The implementation of objectmath — a high-level programming environment for scientific computing

Viklund, Lars; Herber, Johan; Fritzson, Peter

doi:10.1007/3-540-55984-1_28

Cited by 11 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modelica has its roots in a language called dynamic modeling laboratory (DyMoLa) that was initially presented in the PhD thesis of Hilding Elmqvist [Elmqvist, 1978], together with several equation-based object-oriented languages from groups which participated early in the Modelica design effort (some examples are Neutral Model Format (NMF) [Sahlin et al , 1992], ObjectMath [Viklund et al , 1992] and Smile [Kloas et al , 1995]). Later on, the idea of allowing modeling and simulation of equation-based physical systems was further developed and eventually led to the language Modelica that is defined and managed by the Modelica Association.…”

Section: The Modelica Languagementioning

confidence: 99%

An Integrated Development Environment with Enhanced Domain-Specific Interactive Model Validation

Samlaus¹

2015

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Physical modeling plays an important role in many engineering domains. For engineers it is important to model real world objects in order to simulate and assess their behavior. This is particularly true for the development of new product components such as for example wind turbines. The suitability and durability of components needs to be investigated early in the design process to avoid costs that arise when faulty designs appear during component testing. Another domain where models are frequently used is model-driven software development. Data structures and data flow are modeled, for example using UML diagrams. Code is automatically generated from the models and is refined by user-written code. Advantages of model-driven software development are the high abstraction of the code by visualization in diagrams and specialized views which display details of design aspects. Additionally, code generators are usually well tested and design patterns are used in the generated code which leads to good quality and readability. Models for simulation of wind turbines are developed at Fraunhofer Institute for Wind Energy and Energy System Technology.Models with different levels of detail are used during the design process, starting with simple models for basic design decisions and evaluations to high-detailed models (such as Computational Fluid Dynamics (CFD) models for detailed flow analysis of rotor blades). The models defined are parameter-based and do not define a tool specific syntax. Hence, these models must be transferred to a tool-specific format for simulation. Design flaws that are encountered in low-level models have an impact on the detailed models. But equally important, problems encountered in highly detailed models may require changes in the basic models. This leads to the problem that all models that have been defined for a design study, including the transformation to simulation tool models, may need to be altered. To remedy this problem, model-driven software development is used. Models are created once in our simulation environment and simulation models for the different tools are derived from those models. This dissertation focuses on the development of components with different levels of detail with the language Modelica. In order to develop models quickly, the developer needs to be assisted in creating models that are valid regarding language compliance and structural constraints and that show the expected behaviour during simulation. The Modelica Integrated Development Environment (IDE) OneModelica is introduced that provides a data model for Modelica models with the same technology as the general wind turbine models described above. Hence, Modelica code can be generated from the parametric models using existing tools. Features like project-based development allow separating models with different levels of detail, allowing the user to focus on one topic and to create libraries for certain functionality. Interactive Model validation has been implemented to assist the user during development. Hence, the...

show abstract

Section: The Modelica Languagementioning

confidence: 99%

An Integrated Development Environment with Enhanced Domain-Specific Interactive Model Validation

Samlaus¹

2015

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

show abstract

“…We conclude the article with a brief summary of various points made. The first version of MathCode, released in 1998, was partly developed from the code generator in the ObjectMath environment [4,5]. The current version is almost completely rewritten and very much improved.…”

Section: Mathcode: a System For C++ Or Fortran Code Generation From Mmentioning

confidence: 99%

“…In [4] We define a function to create a list of numbers using the external functions. We now compile the package.…”

Section: External Functionsmentioning

confidence: 99%

“…In [4]:= SolveDiffusion1D@Nx_, dt_, nnz_, xasize_, U_D := ModuleA8k, x, dx, kt, rhsmat, colmat, rowmat, valmat, amat, asubmat, xamat<, H*initialize variables and arrays*L kt = 0; dx = 1 ê HNx -1L; rhsmat = Table@0., 8Nx<D; colmat = Table@0, 8nnz<D; rowmat = Table@0, 8nnz<D; valmat = Table@1., 8nnz<D; amat = Table@1., 8nnz<D; asubmat = Table@0, 8nnz<D; xamat = Table@0, 8xasize<D; H*define the matrices*L For@x = 1, x < 2, x = 1 + x, rhsmatPxT = 0.; H++kt; colmatPktT = x; rowmatPktT = x; valmatPktT = 1LD; ForAx = 2, x < Nx, x = 1 + x, rhsmatPxT = UPxT ê dt + HUP-1 + xT -2 UPxT + UP1 + xTL ë dx 2 ; H++kt; colmatPktT = x; rowmatPktT = x; valmatPktT = 1 ê dtLE; For@x = Nx, x < 1 + Nx, x = 1 + x, rhsmatPxT = 0.; H++kt; colmatPktT = x; rowmatPktT = x; valmatPktT = 1LD; H*transform the matrices into SuperLU format*L kt = 0; Do@Do@If@colmatPk1T ã k, ++kt; amatPktT = valmatPk1T; asubmatPktT = -1 + rowmatPk1TD, 8k1, 1, nnz<D, 8k, 1, Nx<D; kt = 0; Do@Do@If@colmatPk1T ã k, ++ktD, 8k1, 1, nnz<D; xamatP1 + kT = kt, 8k, 1, Nx<D; H*call SuperLU-based function to solve the matrix system A.x=B*L linsolvepp@Nx, Nx, nnz, 1, amat, asubmat, xamat, rhsmatD E…”

mentioning

confidence: 99%

See 1 more Smart Citation

MathCode: A System for C++ or Fortran Code Generation from Mathematica

Fritzson¹,

Engelson²,

Sheshadri³

2008

TMJ

Self Cite

View full text Add to dashboard Cite

MathCode is a package that translates a subset of Mathematica into a compiled language like Fortran or C++. The chief goal of the design of MathCode is to add extra performance and portability to the symbolic prototyping capabilities offered by Mathematica. This article discusses several important features of MathCode, such as adding type declarations, examples of functions that can be translated, ways to extend the compilable subset, and generating a stand-alone executable, and presents a few application examples. ‡ Introduction MathCode is a Mathematica add-on that translates a Mathematica program into C++ or Fortran 90. The subset of Mathematica that MathCode is able to translate involves purely numerical operations, and no symbolic operations. In the following sections we provide a variety of examples that show precisely what we mean. The code that is generated can be called and run from within Mathematica, as if you were running a Mathematica function.There are two important purposes that are served by MathCode. Firstly, the C++/ Fortran 90 code runs faster, typically by a factor of about a few hundreds (or about 50 to 100) over interpreted (compiled) Mathematica code, resulting in considerable performance gains, while still requiring hardly any knowledge of C++/Fortran 90 on the part of the user. Secondly, the generated code can also be executed as a stand-alone program outside Mathematica, offering a portability otherwise not possible. You should note, however, that these advantages come at some loss of generality since integer and floating point overflow are not trapped and switched to arbitrary precision as in standard Mathematica code. Here the user is responsible for ensuring an appropriate choice of scaling and input data to matica 6.

show abstract

“…Before work with the Modelica language was started, PELAB was involved in developing a language and tool called ObjectMath [149], an equation-based object-oriented (EOO) specification language for mathematical modeling. Several other groups developed related languages and tools, for example Dymola [37], NMF [121], Smile [72], and gPROMS [11].…”

Section: Introductionmentioning

confidence: 99%

Tools and Methods for Analysis, Debugging, and Performance Improvement of Equation-Based Models

Sjölund¹

2015

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Equation-based object-oriented (EOO) modeling languages such as Modelica provide a convenient, declarative method for describing models of cyber-physical systems. Because of the ease of use of EOO languages, large and complex models can be built with limited effort. However, current state-of-the-art tools do not provide the user with enough information when errors appear or simulation results are wrong. It is of paramount importance that such tools should give the user enough information to correct errors or understand where the problems that lead to wrong simulation results are located. However, understanding the model translation process of an EOO compiler is a daunting task that not only requires knowledge of the numerical algorithms that the tool executes during simulation, but also the complex symbolic transformations being performed.As part of this work, methods have been developed and explored where the EOO tool, an enhanced Modelica compiler, records the transformations during the translation process in order to provide better diagnostics, explanations, and analysis. This information is used to generate better error-messages during translation. It is also used to provide better debugging for a simulation that produces unexpected results or where numerical methods fail.Meeting deadlines is particularly important for real-time applications. It is usually essential to identify possible bottlenecks and either simplify the model or give hints to the compiler that enable it to generate faster code. When profiling and measuring execution times of parts of the model the recorded information can also be used to find out why a particular system model executes slowly.Combined with debugging information, it is possible to find out why this system of equations is slow to solve, which helps understanding what can be done to simplify the model. A tool with a graphical user interface has been developed to make debugging and performance profiling easier. Both debugging and profiling have been combined into a single view so that performance metrics are mapped to equations, which are mapped to debugging information.The algorithmic part of Modelica was extended with meta-modeling constructs (MetaModelica) for language modeling. In this context a quite general approach to debugging and compilation from (extended) Modelica to C code was developed. That makes it possible to use the same executable format for simulation executables as for compiler bootstrapping when the compiler written in MetaModelica compiles itself.Finally, a method and tool prototype suitable for speeding up simulations has been developed. It works by partitioning the model at appropriate places and compiling a simulation executable for a suitable parallel platform. Populärvetenskaplig sammanfattningSom ingenjör lär man sig att förstå världen genom ekvationer. För att beskriva eller modellera ett fysikaliskt system, till exempel vid utveckling av en produkt, vill ingenjören använda sig av de ekvationer som han eller hon lärt sig från böcker eller fö...

show abstract

The implementation of objectmath — a high-level programming environment for scientific computing

Cited by 11 publications

References 5 publications

An Integrated Development Environment with Enhanced Domain-Specific Interactive Model Validation

An Integrated Development Environment with Enhanced Domain-Specific Interactive Model Validation

MathCode: A System for C++ or Fortran Code Generation from Mathematica

Tools and Methods for Analysis, Debugging, and Performance Improvement of Equation-Based Models

Contact Info

Product

Resources

About