Breaking Ice with Air cushion Vehicle - Finding minimum pressure with Newton-Raphson method using Python

*********** Comments by Grader ************GOOD JOB!!SUGGESTION : The other thing that you can do is to attach the code and images in projects.skill-lync.com  and paste the link over here.If you have any doubts please ask us on WHATSAPP or…

*********** Comments by Grader ************GOOD JOB!!SUGGESTION : The other thing that you can do is to attach the code and images in projects.skill-lync.com  and paste the link over here.If you have any doubts please ask us on WHATSAPP or…

In this challenge, we are going to curve fit the given data, which means constructing a mathematical curve that best fits the given series of points. In other words, we are going to derive that function that gives the best representation of the relationship between two values. The code has been shown below - import numpy…

Introduction : Newton-Raphson method is used to find the roots of an equation through iterative approximations starting from a given point. Consider a function f(x) with an intitail point as x0. Then, by employing this method we find the next closest approximation by using the formula - The process is then repeated iteratively…

In this program, we are going to code a program to find the drag force acting on a person riding a bicycle. The drag force depends on the following parameters namely - Drag coefficient, density of fluid, frontal area, velocity.   Since we have to plot two different figures, we are assigning a constant drag coefficient…

In this program we are simulating the motion of a 2R robotic arm inside a workspace using Python. The images are saved under different file names using a counter and then they are stiched together to form a gif animation of the 2R robotic arm in different positions. The code is given below -   import math import matplotlib.pyplot…

In this code, we are going to create a PV diagram for a  standard cycle given the pressure and temperature at different points of the cycle. The code is divided in to two halves. In the first part, we are going to define the engine kinematics that  calculates the volume based on various parameters such as bore,…

A tool used for solving constrained and uncocntrained problems by natural selection is called as genetic algorithm. Each generation is created from the preceding generation by using the following rules -  1. Selection - selection of parents for contribution of the next generation 2. Crossover - combination of two…

In this challenge, we are constructing a curve using the given set of data. The curve constructed needs to be as close in value as to the given set as possible.   Here, we construct the curve using a linear and cubic polynomial and then check the results by plotting it. The code is given below. clear all close all…

% Row vector A = [1 2 4] %Column vector B = [1 ; 2 ; 4]

clear all close all clc b =0.05; g = 9.81; l = 1; m = 0.1; % initial condition theta_0 = [0;3]; % time points t_span = linspace(0,20,100); % solve ODE [t , results] = ode45(@(t,theta) ode_func(t,theta,b,g,l,m), t_span, theta_0); plot(t,results(:,1)) hold on plot(t,results(:,2)) ylabel(\'Plot\') xlabel(\'time\') ct = 1;…

  clear all close all clc %Inputs gama = 1.4; t3 = 2300; %State Variables p1 = 101325; t1 = 500; %Engine geometric parameters bore = 0.1; stroke = 0.1; con_rod = 0.15; cr = 12; %Calculating the swep volume and clearance volume v_swept = (pi/4)*bore^2*stroke; v_clearance = v_swept/(cr-1); v1 = v_swept + v_clearance…

clear all close all clc %Inputs l1 = 1; l2 = 0.5; theta1 = linspace(0,90,10); theta2 = linspace(0,90,10); ct = 1 for i = 1:length(theta1) THETA1 = theta1(i); for j = 1:length(theta2) THETA2 = theta2(j); x0 = 0; y0 = 0; x1 = l1*cosd(THETA1); y1 = l1*sind(THETA1); x2 = x1 + l2*cosd(THETA2); y2 = y1 + l2*sind(THETA2); %Plotting…