A Domain-Specific Compiler for a Parallel Multiresolution Adaptive Numerical Simulation Environment

2016 ◽  
Author(s):  
Samyam Rajbhandari ◽  
Jinsung Kim ◽  
Sriram Krishnamoorthy ◽  
Louis-Noel Pouchet ◽  
Fabrice Rastello ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Environment ◽  
Domain Specific

A New Hardware-in-the-Loop Test System for Electronic Control Unit of Dual-Clutch Transmission Vehicle

2012 ◽  
Vol 490-495 ◽  
pp. 13-18 ◽  
Author(s):  
Ran Chen ◽  
Lin Mi ◽  
Wei Tan
Keyword(s):  
Numerical Simulation ◽  
Electronic Control Unit ◽  
Control Unit ◽  
Test System ◽  
Testing System ◽  
Primary Concern ◽  
Hardware In The Loop ◽  
Electronic Control ◽  
Simulation Environment ◽  
Dual Clutch Transmission

Hardware-in-the-loop simulation (HILS) is a scheme that incorporates some hardware components of primary concern in the numerical simulation environment. This paper discusses the implementation and benefits of using the HIL testing system for electronic control unit of dual-clutch transmission (DCT) vehicle.

An Intelligent Numerical Simulation Environment on Component-Based Models: Part I

2003 ◽  
Author(s):  
Weiwen Deng ◽  
Edward Y. L. Gu
Keyword(s):  
Numerical Simulation ◽  
Expert System ◽  
Dynamic System ◽  
Real World ◽  
Object Oriented ◽  
Physical Nature ◽  
Component Model ◽  
Simulation Environment ◽  
System Technology ◽  
Complex Dynamic

This paper mainly discusses numerical simulation on a component-based model, in which a large complex dynamic system is presumably partitioned into and modeled by a number of interconnected components, which often corresponds to the physical nature in the real world. An independent component computation (ICC) method is proposed to deal numerically with component-based models. With ICC method, each component model is numerically solved independently of, and concurrently with others. This method, combined with the object-oriented methodology and expert system technology, leads to an intelligent numerical simulation environment, which is presented in detail in Part II.

An Intelligent Numerical Simulation Environment on Component-Based Models: Part II

2003 ◽  
Author(s):  
Weiwen Deng ◽  
Edward Y. L. Gu
Keyword(s):  
Numerical Simulation ◽  
Expert System ◽  
Natural Extension ◽  
Object Oriented ◽  
Independent Component ◽  
Simulation Environment ◽  
Simulation Technology ◽  
Systematic Method ◽  
Sequential Computation

An object-oriented simulation technology is first investigated, as a natural extension to the independent component computation (ICC) method under sequential computation environment, proposed in Part I. The intelligence, represented by an embedded expert system, is introduced. A systematic method and structure for establishing an intelligent numerical simulation environment is presented. A Windows-based software, Solver for Windows, has been developed to implement and verify the concepts presented in this paper.

Visual Steering of the Simulation Process in a Scientific Numerical Simulation Environment NCAS -

2000 ◽  
pp. 291-300 ◽  
Author(s):  
Shigeo Kawata ◽  
Choompol Boonmee ◽  
Akira Fujita ◽  
Takashi Nakamura ◽  
Takayuki Teramoto ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Environment ◽  
Simulation Process

scholarly journals Finite Element Assembly Using an Embedded Domain Specific Language

2015 ◽  
Vol 2015 ◽  
pp. 1-22
Author(s):  
Bart Janssens ◽  
Támas Bányai ◽  
Karim Limam ◽  
Walter Bosschaerts
Keyword(s):  
Numerical Simulation ◽  
Finite Element ◽  
Mathematical Formulation ◽  
Integral Form ◽  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific ◽  
Meta Programming ◽  
Programming Techniques ◽  
Methods Numerical

In finite element methods, numerical simulation of the problem requires the generation of a linear system based on an integral form of a problem. Using C++ meta-programming techniques, a method is developed that allows writing code that stays close to the mathematical formulation. We explain the specifics of our method, which relies on the Boost.Proto framework to simplify the evaluation of our language. Some practical examples are elaborated, together with an analysis of the performance. The abstraction overhead is quantified using benchmarks.

scholarly journals Verification of a Python-based TRANsport Simulation Environment for density-driven fluid flow and coupled transport of heat and chemical species

2020 ◽  
Vol 54 ◽  
pp. 67-77
Author(s):  
Thomas Kempka
Keyword(s):  
Numerical Simulation ◽  
Fluid Flow ◽  
Programming Language ◽  
Chemical Species ◽  
Coupled Processes ◽  
Numerical Code ◽  
Numerical Density ◽  
Simulation Environment ◽  
Transport Simulation ◽  
Transport Problems

Abstract. Numerical simulation has become an inevitable tool for improving the understanding on coupled processes in the geological subsurface and its utilisation. However, most of the available open source and commercial modelling codes do not come with flexible chemical modules or simply do not offer a straight-forward way to couple third-party chemical libraries. For that reason, the simple and efficient TRANsport Simulation Environment (TRANSE) has been developed based on the Finite Difference Method in order to solve the density-driven formulation of the Darcy flow equation, coupled with the equations for transport of heat and chemical species. Simple explicit, weighted semi-implicit or fully-implicit numerical schemes are available for the solution of the system of partial differential equations, whereby the entire numerical code is composed of less than 1000 lines of Python code, only. A diffusive flux-corrected advection scheme can be employed in addition to pure upwinding to minimise numerical diffusion in advection-dominated transport problems. The objective of the present study is to verify the numerical code implementation by means of benchmarks for density-driven fluid flow and advection-dominated transport. In summary, TRANSE exhibits a very good agreement with established numerical simulation codes for the benchmarks investigated here. Consequently, its applicability to numerical density-driven flow and transport problems is proven. The main advantage of the presented numerical code is that the implementation of complex problem-specific couplings between flow, transport and chemical reactions becomes feasible without substantial investments in code development using a low-level programming language, but the easy-to-read and -learn Python programming language.

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