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Modeling and Simulation Undergraduate Assignment help | Modeling and Simulation Undergraduate Homework Help

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Modeling and Simulation

Modeling and simulation refers to the physical and logical representation that generate the data and helps in determining decisions or make prediction about the system.It tests those conditions which are very difficult to transcribe with hardware prototypes in early phase of design process and iteration with Modeling and simulation reduces errors which helps in improving the quality of the system design. Computer simulation modeling helps in understanding and evaluating "what if case scenarios" which is mostly used by Designers, program managers, analysts, and engineers .

Important topics covered by Modelling and simulation:

  • continuum methods
  •  finite element analysis
  •  atomistic simulation
  •  molecular dynamics
  • quantum mechanics.
  • Atomistic and molecular simulation methods
  •  functional material properties
  • Schroedinger's equation

Some of the homework help topics include :

  • Modelling approaches, simulation approaches, molecular modelling, discrete element methods, computational fluid dynamics , process systems modelling scope
  • integrating multi-scale modelling, concepts in chemical, process engineering applications, Training in use of modelling, simulation approaches
  • process engineering industries, Game theory, Oscillation, Population Models, Simulation of traffic, Wiener processes, Chaotic behavior, Heat conduction process, Fluids flow
  • System analysis, classification of systems. System theory basics,Model classification: conceptual, abstract, and simulation models. Heterogeneous models
  • Methodology of model building,Simulation systems and languages, means for model and experiment description, Principles of simulation system design
  • Parallel process modelling. Using Petri nets and finite automata in simulation,Models o queuing systems. Discrete simulation models
  • Model time, simulation experiment control,Continuous systems modelling, numerical methods used for continuous simulation, System Dymola/Modelica,Combined simulation
  • role of simulation in digital systems design,Special model classes, models of heterogeneous systems,Cellular automata and simulation,Checking model validity, verification of models, Analysis of simulation results,Simulation results visualization 

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  • Live help for Modeling and Simulation Undergraduate Assignment online quiz & online tests

Help for complex topics like :

  • Model optimization,Generating, transformation, and testing of pseudorandom numbers, Stochastic models, Monte Carlo method, commonly used simulation systems
  • real time training and optimisation., Types of model: first principles versus stochastic, heuristic, empirical; Physical equations of systems: algebraic and differential
  • Constraint equations: equality and inequality;Time domain solutions: steady state and dynamic;Modelling techniques:  Formation of lumped parameter models
  • Classical assumptions; Accumulation equals input minus output, Analogies with electrical and mechanical systems, Significance of capacity for energy storage
  • Absolute and deviation variables, Linearisation, Scope for approximation, Laplace transformations, Conversion into transfer function models, Translation into block diagrams
  • System models:  Modelling of control loop elements;  Integration of process and control models, System block diagrams, Validation of models
  • Zero capacity systems, Hydrodynamic and electromechanical models, Models of reacting systems, Multiloop systems, State space modelling of multivariable systems, Models of distributed parameter systems, Discrete event modelling
  • Process models:  Dynamic models of a variety items of plant: stirred tanks, jacketed vessels, pressure systems, heat exchangers, packed columns
  • Models of a variety of operations: mixing, heating, blending, pumping, reaction, distillation; Simulation:  Use of continuous simulation languages
  • Simulation of linear & non-linear dynamic systems, Selection of numerical integration routines, Choice of step length & run time, Setting up initial and boundary conditions
  • Applying forcing functions and disturbances, discrete event simulation languages, Documentation & flow charts, Interpretation of error messages & debugging, Functional testing and validation


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