Simulation-based Optimal Design Research Articles

Multi-criteria analysis on the simulation-based optimal design of a new stack-type natural ventilation system for industrial buildings

This study focuses on optimizing a new stack-type natural ventilation system (NVS) designed for industrial buildings. Forty-five different designs were formed in terms of the different NVS sizes. The formed designs were numerically analyzed to determine the performance of the designs. The performance of the designed system was evaluated by exergy analysis. Additionally, the designs were evaluated economically using the net present value (NPV) method. Then multi-criteria decision-making analysis was conducted to determine the optimal solution under the results of economic and exergetic outputs. The input values were selected as non-direct parameters to obtain a homogenous ventilation. As a result, it was determined that implementing this designed model would result in annual savings of 176,922.14 m3 of natural gas, 331.71 tons of CO2 emissions, and 0.716 GWh of electricity. The optimum system was determined as investable with an NPV value of 35,475.20 $ for a 20-year lifetime.

Stress Cone Effect Analysis and Optimum Design of Extra High-Voltage Cable Joint Using Neural Networks

The major problem in high-voltage cable accessories is the electric field stress caused by the high-voltage level at the cable junction. In this paper, a neural network and simulation-based optimal design for extra high-voltage (EHV) cable accessory is proposed to ensure the optimum electrical design. The different kinds of geometries of the EHV underground cable accessory are realized in the Comsol Multiphysics software program, and the electric field analyses are conducted based on the finite element method. The design of high-voltage underground cable accessories is examined both within and without the optimal stress cone geometry. A neural network-based model is developed according to the analysis results obtained in the simulation. Thanks to this model, the geometry of the stress cone is determined. In the simulation-based analysis, the electric field stress caused by the high-voltage level at critical points is controlled with an optimum joint design using neural networks. The proposed model will contribute to EHV cable joint design at different stress levels.

Mathematical Modelling for Furnace Design Refining Molten Aluminum

The design of an aluminium melting furnace has faced two challenges: mathematical modelling and simulative optimization. This paper first uses fluid dynamics to model the aluminium process mathematically. Then, the model is utilized to simulate a round shaped reverberatory furnace for melting aluminium alloys. In order to achieve the highest thermal efficiency of the furnace, modelling and simulation are performed to predict complex flow patterns, geometries, temperature profiles of the mixture-gas air through the main chamber, as well as the melting tower attached to the furnace. The results led to the establishment of optimal position and angle of the burner, which are validated through physical experiments, ensuring recirculation of the combustion gases through the melting chamber and the melting tower. Furthermore, a proper arrangement of refractory materials is derived to avoid heat losses through the outer surface of the furnace. Temperature profiles are also determined for the optimization to arrive at the final design of the furnace. Compared with manual designs previously practiced, the simulation-based optimal design of furnaces offers excellent guidance, an increase in the aluminium processing and magnesium removal for more refined alloys, and an increased processing rate of aluminium chip accession.

Effects of harvest time, fruit size and cultivar on the bulk optical properties of Satsuma mandarin

As Satsuma mandarin is a multi-layered fruit, the optical properties of the different tissue layers influence the performance of spectroscopy-based quality detection models. Therefore, the variation in the bulk optical properties (BOP) of the inner (juice vesicles) and outer (flavedo) tissue layer of Satsuma mandarin was investigated for different harvest times, fruit sizes and cultivars. Satsuma mandarin fruit from three different cultivars (Iwasaki, Okitsu and Goku Wase) were harvested at three different times around the recommended harvest date and the BOP of the inner and outer tissue layers were estimated from double integrating spheres measurements. Along the harvest time, the absorption peak related to carotenoids in the bulk absorption coefficient (μa) spectrum increased in both tissue layers, while the values attributed to chlorophylls decreased in the flavedo. Moreover, a slightly increasing trend over the harvest time was observed in the bulk scattering coefficient (μs) spectra of juice vesicles, especially at the early stage. The μa values of both tissue layers and the μs values of juice vesicles deviated among different fruit sizes, which can be related to the development stage. Smaller fruit presented a higher carotenoids concentration in juice vesicles, an advanced color change in flavedo throughout the maturation stages and higher μs values in juice vesicles at earlier maturation stages. The μa differences in pigment absorption among different cultivars were related to the maturation order. Earlier maturing cultivars possessed higher μa values related to carotenoids absorption in juice vesicles and advanced color changes in flavedo. The μs values of the flavedo differed among the cultivars and these differences were enhanced during maturation. These insights in the sources of variation in the bulk optical properties of Satsuma mandarin tissues can be used for simulation-based optimal design of an optical measurement configuration and interpretation of the acquired signals in spectroscopy-based quality detection and monitoring.

Application of artificial intelligence and evolutionary algorithms in simulation-based optimal design of a piezoelectric energy harvester

This paper tackles the problem of finding the optimal design parameters for a piezoelectric energy harvester. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that leads to higher energy harvesting efficiency and also enhances the obtained electrical power. The basis of the optimization platform is a numerical model of the energy harvesting system operating during electrical transient of charging an external storage capacitor. The model consists of a cantilever beam partially coated with piezoelectric patches, a non-linear interfacing and conditioning circuit, and a storage device. The numerical model simulates a complete energy harvesting scenario from piezoelectric transduction, to power enhancement and conditioning through interfacing circuit and energy storage. Two different case studies are considered for beams under harmonic tip-force, and harmonic base-excitation. Since performing multiple simulations in order to evaluate the objective function is computationally expensive and imposes time and space (memory) complexities, a more efficient Neural Network (NN) model is first trained based on a set of training data obtained from the numerical model. Performance and accuracy of the NN training is studied using available statistical methods. Second, a Genetic Algorithm (GA) optimization performs a block-box optimization procedure, using the trained Neural Network model for objective function evaluation. Finally, a thorough analysis of the optimal design parameters obtained from the optimization process is provided.

Simulation-based Bayesian optimal design for multi-factor accelerated life tests

ABSTRACTWe consider simulation-based methods for the design of multi-stress factor accelerated life tests (ALTs) in a Bayesian decision theoretic framework. Multi-stress factor ALTs are challenging due to the increased number of simulation runs required as a result of stress factor-level combinations. We propose the use of Latin hypercube sampling to reduce the simulation cost without loss of statistical efficiency. Exploration and optimization of expected utility function is carried out by a developed algorithm that utilizes Markov chain Monte Carlo methods and nonparametric smoothing techniques. A comparison of proposed approach to a full grid simulation is provided to illustrate computational cost reduction.

Simulation-based fully Bayesian experimental design for mixed effects models

Bayesian inference has commonly been performed on nonlinear mixed effects models. However, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models, especially those that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. Fully Bayesian experimental designs for nonlinear mixed effects models are presented, which involve the use of simulation-based optimal design methods to search over both continuous and discrete design spaces. The design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and can be addressed using the methods presented.

Likelihood-free simulation-based optimal design with an application to spatial extremes.

In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures.Electronic supplementary materialThe online version of this article (doi:10.1007/s00477-015-1067-8) contains supplementary material, which is available to authorized users.

SIMULATION-BASED OPTIMAL DESIGN OF HFCVD EQUIPMENT ADOPTED FOR MASS PRODUCTION OF DIAMOND FILMS ON INNER-HOLE SURFACES

HFCVD diamond films have been extensively applied on inner-hole surfaces of some wear resistant components as protective coatings, the mass production of which puts forward a high request to the HFCVD equipment. In this paper, adopting a typical kind of drawing die as the substrate, several different substrate configurations are proposed to expand the capacity of the equipment. Temperature distributions on inner-hole surfaces of the substrates are respectively predicted and compared by the finite volume method (FVM) simulations. In summary, for parallel configurations, proper-designed auxiliary devices (e.g. the heat-insulated plate, the water-cooled plate and the heat-insulated cushion block) can provide sufficient effects on the uniformity of the substrate temperature distribution. Moreover, without any auxiliary devices, homogeneous substrate temperature distributions can also be obtained using circular pattern configurations (radial, hexagon and triangular). Accordingly, the HFCVD equipment referring to the design concept of the triangular configuration is trial-manufactured. In the verification experiments, similar substrate temperature values are detected in three different modules, and as-deposited microcrystalline diamond (MCD) and nanocrystalline diamond (NCD) films both present uniform grain size, thickness and quality, well validating the rationality and correctness of the simulation-based optimal design.

Life-cycle cost benefit analysis and optimal design of small scale active storage system for building demand limiting

This paper presents a quantitative analysis on the life-cycle cost saving potentials of active cold storage systems concerning the operational cost, initial investment and the space cost. A simulation-based optimal design method is developed and used to optimize the storage capacity and analyze the impact of the capacity on the life-cycle cost saving potentials. Using the actual load profiles of three real buildings of different sizes, the life-cycle cost saving potentials under demand limiting control are investigated under Hong Kong electricity price structure. The optimal storage capacities, monthly/annual operational cost savings and corresponding peak demand set-points are obtained from using the marginal decision rule. Results show that small scale storages can offer substantial annual net cost saving. In addition, it can help the grid to achieve higher grid power reliability by enhancing the demand response ability of buildings.

Optimal experimental design for nonlinear ill-posed problems applied to gravity dams

The safe operation of gravity dams requires continuous monitoring in order to detect any changes concerning the stability of these constructions. Damage which may result from cyclic loading, variation in temperature, aging, chemical reactions, etc needs to be identified as fast and as reliable as possible. Generally, existing dams are well monitored by several types of measurement devices which log different physical quantities. The monitoring practice is according to official guidelines and the engineer’s experience. The aim of this paper is to perform a simulation-based optimal design for the monitoring of existing dams. Therefore, a design criterion which is based on average mean-squared reconstruction errors is derived. The reconstructions are obtained as regularized solutions of the nonlinear, inverse and ill-posed problem of damage identification. The basis for these investigations is a hydro-mechanically coupled model applied to gravity dams. Damaged zones in the dams are described by a smeared crack model, i.e. by spatially varying material properties. The inherent correlation of changes in the dominating parameters is explicitly considered during the inverse analysis. For the solution and regularization of the inverse problem, the iteratively regularized Gauss–Newton method is applied. Numerical results of the inverse analysis and the design process allow assessments of the applicability of the strategies proposed here.

A simulation-based optimal design and analysis method for designing a train overhaul maintenance facility

This paper presents a simulation-based optimal design and analysis method for designing a train overhaul maintenance facility. Because the train is composed of one or more cars, the authors design the simulation model after analysing the operation of the train according to the train's following parts: train, car, car body, and wheel. The authors consider the critical (dependent) factors and design (independent) parameters for the simulation analysis. Therefore, multi-criteria decision making (MCDM) is proposed for selecting alternatives, and simulation optimization is used for finding the optimal design parameters of the selected alternative. A case study for the above approach is applied to the electric locomotive overhaul maintenance facility. This paper provides a comprehensive framework for a train overhaul maintenance facility design using MCDM and the simulation optimization.

Simulation‐based Bayesian optimal design of aircraft trajectories for air traffic management

AbstractIn this paper, we consider a specific Air Traffic Management problem, where multiple aircraft in a specific region are required to reach a different target zone in the minimum expected time, while maintaining safe separation. The problem is complicated by the presence of random wind disturbances. We propose a realistic policy to automatically generate optimal and safe maneuvres for each aircraft. The parameters of the optimal policy are computed using a sequential Monte Carlo approach. Copyright © 2010 John Wiley & Sons, Ltd.

Simulation-based optimal design of α-β-γ-δ filter

The existing third-order tracker known as α-β-γ filter has been used for target tracking and predicting for years. The filter can track the target's position and velocity, but not the acceleration. To extend its capability, a new fourth-order target tracker called α-β-γ-Δ filter is proposed. The main objective of this study was to find the optimal set of filter parameters that leads to minimum position tracking errors. The tracking errors between using the α-β-γ filter and the α-β-γ-Δ filter are compared. As a result, the new filter exhibits significant improvement in position tracking accuracy over the existing third-order filter, but at the expense of computational time in search of the optimal filter. To reduce the computational time, a simulation-based optimization technique via Taguchi method is introduced.

Optimal Bayesian Design for Patient Selection in a Clinical Study

Bayesian experimental design for a clinical trial involves specifying a utility function that models the purpose of the trial, in this case the selection of patients for a diagnostic test. The best sample of patients is selected by maximizing expected utility. This optimization task poses difficulties due to a high-dimensional discrete design space and, also, to an expected utility formula of high complexity. A simulation-based optimal design method is feasible in this case. In addition, two deterministic algorithms that perform a systematic search over the design space are developed to address the computational issues.

Simulation-based Optimal Design for Accelerated Degradation Tests with Mixed-effects Model

Accelerated degradation test(ADT) is widely used to evaluate the reliability of highly reliable products with long life if there exists a product quality characteristic whose degradation over time can be related to reliability.Different ADT plans induce very different estimation of reliability and life characteristics with different test cost.It is a main challenge that how to design accelerated degradation test plans which induce precise results with low cost.Aimed at an occasion that analytical solution of optimal plan does not exist,a new method of Monte Carlo simulation-based optimal design for ADT is presented to convert a difficult design problem to a rather simple problem of statistical analysis for application.Optimal design for constant stress accelerated degradation test of light emitting diode is taken as an illustrative example to demonstrate the validity of the proposed method.Results of sensitivity analysis of the optimal test plan show that the plan is robust when the model deviation is small.In application,optimal plans of other types of accelerated degradation test can be obtained by the same method and process as in the example.

Support Tools for Simulation-Based Optimal Design of Power Networks With Embedded Power Electronics

This paper presents a gradient-based nonlinear optimization wrapper for an electromagnetic transient simulation program to assist in the design of hardware and control parameters of flexible AC transmission system (FACTS) apparatus. A new second-order sensitivity analysis method is introduced that quantifies the sensitivity of the optimal solution to parameter uncertainties. Using this approach, the sensitivity of the harmonic elimination capability of a pulsewidth-modulated voltage-source converter to variations in switching angles is investigated. This paper also adapts the method of Pareto optimization to the simulation-based design of FACTS apparatus when multiple design objectives are to be satisfied. The developed Pareto optimization tool generates a Pareto frontier plot to visualize the design tradeoffs between the multiple objectives. The effectiveness of the tool is demonstrated through a design example that selects optimal values for the DC-bus capacitor and the control settings of a STATCOM.

Probabilistic Analytical Target Cascading: A Moment Matching Formulation for Multilevel Optimization Under Uncertainty

Abstract Analytical target cascading (ATC) is a methodology for hierarchical multilevel system design optimization. In previous work, the deterministic ATC formulation was extended to account for random variables represented by expected values to be matched among subproblems and thus ensure design consistency. In this work, the probabilistic formulation is augmented to allow the introduction and matching of additional probabilistic characteristics. A particular probabilistic analytical target cascading (PATC) formulation is proposed that matches the first two moments of interrelated responses and linking variables. Several implementation issues are addressed, including representation of probabilistic design targets, matching responses and linking variables under uncertainty, and coordination strategies. Analytical and simulation-based optimal design examples are used to illustrate the new formulation. The accuracy of the proposed PATC formulation is demonstrated by comparing PATC results to those obtained using a probabilistic all-in-one formulation.

Simulation-based optimal design of heavy trucks by model-based decomposition: an extensive analytical target cascading case study

We present the findings of an extensive case study for the decomposed, simulation-based, optimal design of an advanced technology heavy truck by means of analytical target cascading. The use of a series hybrid-electric propulsion system, in-hub motors, and variable height suspensions is considered with the intention of improving both commercial and military design attributes according to a dual-use philosophy. Emphasis is given to fuel economy, ride, and mobility characteristics. The latter are predicted by appropriately developed analytical and simulation models. This article builds on previous work and focuses on recent efforts to refine the applied methodologies and draw final conclusions.

Sample reuse Simulation in Optimal Design forTmax in Pharmacokinetic Experiments

SummaryT max and Cmax are important pharmacokinetic parameters in drug development processes. Often a nonparametric procedure is needed to estimate them when model independence is required. This paper proposes a simulation-based optimal design procedure for finding optimal sampling times for nonparametric estimates of Tmax and Cmax for each subject, assuming that the drug concentration follows a non-linear mixed model. The main difficulty of using standard optimal design procedures is that the property of the nonparametric estimate is very complicated. This procedure uses a sample reuse simulation to calculate the design criterion, which is an integral of multiple dimension, so that effective optimization procedures such as Newton-type procedures can be used directly to find optimal designs. This procedure is used to construct optimal designs for an open one-compartment model. An approximation based on the Taylor expansion is also derived and showed results that were consistent with those based on the sample reuse simulation.