The GUI-Based Simulation Optimization Platform for Fault Grey Prediction of Diesel Engine

2014 ◽  
Vol 687-691 ◽  
pp. 1049-1053
Author(s):  
Xiang Huan Zu ◽  
Jing Wang ◽  
Chuan Lei Yang ◽  
Xuan Qin
Keyword(s):  
Diesel Engine ◽  
Optimization Problem ◽  
Simulation Optimization ◽  
Simulation Platform ◽  
Grey Prediction ◽  
Post Processing ◽  
Input Output ◽  
Operating Environment ◽  
Modeling Process ◽  
Simulation Calculation

The simulation optimization platform for fault grey prediction of diesel engine is designed by MATLAB GUI, to ensure a reliable operating environment. The GM (1,1) model and improved models are presented in the platform to solve the optimization problem of prediction precision. The platform sets input, output, simulation calculation and post processing functions as one and is compiled to the executable program at last . As is proved by simulation examples, the platform simplifies the modeling process and improves the efficiency of the simulation, It is confirmed that the simulation platform for fault grey prediction of diesel engine is practical.

A Study on Efficient Computing Budget Allocation for a Two-Stage Problem

2021 ◽  
pp. 2050044
Author(s):  
Tianxiang Wang ◽  
Jie Xu ◽  
Jian-Qiang Hu
Keyword(s):  
Optimization Problem ◽  
Simulation Optimization ◽  
Risk Measure ◽  
Budget Allocation ◽  
Optimal Decision ◽  
Two Stage ◽  
Second Stage ◽  
Allocation Procedure ◽  
Optimal Computing Budget Allocation ◽  
Sequential Allocation

We consider how to allocate simulation budget to estimate the risk measure of a system in a two-stage simulation optimization problem. In this problem, the first stage simulation generates scenarios that serve as inputs to the second stage simulation. For each sampled first stage scenario, the second stage procedure solves a simulation optimization problem by evaluating a number of decisions and selecting the optimal decision for the scenario. It also provides the estimated performance of the system over all sampled first stage scenarios to estimate the system’s reliability or risk measure, which is defined as the probability of the system’s performance exceeding a given threshold under various scenarios. Usually, such a two-stage procedure is very computationally expensive. To address this challenge, we propose a simulation budget allocation procedure to improve the computational efficiency for two-stage simulation optimization. After generating first stage scenarios, a sequential allocation procedure selects the scenario to simulate, followed by an optimal computing budget allocation scheme that determines the decision to simulate in the second stage simulation. Numerical experiments show that the proposed procedure significantly improves the efficiency of the two-stage simulation optimization for estimating system’s reliability.

Imaging and post-processing of laser-induced fluorescence from NO in a diesel engine

1997 ◽  
Vol 64 (6) ◽  
pp. 717-724 ◽  
Author(s):  
T.M. Brugman ◽  
G.G.M. Stoffels ◽  
N. Dam ◽  
W.L. Meerts ◽  
J.J. ter Meulen
Keyword(s):  
Diesel Engine ◽  
Laser Induced Fluorescence ◽  
Post Processing

A new grey prediction model considering the data gap compensation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Che-Jung Chang ◽  
Chien-Chih Chen ◽  
Wen-Li Dai ◽  
Guiping Li
Keyword(s):  
Experimental Results ◽  
Small Data ◽  
Grey Prediction ◽  
Data Set ◽  
Forecasting Accuracy ◽  
Content Type ◽  
Background Values ◽  
Managerial Decisions ◽  
Modeling Process ◽  
Grey Models

PurposeThe purpose of this paper is to develop a small data set forecasting method to improve the effectiveness when making managerial decisions.Design/methodology/approachIn the grey modeling process, appropriate background values are one of the key factors in determining forecasting accuracy. In this paper, grey compensation terms are developed to make more appropriate background values to further improve the forecasting accuracy of grey models.FindingsIn the experiment, three real cases were used to validate the effectiveness of the proposed method. The experimental results show that the proposed method can improve the accuracy of grey predictions. The results further indicate that background values determined by the proposed compensation terms can improve the accuracy of grey model in the three cases.Originality/valuePrevious studies determine appropriate background values within the limitation of traditional grey modeling process, while this study makes new background values without the limitation. The experimental results would encourage researchers to develop more accuracy grey models without the limitation when determining background values.

Dynamic energy-efficient torque allocation algorithm for in-wheel motor-driven vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 1815-1825 ◽  
Author(s):  
Hao Chen ◽  
Peng Du ◽  
Yuan Wang ◽  
Dafeng Jin ◽  
Xiaomin Lian
Keyword(s):  
Energy Consumption ◽  
Fault Tolerance ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Driving Cycle ◽  
Simulation Platform ◽  
Allocation Algorithm ◽  
Computational Load ◽  
Torque Vectoring ◽  
Overall Performance

In-wheel motor-driven vehicle improves the overall performance with its torque vectoring system, which distributes the torque command of each motor. This paper proposes a novel torque allocation algorithm to dynamically optimize energy consumption of the vehicle. It splits the optimization problem into two sub-problems and obtains the executive torque of each side. The method also simplifies the solution by modification and discretization of feasible torque space, thus ensuring that there must be solvable and reducing online computational load. Two representative simulation cases—New European Driving Cycle and Fault Tolerance—have been selected and conducted through Cruise–Simulink co-simulation platform. The simulation verifies that the method decreases three energy consumption indices by 18.5%, 13.9%, and 14.7%, respectively, than those of the average allocation and coordinates all motors effectively based on vehicle’s operating status, which proves its practicability and robustness.

scholarly journals Evaluation and optimisation of the I/O scalability for the next generation of Earth system models: IFS CY43R3 and XIOS 2.0 integration as a case study

2021 ◽  
Author(s):  
Xavier Yepes-Arbós ◽  
Gijs van den Oord ◽  
Mario C. Acosta ◽  
Glenn D. Carver
Keyword(s):  
Message Passing Interface ◽  
Climate Model ◽  
Critical Path ◽  
Computational Cost ◽  
Earth System ◽  
Post Processing ◽  
Input Output ◽  
System Models ◽  
Computational Performance ◽  
Earth System Models

Abstract. Earth system models have considerably increased their spatial resolution to solve more complex problems and achieve more realistic solutions. However, this generates an enormous amount of model data which requires proper management. Some Earth system models use inefficient sequential Input/Output (I/O) schemes that do not scale well when many parallel resources are used. In order to address this issue, the most commonly adopted approach is to use scalable parallel I/O solutions that offer both computational performance and efficiency. In this paper we analyse the I/O process of the European Centre for Medium-Range Weather Forecasts (ECMWF) operational Integrated Forecasting System (IFS) CY43R3. IFS can use two different output schemes: a parallel I/O server developed by MeteoFrance used operationally, and an obsolescent sequential I/O scheme. The latter is the only scheme that is being exposed by the OpenIFS variant of IFS. “Downstream” Earth system models that have adopted older versions of an IFS derivative as a component – such as the EC-Earth 3 climate model – also face a bottleneck due to the limited I/O capabilities and performance of the sequential output scheme. Moreover it is often desirable to produce gridpoint-space Network Common Data Format (NetCDF) files instead of the IFS native spectral and gridpoint output fields in General Regularly-distributed Information in Binary form (GRIB) format, which requires the development of model-specific post-processing tools. We present the integration of the XML Input/Output Server (XIOS) 2.0 into IFS CY43R3. XIOS is an asynchronous Message Passing Interface (MPI) I/O server that offers features especially targeted at climate models: NetCDF format output files, inline diagnostics, regridding, and when properly configured, the capability to produce CMOR-compliant data. We therefore expect our work to reduce the computational cost of data-intensive (high-resolution) climate runs, thereby shortening the critical path of EC-Earth 4 experiments. The performance evaluation suggests that the use of XIOS 2.0 in IFS CY43R3 to output data achieves an adequate performance as well outperforming the sequential I/O scheme. Furthermore, when we also take into account the post-processing task, needed to convert GRIB files to NetCDF files, and also transform IFS spectral output fields to gridpoint space, our integration not only surpasses the sequential output scheme but also the IFS I/O server.

Research and Application of K-DOPS Algorithm in Virtual Maintenance Training System for Diesel Engine

2013 ◽  
Vol 805-806 ◽  
pp. 1911-1916 ◽  
Author(s):  
Jian Zhao Zhou ◽  
Xiao Pan Xu ◽  
Zi Cheng Zhu ◽  
Wei Jun Chu ◽  
Ting Xu ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Collision Detection ◽  
Training System ◽  
Virtual Simulation ◽  
Simulation Platform ◽  
Simulation Environment ◽  
Detection Algorithms ◽  
Maintenance System ◽  
Virtual Maintenance

Collision detection can effectively improve the authenticity, credibility and immersion of the virtual simulation environment. So this article mainly analyzed several classic collision detection algorithms, and put forward to use K-DOPS method in virtual maintenance system. Through EON simulation platform, the execution results of the algorithm were tested. The results show that the use of K-DOPS algorithm in collision detection can real-timely avoid collision and penetration between the part models in virtual maintenance training system for diesel engine, brilliantly enhancing the authenticity and immersion of the simulation environment.

Simulation Optimization for MRO Systems Operations

2017 ◽  
Vol 34 (02) ◽  
pp. 1750003 ◽  
Author(s):  
Pai Liu ◽  
Xi Zhang ◽  
Zhongshun Shi ◽  
Zewen Huang
Keyword(s):  
Optimization Problem ◽  
Simulation Optimization ◽  
Difficult Problem ◽  
Asymptotic Convergence ◽  
High Quality ◽  
Nested Partitions ◽  
Material Recovery ◽  
Processing Times ◽  
Key Characteristics ◽  
Two Hybrid

In this paper, we address the scheduling issues in a class of maintenance, repair and overhaul systems. By considering all key characteristics such as disassembly, material recovery uncertainty, material matching requirements, stochastic routings and variable processing times, the scheduling problem is formulated into a simulation optimization problem. To solve this difficult problem, we developed two hybrid algorithms based on nested partitions method and optimal computing budged allocation technology. Asymptotic convergence of these two algorithms is proved and numerical results show that the proposed algorithms can generate high quality solutions which outperform the performance of many heuristics.

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