scholarly journals A Universal MDO Framework Based on the Adaptive Discipline Surrogate Model

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Hua Su ◽  
Chun-lin Gong ◽  
Liang-xian Gu
Keyword(s):  
Surrogate Model ◽  
Multidisciplinary Design Optimization ◽  
Sampling Method ◽  
Solution Method ◽  
Iteration Process ◽  
Surrogate Models ◽  
High Fidelity ◽  
Sampling Process ◽  
Model Update ◽  
Parallel Environment

High time-consuming computation has become an obvious characteristic of the modern multidisciplinary design optimization (MDO) solving procedure. To reduce the computing cost and improve solving environment of the traditional MDO solution method, this article introduces a novel universal MDO framework based on the support of adaptive discipline surrogate model with asymptotical correction by discriminative sampling. The MDO solving procedure is decomposed into three parts: framework level, architecture level, and discipline level. Framework level controls the MDO solving procedure and carries out convergence estimation; architecture level executes the MDO solution method with discipline surrogate models; discipline level analyzes discipline models to establish adaptive discipline surrogate models based on a stochastic asymptotical sampling method. The MDO solving procedure is executed as an iterative way included with discipline surrogate model correcting, MDO solving, and discipline analyzing. These are accomplished by the iteration process control at the framework level, the MDO decomposition at the architecture level, and the discipline surrogate model update at the discipline level. The framework executes these three parts separately in a hierarchical and modularized way. The discipline models and disciplinary design point sampling process are all independent; parallel computing could be used to increase computing efficiency in parallel environment. Several MDO benchmarks are tested in this MDO framework. Results show that the number of discipline evaluations in the framework is half or less of the original MDO solution method and is very useful and suitable for the complex high-fidelity MDO problem.

scholarly journals Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models

2022 ◽  
Vol 7 (01) ◽  
pp. 31-51
Author(s):  
Tanya Peart ◽  
Nicolas Aubin ◽  
Stefano Nava ◽  
John Cater ◽  
Stuart Norris
Keyword(s):  
Surrogate Model ◽  
Lift Coefficient ◽  
Simulation Method ◽  
Surrogate Models ◽  
High Fidelity ◽  
Data Points ◽  
Velocity Prediction ◽  
The Difference ◽  
Fidelity Model ◽  
Selection Of

Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.

Pengaruh Citra Merek, Kualitas Produk Dan Harga Terhadap Kepuasan Konsumen Pada Bisnis Franchise Minuman

2017 ◽  
Vol 24 (2) ◽  
pp. 144-153
Author(s):  
Yunita Fitri Wahyuningtyas
Keyword(s):  
Regression Analysis ◽  
Marketing Strategy ◽  
Product Quality ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Sampling Method ◽  
Sampling Technique ◽  
Sampling Process ◽  
And Function ◽  
Simultaneous Influence

This research is conducted upon the emergence of many companies producing the same product of the same kind and function. It leads to the urgency of proper and well planned marketing strategy. This research aims to investigate how far the influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. This research utilizes 5 likert scale questionnaire which is tested by using multiple regression analysis to reveal whether or not there is partial and simultaneous influence of branding, product quality, and price toward consumer’s satisfaction in beverage franchise business. Sampling method is accidental sampling technique, in which sample of particular population is taken based on the accessibility and availability of the sample during the sampling process. Sample used is 100 samples among consumers or customers of Mang Endy Milkshake. The result shows that branding, product quality, and price influence consumer’s satisfaction in beverage franchise business.

Efficient Multi-Objective Optimization of Labyrinth Seal Leakage in Steam Turbines Based on Hybrid Surrogate Models

2016 ◽  
Author(s):  
Kevin Cremanns ◽  
Dirk Roos ◽  
Simon Hecker ◽  
Peter Dumstorff ◽  
Henning Almstedt ◽  
...  
Keyword(s):  
Steam Turbine ◽  
Surrogate Model ◽  
Power Plants ◽  
Polynomial Regression ◽  
Renewable Energy Sources ◽  
Surrogate Models ◽  
Future Market ◽  
Steam Turbines ◽  
Model Calculations ◽  
Highly Efficient

The demand for energy is increasingly covered through renewable energy sources. As a consequence, conventional power plants need to respond to power fluctuations in the grid much more frequently than in the past. Additionally, steam turbine components are expected to deal with high loads due to this new kind of energy management. Changes in steam temperature caused by rapid load changes or fast starts lead to high levels of thermal stress in the turbine components. Therefore, todays energy market requires highly efficient power plants which can be operated under flexible conditions. In order to meet the current and future market requirements, turbine components are optimized with respect to multi-dimensional target functions. The development of steam turbine components is a complex process involving different engineering disciplines and time-consuming calculations. Currently, optimization is used most frequently for subtasks within the individual discipline. For a holistic approach, highly efficient calculation methods, which are able to deal with high dimensional and multidisciplinary systems, are needed. One approach to solve this problem is the usage of surrogate models using mathematical methods e.g. polynomial regression or the more sophisticated Kriging. With proper training, these methods can deliver results which are nearly as accurate as the full model calculations themselves in a fraction of time. Surrogate models have to face different requirements: the underlying outputs can be, for example, highly non-linear, noisy or discontinuous. In addition, the surrogate models need to be constructed out of a large number of variables, where often only a few parameters are important. In order to achieve good prognosis quality only the most important parameters should be used to create the surrogate models. Unimportant parameters do not improve the prognosis quality but generate additional noise to the approximation result. Another challenge is to achieve good results with as little design information as possible. This is important because in practice the necessary information is usually only obtained by very time-consuming simulations. This paper presents an efficient optimization procedure using a self-developed hybrid surrogate model consisting of moving least squares and anisotropic Kriging. With its maximized prognosis quality, it is capable of handling the challenges mentioned above. This enables time-efficient optimization. Additionally, a preceding sensitivity analysis identifies the most important parameters regarding the objectives. This leads to a fast convergence of the optimization and a more accurate surrogate model. An example of this method is shown for the optimization of a labyrinth shaft seal used in steam turbines. Within the optimization the opposed objectives of minimizing leakage mass flow and decreasing total enthalpy increase due to friction are considered.

Surrogate Population Models for Large-Scale Neural Simulations

2015 ◽  
Vol 27 (6) ◽  
pp. 1186-1222 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Surrogate Model ◽  
Latent Variables ◽  
Large Scale ◽  
Neural Model ◽  
Surrogate Models ◽  
Weighted Sums ◽  
Neural Systems ◽  
Neuron Models ◽  
Linear Filters ◽  
Population Activity

Because different parts of the brain have rich interconnections, it is not possible to model small parts realistically in isolation. However, it is also impractical to simulate large neural systems in detail. This article outlines a new approach to multiscale modeling of neural systems that involves constructing efficient surrogate models of populations. Given a population of neuron models with correlated activity and with specific, nonrandom connections, a surrogate model is constructed in order to approximate the aggregate outputs of the population. The surrogate model requires less computation than the neural model, but it has a clear and specific relationship with the neural model. For example, approximate spike rasters for specific neurons can be derived from a simulation of the surrogate model. This article deals specifically with neural engineering framework (NEF) circuits of leaky-integrate-and-fire point neurons. Weighted sums of spikes are modeled by interpolating over latent variables in the population activity, and linear filters operate on gaussian random variables to approximate spike-related fluctuations. It is found that the surrogate models can often closely approximate network behavior with orders-of-magnitude reduction in computational demands, although there are certain systematic differences between the spiking and surrogate models. Since individual spikes are not modeled, some simulations can be performed with much longer steps sizes (e.g., 20 ms). Possible extensions to non-NEF networks and to more complex neuron models are discussed.

scholarly journals Development of passive sampler POCIS for the analysis of polar pesticides in surface water

2015 ◽  
Vol 18 (2) ◽  
pp. 132-144
Author(s):  
Trang Thi Nhu Tran ◽  
Ty Thi Pham ◽  
Hai Lam Son Truong
Keyword(s):  
Surface Water ◽  
Environmental Factors ◽  
Pesticide Residues ◽  
Passive Sampling ◽  
Sampling Method ◽  
Passive Sampler ◽  
Polar Pesticides ◽  
Sampling Process ◽  
European Standards ◽  
First Time

The first time in Vietnam a passive sampling method has been developed to analyse the polar pesticides in surface water. The initial investigations of POCIS were performed for 7 polar pesticides as simazine,thiodicarb, carbofuran, chlortoluron, atrazine, isoproturon, and diuron. We determined the sampling rates RS for these substances ranged from 0.369 to 0.962 L day- 1. The obtained values of ku and RS showed the important influence of environmental factors such as flow on the ability to integrate polar pesticides in passive sampling process. This method can be applied to determine these 7 polar pesticides in surface water at trace levels according to European standards for pesticide residues in water (< 0.1 μg L-1).

scholarly journals Aeromechanical Analysis of a Complete Wind Turbine Using Nonlinear Frequency Domain Solution Method

2020 ◽  
Author(s):  
Shine Win Naung ◽  
Mohammad Rahmati ◽  
Hamed Farokhi
Keyword(s):  
Wind Turbine ◽  
Frequency Domain ◽  
Solution Method ◽  
Computational Cost ◽  
Turbine Rotor ◽  
Forced Response ◽  
High Fidelity ◽  
Nonlinear Frequency ◽  
Cfd Method ◽  
Turbine Model

Abstract The high-fidelity computational fluid dynamics (CFD) simulations of a complete wind turbine model usually require significant computational resources. It will require much more resources if the fluid-structure interactions between the blade and the flow are considered, and it has been the major challenge in the industry. The aeromechanical analysis of a complete wind turbine model using a high-fidelity CFD method is discussed in this paper. The distinctiveness of this paper is the application of the nonlinear frequency domain solution method to analyse the forced response and flutter instability of the blade as well as to investigate the unsteady flow field across the wind turbine rotor and the tower. This method also enables the aeromechanical simulations of wind turbines for various inter blade phase angles in a combination with a phase shift solution method. Extensive validations of the nonlinear frequency domain solution method against the conventional time domain solution method reveal that the proposed frequency domain solution method can reduce the computational cost by one to two orders of magnitude.

scholarly journals Surrogate-based pumping optimization of coastal aquifers under limited computational budgets

2017 ◽  
Vol 20 (1) ◽  
pp. 164-176 ◽  
Author(s):  
Vasileios Christelis ◽  
Rommel G. Regis ◽  
Aristotelis Mantoglou
Keyword(s):  
Coastal Aquifers ◽  
Surrogate Model ◽  
Coastal Aquifer ◽  
Surrogate Models ◽  
Salt Transport ◽  
Direct Optimization ◽  
Aquifer Management ◽  
Pumping Optimization ◽  
Local Improvement ◽  
Coastal Aquifer Management

Abstract The computationally expensive variable density and salt transport numerical models hinder the implementation of simulation-optimization routines for coastal aquifer management. To reduce the computational cost, surrogate models have been utilized in pumping optimization of coastal aquifers. However, it has not been previously addressed whether surrogate modelling is effective given a limited number of numerical simulations with the seawater intrusion model. To that end, two surrogate-based optimization (SBO) frameworks are employed and compared against the direct optimization approach, under restricted computational budgets. The first, a surrogate-assisted algorithm, employs a strategy which aims at a fast local improvement of the surrogate model around optimal values. The other, balances global and local improvement of the surrogate model and is applied for the first time in coastal aquifer management. The performance of the algorithms is investigated for optimization problems of moderate and large dimensionalities. The statistical analysis indicates that for the specified computational budgets, the sample means of the SBO methods are statistically significantly better than those of the direct optimization. Additionally, the selection of cubic radial basis functions as surrogate models, enables the construction of very fast approximations for problems with up to 40 decision variables and 40 constraint functions.

An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging

2017 ◽  
Vol 50 (1) ◽  
pp. 145-163 ◽  
Author(s):  
Jiexiang Hu ◽  
Qi Zhou ◽  
Ping Jiang ◽  
Xinyu Shao ◽  
Tingli Xie
Keyword(s):  
Adaptive Sampling ◽  
Sampling Method ◽  
Surrogate Models ◽  
Variable Fidelity
Sign in / Sign up

Export Citation Format

Share Document