Computational engineering

Computational Engineering methods and frameworks include:

• High performance computing and techniques to gain efficiency (through change in computer architecture, parallel algorithms etc.)
• Modeling and simulation
• Algorithms for solving discrete and continuous problems
• Analysis and visualization of data
• Mathematical foundations: Numerical and applied linear algebra, initial & boundary value problems, Fourier analysis, optimization
• Data Science for developing methods and algorithms to handle and extract knowledge from large scientific data

With regard to computing, computer programming, algorithms, and parallel computing play a major role in Computational Engineering. The most widely used programming language in the scientific community is FORTRAN.{{Cite web|title=Why is fortran extensively used in scientific computing and not any other language?|url=https://www.researchgate.net/post/Why_is_fortran_extensively_used_in_scientific_computing_and_not_any_other_language|website=ResearchGate|language=en|access-date=2020-05-23}} Recently, C++ and C have increased in popularity over FORTRAN. Due to the wealth of legacy code in FORTRAN and its simpler syntax, the scientific computing community has been slow in completely adopting C++ as the lingua franca. Because of its very natural way of expressing mathematical computations, and its built-in visualization capacities, the proprietary language/environment MATLAB is also widely used, especially for rapid application development and model verification. Python along with external libraries (such as NumPy, SciPy, Matplotlib) has gained some popularity as a free and Copycenter alternative to MATLAB.

There are a number of Free and Open-Source Software (FOSS) tools that support Computational Engineering.

• OpenSCAD was released in 2010 and allows the scripted generation of CAD models, which can form the basis for Computational Engineering Models.
• [https://github.com/CadQuery/cadquery CadQuery] uses Python to generate CAD models and is based on the OpenCascade framework. It is released under the Apache 2.0 Open-Source License.
• [https://github.com/leap71/PicoGK PicoGK]is an open-source framework for Computational Engineering which was released under the Apache 2.0 Open-Source License.

File:Elmer-pump-heatequation.png on a pump casing model using the finite element method.]]

Computational Engineering finds diverse applications, including in:

• Aerospace Engineering and Mechanical Engineering: combustion simulations, structural dynamics, computational fluid dynamics, computational thermodynamics, computational solid mechanics, vehicle crash simulation, biomechanics, trajectory calculation of satellites
• Astrophysical systems
• Battlefield simulations and military gaming, homeland security, emergency response
• Biology and Medicine: protein folding simulations (and other macromolecules), bioinformatics, genomics, computational neurological modeling, modeling of biological systems (e.g., ecological systems), 3D CT ultrasound, MRI imaging, molecular bionetworks, cancer and seizure control
• Chemistry: calculating the structures and properties of chemical compounds/molecules and solids, computational chemistry/cheminformatics, molecular mechanics simulations, computational chemical methods in solid state physics, chemical pollution transport
• Civil Engineering: finite element analysis, structures with random loads, construction engineering, water supply systems, transportation/vehicle modeling
• Computer Engineering, Electrical Engineering, and Telecommunications: VLSI, computational electromagnetics, semiconductor modeling, simulation of microelectronics, energy infrastructure, RF simulation, networks
• Epidemiology: influenza spread
• Environmental Engineering and Numerical weather prediction: climate research, Computational geophysics (seismic processing), modeling of natural disasters
• Finance: derivative pricing, risk management
• Industrial Engineering: discrete event and Monte-Carlo simulations (for logistics and manufacturing systems for example), queueing networks, mathematical optimization
• Material Science: glass manufacturing, polymers, and crystals
• Nuclear Engineering: nuclear reactor modeling, radiation shielding simulations, fusion simulations
• Petroleum engineering: petroleum reservoir modeling, oil and gas exploration
• Physics: Computational particle physics, automatic calculation of particle interaction or decay, plasma modeling, cosmological simulations
• Transportation

• Applied mathematics
• Computational science
• Computational mathematics
• Computational fluid dynamics
• Computational electromagnetics
• Engineering mathematics
• High-performance computing
• Grand Challenges
• List of computer-aided engineering software
• List of open-source engineering software
• Numerical analysis
• Modeling and simulation
• Multiphysics

{{reflist}}

• [http://www.ices.utexas.edu/ Oden Institute for Computational Engineering and Sciences]
• [http://www.articleworld.org/index.php/Computational_engineering Scope of Computational engineering]
• [http://www.siam.org/students/resources/report.php Society of Industrial and Applied Mathematics]
• [http://www.ic2e.org/ International Centre for Computational Engineering (IC2E)]
• [http://www.cse.gatech.edu Georgia Institute of Technology, USA, MS/PhD Programme Computational Science & Engineering]
• [https://web.archive.org/web/20080516161208/http://www.utc.edu/ComputationalEngineering/ The graduate program for the University of Tennessee at Chattanooga]
• [https://web.archive.org/web/20160304031252/http://www.iprj.uerj.br/index.php/modelagem-computacional/english/ Master and PhD Program in Computational Modeling at Rio de Janeiro State University]
• [http://www.openeering.com/made_with_scilab Computational Science and Engineering with Scilab]
• [http://www.cimne.com/ Internacional Center for Numerical Methods in Engineering (CIMNE)]

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Category:Computational science

Category:Computational fields of study