AS4202 Computational Modeling and Data Analysis in Aerospace Engineering Syllabus:
AS4202 Computational Modeling and Data Analysis in Aerospace Engineering Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
This course will make the students
1. To get familiarize with the procedure to obtain numerical solution to fluid dynamic problems.
2. To gain knowledge on the important aspects of grid generation for practical problems.
3. To get exposure on time dependant and panel methods.
4. To understand the use of computation to understand real world phenomena.
5. To learn the data analysis techniques and its applications to space science.
UNIT I NUMERICAL SOLUTIONS OF SOME FLUID DYNAMICAL PROBLEMS
Basic fluid dynamics equations, Equations in general orthogonal coordinate system, Body fitted coordinate systems, mathematical properties of fluid dynamic equations and classification of partial differential equations – Finding solution of a simple gas dynamic problem, Local similar solutions of boundary layer equations, Numerical integration and shooting technique. Numerical solution for CD nozzle isentropic flows and local similar solutions of boundary layer equations Panel methods.
UNIT II GRID GENERATION
Need for grid generation – Various grid generation techniques – Algebraic, conformal and numerical grid generation – importance of grid control functions – boundary point control – orthogonality of grid lines at boundaries. Elliptic grid generation using Laplace’s equations for geometries like aerofoil and CD nozzle. Unstructured grids, Cartesian grids, hybrid grids, grid around typical 2D and 3D geometries – Overlapping grids – Grids around multi bodies.
UNIT III TIME DEPENDENT METHODS
Stability of solution, Explicit methods, Time split methods, Approximate factorization scheme, Unsteady transonic flow around airfoils. Some time dependent solutions of gas dynamic problems. Numerical solution of unsteady 2-D heat conduction problems using SLOR methods.
UNIT IV INTRODUCTION TO DATA ANALYSIS
An introduction to probability theory-the modeling and analysis of probabilistic systems and elements of statistical inference – Probabilistic models – conditional probability. Discrete and continuous random variables
UNIT V DATA ANALYSIS IN AEROSPACE APLICATIONS
Expectation and conditional expectation, and random variables – Limit Theorems – Bayesian estimation and hypothesis testing – Elements of classical statistical inference – Bernoulli and Poisson processes – Markov chains.
TOTAL: 60 PERIODS
COURSE OUTCOMES:
At the end of this course, students will be able
CO1: To arrive at the numerical solutions to boundary layer equations.
CO2: To perform numerical grid generation and have knowledge about the mapping techniques.
CO3: To familiarise himself/herself with high performance computing for CFD applications.
CO4: To implement the explicit time dependent methods and their factorization schemes.
CO5: To do the stability analysis and linearization of the implicit methods.
REFERENCES:
1. Bose. TK, “Numerical Fluid Dynamics”,Narosa Publishing House, 2001.
2. Chung. TJ, “Computational Fluid Dynamics”, Cambridge University Press, 2010.
3. Hirsch,AA, “Introduction to Computational Fluid Dynamics”, McGraw-Hill, 1989.
4. John D. Anderson, “Computational Fluid Dynamics”, McGraw Hill Education, 2017.
5. Anil Maheshwari, Data Analytics, McGraw Hill Education; First edition, 2017
6. Erwin Kreysig, Advanced Engineering Mathematics Wiley 2015.