimization through gradient-based optimization with analytic derivatives. Using this coupled aeropropulsive framework, 18 computational fluid dynamics (CFD)-based aeropropulsive design optimizations are performed to study the power requirements of the BLI configuration and a reference podded configuration where the electric fan ingests freestream air. This study provides the first set of CFD-based performance data for the STARC-ABL concept across the design space of BLI fan size and pressure ratio. The results quantify the power savings through BLI compared to a traditional propulsion system.

Aseismic Optimization of Mega-sub Controlled Structures Based on Gaussian Process Surrogate Model

Due to the complex seismic characteristics of mega-sub controlled structures (MSCS), it is difficult to give full play to their advantages in earthquake resistance by traditional design methods. Meta-heuristic optimization algorithms can be used to improve the seismic performance, but the structural response needs to be calculated repeatedly, which results in high computation cost. To overcome these challenges, an efficient aseismic optimization design procedure for engineering application is developed. In this procedure, the model of optimization problem is established based on time history analysis (THA). Gaussian process regression (GPR) surrogate models are employed to predict the values of the objective and constraint functions. The expected improvement (EI) and constrained expected improvement (CEI) criteria are adopted to update the training sample set and obtain the optimal solution. Then, two examples are presented to validate the effectiveness and efficiency of this method in optimization problems of structures under earthquake loads. Finally, it is applied to optimizations of a MSCS and a mega frame structure (MFS), respectively. The response and cost of the optimized structures are reduced, and the MSCS shows better earthquake resistant capacities.

Multicriteria Optimal Latin Hypercube Design-Based Surrogate-Assisted Design Optimization for a Permanent-Magnet Vernier Machine

This article proposes an efficient surrogate-assisted design optimization method based on a multicriteria optimal Latin hypercube design (LHD) for multi-objective optimization of a surface-mounted permanent-magnet vernier machine (SPMVM). An improved iterated local search (ILS) method is proposed to optimize the spatial distribution uniformity and orthogonality of LHDs so that the data feature over the wide ranges of optimization variables can be captured more efficiently. Using the optimal LHD, a highly generalizable surrogate model can be trained with fewer samples, thus greatly reducing the required number of finite element analysis (FEA) cases and improving the optimization efficiency. A prototype corresponding to the optimal design is built and measured to validate the proposed method.

Computationally efficient analysis and design optimization of vibrational energy harvesters at the infrastructure scale

Recently, computational methodologies have been developed that are tailored to scenarios where a nominal structural model is modified by local features, such as uncertainty in the connection models between structural subsystems or the augmentation of a structural system with energy dissipation devices. In these methodologies, the locality of these features is exploited to yield computational efficiency gains of several orders of magnitude, often reducing computational demands from days to minutes. Additionally, researchers over the last decade have actively studied converting ambient vibration response kinetic energy into electrical energy. Results from the application of the algorithms to two structural models of high-rise buildings equipped with one harvester on the roof and distributed harvesters throughout the upper levels are discussed, specifically the computational efficiency of the method. Additionally, the design parameters of the harvester(s) are often difficult to determine, specifically the mass ratio, stiffness and damping. The computationally efficient methods have also been applied to determine optimal harvester(s) design parameters to maximize the power generated. Results from the design optimizations show that the distributed harvester configuration can generate equal or more power than one harvester with equivalent mass. This potentially allows for more flexibility when incorporating these devices into the overall structural system design.

Thermal and Structural Behaviour of Offshore Structures with Passive Fire Protection

In offshore structures, hydrocarbon fires cause the structure to loose its rigidity rapidly and this leads to structural integrity and stability problems. The Passive Fire Protection (PFP) system slows the transfer rate of fire heat and helps to prevent the collapse of structures and human losses. The vital design factors are decided in the detailed design stage. The determined design thickness must be accurately applied in the fabrication yard. However, there are many cases that the PFP is overused because of various reasons. This excessive application of the PFP is an unavoidable problem. Several studies have been conducted on the efficient application and optimal design of the PFP. However, the strength of the PFP has not been considered. In addition, research studies on the correlation between the thickness of the PFP and the structural behaviour are not widely available. Therefore, this study attempts to analyse the thermal and mechanical effects of the PFP on the structure when it is applied to the structural member. In particular, it is intended to determine the change in the behaviour of the structural member as the thickness of the PFP increases.

Efficient General Reflectarray Design and Direct Layout Optimization with a Simple and Accurate Database Using Multilinear Interpolation

In this work, a simple, efficient and accurate database in the form of a lookup table to use in reflectarray design and direct layout optimization is presented. The database uses N-linear interpolation internally to estimate the reflection coefficients at coordinates that are not stored within it. The speed and accuracy of this approach were measured against the use of the full-wave technique based on local periodicity to populate the database. In addition, it was also compared with a machine learning technique, namely, support vector machines applied to regression in the same conditions, to elucidate the advantages and disadvantages of each one of these techniques. The results obtained from the application to the layout design, analysis and crosspolar optimization of a very large reflectarray for space applications show that, despite using a simple N-linear interpolation, the database offers sufficient accuracy, while considerably accelerating the overall design process as long as it is conveniently populated.

Exploring Multiple-Objective Optimization for Efficient Test Design with Genetic Algorithm

Exploring Multiple-Objective Optimization for Efficient Test Design with Genetic Algorithm

Stable Parametric Model Order Reduction of Piezoelectric Energy Harvester by Matrix Interpolation

In this work, we present a successful application of the matrix interpolation-based parametric model order reduction method for generating stable reduced-order models of a piezoelectric energy harvester device. In order to ensure the stability of these geometrically parameterized reduced order models, we have combined the above method with four stabilization approaches: MOR after Schur, MOR after implicit Schur, Schur after MOR, and multiphysics structure-preserving MOR. We apply the combined approaches directly to large-scale multi-physical finite element models enabling so, an efficient design optimization based on accurate numerical models.

Surrogate based design optimization of low noise amplifier for ISM band

Design optimization of microwave circuits is a crucial matter for microwave engineers. For the last decades, researchers had been studying surrogate models for a computationally efficient design optimization process. Artificial Intelligence based algorithms had been used for modelling of complex microwave stages such as antenna, amplifiers and frequency selective surfaces. In this study, design optimization of a Low Noise Amplifier (LNA) based on surrogate models for both transistor stage and input & output matching circuits had been presented. Support Vector Regression Machine had been used for creating surrogate models of a microwave transistor and a non-uniform transmission line for having a fast and accurate LNA design optimization process alongside a low profile and high performance LNA using on-uniform transmission lines. By using this methodology, not only designer can create a mapping for missing points in the sparse sample S parameter data points provided by manufacturers but also the overall simulation duration can be significantly reduced thanks to the fast nature of surrogates compared to EM simulators. Here, the proposed surrogate models had been used alongside of Particle Swarm Optimization algorithm to determine optimal geometrical values of input/output matching networks. Then the obtained designs are prototyped and measured. The measured results are also compared with the performance results of counterpart design in literature. As for results, not only the proposed methodology is an effective, fast and reliable method for computationally efficient design optimization process of LNA but also provides better results than the counterpart design in literature.

Mosaic Based Optimization of NRD Guide Devices Using Binary Evolutionary Approaches and 2D-FVFEM

In this paper, efficient optimal design approaches based on mosaic optimization concept are developed for NRD guide devices to realize the high-performance compact millimeter-wave integrated circuit. Binary representation-based genetic algorithm, differential evolution algorithm, harmony search algorithm, firefly algorithm, and particle swarm optimization are developed to efficiently optimize the pixel pattern in the design region of NRD guide devices. To demonstrate the usefulness of these population-based optimizations, a comparative study based on the problem-solving success rate is conducted first. To carry out this study, four NRD circuit components are designed which include low crosstalk waveguide crossing, T-branch power splitter, bending waveguide, and frequency demultiplexer. The proposed optimal devices achieve high transmission efficiencies greater than 99.9%, 49.9%:49.9%, 99.9% at 60 GHz and 96.4%, 98.5% at 59 GHz and 61 GHz. In addition, the same NRD guide components except frequency demultiplexer are also designed at wideband operation and achieve broad bandwidth around 5 GHz, 4 GHz, and 3 GHz. In order to improve the computational efficiency, the originally developed two-dimensional full vectorial finite element method is employed for the numerical simulations. This paper demonstrates the detailed implementation procedure of developed evolutionary approaches for the material distribution in the design region of NRD guide devices, comparative study of developed optimization approaches, and proposed highly attributed NRD circuit components for the realization of NRD based high-performance compact millimeter-wave circuit.

Breakthrough instruments and products: Laser light tunable across the visible up to mid-infrared: Novel turnkey cw OPO with efficiency-optimized design.

Offering unique wavelength versatility, continuous-wave optical parametric oscillators (cw OPOs) are appealing sources of widely tunable laser light. Due to technical challenges, however, practical tunable cw OPO devices have so far been mostly limited either to near-infrared wavelength coverage or to maximum optical output powers in the 100 mW range-or both. We present a novel cw OPO designachieving output powers at the Watt-level throughout the very wide 500-765nm gap-free tuning range in the visible and coverage of up to 3540nm in the infrared spectral range. The system layout and its wavelength tuning options are tailored, in particular, to fundamental experiments in quantum and nanophotonics research as well as to applied holography.

Numerical simulation of particle screening efficiency of large multi-layer vibrating screen based on discrete element method

With the increasing requirements for energy conservation and environmental protection, multi-layer vibrating screens have become hot issues. Compared with single-layer vibrating screens, multi-layer vibrating screens has much better performance in terms of processing effect, treatment capacity, and environmental protection. The research on the physical parameters of the multi-layer vibrating screen is of great significance to the actual production. However, analysis and simulation studies of multi-layer vibrating screens are limited. In this paper, the screening process of wet particles on a multi-layer vibrating screen was simulated by using the discrete element method. The characteristics and application scope of the two vibration modes were analyzed. The particle penetration rate, the number of collisions, and the distribution of the particles under 23 combinations of structures and vibration parameters were investigated. The influence of different parameters on screening performance was analyzed. Several optimal combinations of frequency, amplitude and screen inclination angle under different working conditions were obtained. The screening efficiency of the balanced elliptic motion is higher than that of the linear motion. The best combination of the three parameters is 4 mm amplitude, 20 Hz frequency, and 3° inclination angle. The efficiency is higher when the particles follow a distribution of arithmetic on the screen. This study provides a reference for the efficient operation and optimal design of large multi-layer screening equipment.

Multiobjective optimization design of hybrid composite laminates for vibration using sequential permutation table method

Due to the large variable design space in optimization problems of composite laminates, it remains one of the challenging tasks to develop efficient optimization design methods to improve the design flexibility and efficiency. This work presents a sequential permutation table (SPT) method for the multiobjective optimization design of two-material hybrid composite laminates with simply supported boundary conditions, which maximizes the fundamental frequency and minimizes the cost/weight. Based on the vibration analysis of hybrid composite laminates, the approximate linear regularity of the square of fundamental frequency is derived, and two best ply orientations for the two materials are identified, respectively. By assigning one best ply orientation with maximum fundamental frequency at respective stacking positions, and using another best ply orientation to replace plies in the stacking sequence from the mid-plane to the outermost can lead to the optimum. Two multiobjective optimization problems are employed to verify the SPT method, results are compared with those obtained by heuristic algorithms. The obtained better solutions demonstrate the effectiveness and efficiency of the SPT method and its potentials for optimal design of hybrid composite laminates.

An Arbitrary Hybrid Turbulence Modeling Approach for Efficient and Accurate Automotive Aerodynamic Analysis and Design Optimization

The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).

Efficient Surrogate Modeling and Design Optimization of Compact Integrated On-Chip Inductors Based on Multi-Fidelity EM Simulation Models

High-performance and small-size on-chip inductors play a critical role in contemporary radio-frequency integrated circuits. This work presents a reliable surrogate modeling technique combining low-fidelity EM simulation models, response surface approximations based on kriging interpolation, and space mapping technology. The reported method is useful for the development of broadband and highly accurate data-driven models of integrated inductors within a practical timeframe, especially in terms of the computational expense of training data acquisition. Application of the constructed surrogate model for rapid design optimization of a compact on-chip inductor is demonstrated. The optimized EM-validated design solution can be reached at a low computational cost, which is a considerable improvement over existing approaches. In addition, this work provides a description and illustrates the usefulness of a multi-fidelity design optimization method incorporating EM computational models of graduated complexity and local polynomial approximations managed by an output space mapping optimization framework. As shown by the application example, the final design solution is obtained at the cost of a few high-fidelity EM simulations of a small-size integrated coil. A supplementary description of variable-fidelity EM computational models and a trade-off between model accuracy and its processing time complements the work.

Automated and interactive evaluation of welding producibility in an multidisciplinary design optimization environment for aircraft components

The automation capabilities and virtual tools within engineering disciplines, such as structural mechanics and aerodynamics, enable efficient Multidisciplinary Design Optimization (MDO) approaches to evaluate and optimize the performance of a large number of design variants during early design stages of aircraft components. However, for components that are designed to be welded, in which multiple functional requirements are satisfied by one single welded structure, the automation and simulation capabilities to evaluate welding-producibility and predict welding quality (geometrical deformation, weld bead geometrical quality, cracks, pores, etc) are limited. Besides the complexity of simulating all phenomena within the welding process, one of the main problems in welded integrated components is the existing coupling between welding quality metrics and product geometry. Welding quality can vary for every new product geometrical variant. Thus, there is a need of analyzing rapidly and virtually the interaction and sensitivity coefficients between design parameters and welding quality to predict welding producibility. This paper presents as a result an automated and interactive welding-producibility evaluation approach. This approach incorporates a data-based of welding-producibility criteria, as well as welding simulation and metamodel methods, which enable an interactive and automated evaluation of welding quality of a large number of product variants. The approach has been tested in an industrial use-case involving a multidisciplinary design process of aircraft components. The results from analyzing the welding-producibility of a set of design variants have been plotted together with the analysis results from other engineering disciplines resulting in an interactive tool built with parallel coordinate graphs. The approach proposed allows the generation and reuse of welding producibility information to perform analyses within a big spectrum of the design space in a rapid and interactive fashion, thus supporting designers on dealing with changes and taking fact-based decisions during the multidisciplinary design process.

Ensemble‐based surrogate modeling of microwave antennas using XGBoost algorithm

AbstractWith respect to the ever‐increasing performance needs in communication technologies, the need for accurate and computational efficient design optimization methods for high‐end microwave designs are also increased. Many studies had been proposed for the last decades for creating numerical modeling methods for having high accurate, stable, and computation efficient solutions suitable to be used in the design optimization process. Ensemble learning is a technique that the models are strategically created and combined to solve a specific computer intelligence challenge and primarily employed to boost the efficiency of a model or to lower the risk of a weak learner collection. Herein, XGBoosting‐based ensemble learning had been used for having surrogate models for three different microwave designs. In the first and second study cases, two microwave designs from the literature are taken into consideration for testing the performance of the proposed model with existing methods. Furthermore, a novel antenna design had been studied as a third study case with sparse training samples, to test the performance of the proposed modeling technique. As a result, the proposed method had achieved a remarkable performance for all the mentioned study cases both based on its own performance measures and its comparison with the counterpart algorithms.