Parsing NASA thermodynamic data on MATLAB

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Equation for Ice Breaking with an Air Cushion Vehicle:-  `p^3*(1-beta^3)+(0.4*h*beta^2-(sigma*h^2)/r^2)*p^2+(sigma^2*h^4)/(3*r^4)*p-((sigma*h^2)/(3*r^2))^3=0` p = pressure h = thickness of the ice `sigma= " tensile strength of the ice"` r = size of the air cushion `beta="Function of width of the ice…

This code takes takes the data from a user specified CONVERGE format file. It also checks the file for the correct format i.e. compatibility. If the file is not a CONVERGE output data format then the code will throw an error statement "This is not a valid CONVERGE output file.". Also, if the file format is correct but,…

b = 0.5  Damping factor (kg/s) m = 1  Mass (kg) g = 9.81  Acceleration due to gravity (m/s^2) L = 1  Length of Pendulum (m) Angular displacement (rad) Angular velocity (rad/sec) simple_pendulum_run % This script simulates simple pendulum motion % Clearing close all; clear; clc; % Inputs b = 0.5; % Damping…

Mathematical model:- `ddot theta + (b/m)*dot theta+(g/L)*sin(theta)=0` `where,` `ddot theta=`Angular Acceleration` `dot theta=`Angular Velocity` `theta`=Angular Displacement` `b=`Damping Factor` `g=`Acceleration due to gravity = 9.81 (m/s^2)` `L=`Length of pendulum` `m=`Mass of bob`   Converting this second order…

This project is on curve fitting given a data set containing range values for two variables, temperature and specific heat in this case. This code uses curve_fit function from scipy to fit linear,qudratic,cubic and multiple functions to the data set. Finally, it also calculates mean squared error for each fit. """ This…

This code fits the specific heat vs temperature data to linear and cubic curve and plots original data, linear fit and cubic fit. It also displays mean squared error for both linear fit and cubic fit. % Curve fitting Specific heat vs temperature %Clearing close all; clear; clc; data = load('data'); temp = data(:, 1); cp…

Otto cycle processes:- Process 1-2 (Isentropic compression) Process 2-3 (Contant volume heat addition) Process 3-4 (Isentropic expansion) Process 4-1 (Contant volume heat rejection)   'main_script' has all the inputs and calculates state variables i.e pressure, volume and temeprature. Then, it calls 'v_at_crank_angle.m'…

Otto cycle processes:- Process 1-2 (Isentropic compression) Process 2-3 (Contant volume heat addition) Process 3-4 (Isentropic expansion) Process 4-1 (Contant volume heat rejection)   Themal efficiency, `eta = 1 - (1/(r^(gamma-1)))`    This code will initialize the inputs. It will also initilize the…

`F_d`= (C_d * A * rho * v^2)/2` where, `C_d`= Drag coefficient `A` = Area `rho` = Density of fluid `v` = velocity  `F_d`= Drag force     Part 1 :- Ploting Drag force vs velocity (drag_force).  This script plots Drag force vs velocity.   % Ploting Drag force vs velocity % CLearing…

`F_d= (C_d * A * rho * v^2)/2` where, `C_d` = Drag coefficient `A` = Area `rho`= = Density of fluid `v`= velocity    `F_d`  Drag force   Part1 :- Ploting Drag force vs velocity.   This script plots Drag force vs velocity.   """ Code to calculate drag force experienced by…

This MATLAB code simulates a 2 link robot Kinematics for each link moving from 0 degree to 90 degrees.   two_R_Robotic_Arm_Simulation.m %% Clearing close all; clear; clc; %% Inputs l1 = 1; % Length of Link 1 (m) l2 = 0.5; % Length of Link 2 (m) theta = 0:10:90; % Range of values of angle for Link 1 and 2 w.r.t X-direction…

This code simulates a 2 link robot with each link travelling from 0 degree to 90 degrees. # 2R Robotic Arm Simulator import matplotlib.pyplot as plt import numpy as np import math import os # Inputs start_angle = 0 end_angle = math.pi/2 x0 = 0 # x-co-ordinate of Link 1, base connector y0 = 0 # y-co-ordinate of…

This code asks for substance molecular formula as input. It outputs the molecular weight. It takes the co-efficient data of the corresponding substance from NASA thermodynamic data file named "therm.dat". Then, it calculates the specific enthalpy, specific entropy, and specific heat for range of temperature…

1. Genetic algorithm is an optimization method that mimics natural process of evolution. A population of input parameters (chromosomes) is generated randomly. Then, the fitness of each parameter is calculated by applying the parameters to the fitness function to be optimized. Next step is the selection process. In this…