The numerical analysis method is mainly used in the area of mathematics and computer science that creates, analyzes, and implements algorithms for solving numerical problems of continuous mathematics. Such types of problems generally originate from real-world applications of algebra, geometry and calculus, and they also involve variables that vary continuously. These problems occur throughout the natural sciences, social sciences, engineering, medicine, and the field of business. Introduction of numerical analysis during the past half-century, the growth in power and availability of digital computers has led to the increasing use of realistic mathematical models in science and engineering. Here we will learn more about numerical method and analysis of numerical methods.
Numerical methods are techniques that are used to approximate mathematical procedures. We need approximations because we either cannot solve the procedure analytically or because the analytical method is intractable (an example is solving a set of a thousand simultaneous linear equations for a thousand unknowns).
Types of Numerical Methods
There are different numerical methods for solving different types of problems. Some of the basic numerical methods are-
Introduction to Numerical Methods for Solving Ordinary Differential Equations is Given below:
Numerical Methods for Solving Partial Differential Equations
The term “numerical computations” means to use computers for solving problems involving real numbers. In this process of problem-solving, we can distinguish several more or less distinct phases. The first phase is formulation. While formulating a mathematical model of a physical situation, scientists should take into account the fact that they expect to solve a problem on a computer. Therefore they will provide for specific objectives, proper input data, adequate checks, and for the type and amount of output.
Once a problem has been formulated, then the numerical methods, together with preliminary error analysis, must be devised for solving the problem. A numerical method that can be used to solve a problem is called an algorithm. An algorithm is a complete and unambiguous set of procedures that are used to find the solution to a mathematical problem. The selection or construction of appropriate algorithms is done with the help of numerical analysis. We have to decide on a specific algorithm or set of algorithms for solving the problem, numerical analysts should also consider all the sources of error that may affect the results. They should consider how much accuracy is required. To estimate the magnitude of the round-off and discretization errors, and determine an appropriate step size or the number of iterations required.
The programmer should transform the suggested algorithm into a set of unambiguous that is followed by step-by-step instructions to the computer. The flow chart is the first step in this procedure. A flow chart is simply a set of procedures, that are usually written in logical block form, which the computer will follow. The complexity of the flow will depend upon the complexity of the problem and the amount of detail included. However, it should be possible for someone else other than the programmer to follow the flow of information from the chart. The flow chart is an effective aid to the programmer, they must translate its major functions into a program. And, at the same time, it is an effective means of communication to others who wish to understand what the program does.
Numerical and Statistical Methods
Numerical methods, as said above are techniques to approximate mathematical procedures. On the other hand, statistics is the study and manipulation of data, including ways to gather, review, analyze, and draw conclusions from the given data. Thus we can say, statistical methods are mathematical formulas, models, and techniques that are used in the statistical analysis of raw research data. The application of statistical methods extracts information from research data and provides different methods to assess the robustness of research outputs. Some common statistical tools and procedures are given below :
Introduction to Finite Element Method
The various laws of physics related to space and time-dependent problems are usually expressed in terms of partial differential equations (PDEs). If we have the vast majority of geometries and problems, these PDEs cannot be solved using analytical methods. Instead of that, we have created an approximation of the equations, typically based upon different types of discretizations. These discretization methods approximate the PDEs with numerical model equations, which can be solved using numerical methods. Thus, the solution to the numerical model equations is, in turn, an approximation of the real solution to the PDEs. The finite element method is used to compute such approximations.
The finite element method is a numerical technique that is used for solving problems that are described by partial differential equations or can be formulated as functional minimization. A domain of interest is represented by the assembly of finite elements. Approximating functions in finite elements are determined in terms of nodal values of a physical field. A continuous physical problem is transformed into a discretized finite element problem with the help of unknown nodal values. For a linear problem, a system of linear algebraic equations must be solved. We can recover values inside finite elements using the nodal values.
Two Features of the Fem are Mentioned below:
Typical Classes of Engineering Problems That Can be Solved Using Fem are:
Finite Element Method MATLAB
Finite element analysis is a computational method for analyzing the behaviour of physical products under loads and boundary conditions. A typical FEA workflow in MATLAB includes
Importing or creating geometry.
Defining physics of the problem with the help of load, boundary and initial conditions.
Solving and visualizing results.
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The design of experiments or optimization techniques can be used along with FEA to perform trade-off studies or to design an optimal product for specific applications.
MATLAB is Very Useful Software and is Very Easy to Apply Finite Element Analysis Using MATLAB. It Helps Us in Applying Fem in Several Ways:
Partial differential equations (PDEs) can be solved using the inbuilt Partial Differential Equation Toolbox.
In MATLAB, with the help of Statistics and Machine Learning Toolbox, we can apply the design of experiments and other statistics and machine learning techniques with finite element analysis.
Also, the optimization techniques can be applied to FEM simulations to come up with an optimum design with Optimization Toolbox.
Parallel Computing Toolbox speeds up the analysis by distributing multiple Finite element analysis simulations to run in parallel.