×
Reviews 4.9/5 Order Now

Python Program to Implement Steady State Equations Assignment Solution

June 28, 2024
Dr. Olivia Campbell
Dr. Olivia
🇺🇸 United States
Python
Dr. Olivia Campbell holds a Ph.D. in Computer Science from the University of Cambridge. With over 800 completed assignments, she specializes in developing complex Python applications, including fitness trackers and exercise planners. Dr. Campbell's expertise lies in algorithm design and data analysis, ensuring optimal performance and accuracy in every project she undertakes.
Key Topics
  • Instructions
    • Objective
  • Requirements and Specifications
Tip of the day
Design the database schema carefully before implementation. Proper normalization, clear table relationships, and correct use of primary and foreign keys help ensure data accuracy and make database assignments easier to query and manage.
News
In 2026, universities worldwide are expanding academic offerings with new tech programs, including a Bachelor’s in Artificial Intelligence launching at Pace University and an AI Engineering minor becoming available at BYU-Idaho for the spring term.

Instructions

Objective

Write a python assignment program to implement steady state equations.

Requirements and Specifications

program-to-implement-steady-state-equations-in-python

Source Code

import numpy as np import matplotlib.pyplot as plt M = 5.0 gam = 5./3. # ========================================================================== # ========================================================================== def ddx(x, F): Ns = F.size - 1 dFdx = np.zeros(Ns + 1, dtype='double') dFdx[0] = (F[1] - F[0]) / (x[1] - x[0]) for i in range(1, Ns): dFdx[i] = (F[i + 1] - F[i - 1]) / (x[i + 1] - x[i - 1]) dFdx[Ns] = (F[Ns] - F[Ns - 1]) / (x[Ns] - x[Ns - 1]) return dFdx # ========================================================================== # ========================================================================== if __name__ == '__main__': electron = np.loadtxt('./electron_quantites.txt') ion = np.loadtxt('./ion_quantites.txt') x = electron[:,0] # positions rho = ion[:,1] # this is rho/rho0. ke = electron[:,2] Te = electron[:,1] Ti = ion[:,2] ki = ion[:,3] eta = ion[:,4] u = ion[:,5] p = ion[:,6] E = ion[:,7] # First, calculate du/dx dudx = ddx(x, u) # Calculate dTi/dx dTidx = ddx(x, Ti) # Calculate dTe/dx dTedx = ddx(x, Te) # From equation (1b), calculate p0u0^2 + p0 eq1b = p + gam*M**2.*rho*u**2. - eta*dudx eq1b_right = gam*M**2 + p[0] # Equation (1c) eq1c = E*u + p*u - ki*dTidx - ke*dTedx - eta*u*dudx eq1c_right = E[0]/rho[0] + p[0] # Eq. 1a eq1a = rho*u #========================================================================== #========================================================================== # create plot plt.figure(figsize=(7,7)) # Plot dTidx plt.plot(x, rho, label = r'$\rho$') plt.plot(x, ke, label = '$k_{e}$') plt.plot(x, Te, label = '$T_{e}$') plt.plot(x, Ti, label = '$T_{i}$') plt.plot(x, ki, label = r'$k_{i}$') plt.plot(x, eta, label = r'$\eta$') plt.grid(True) plt.xlabel('Position') plt.legend() plt.show() plt.figure(figsize=(7,7)) plt.plot(x, u, label = 'u') plt.plot(x, p, label = 'p') plt.plot(x, E, label = 'E') plt.plot(x, dudx, label = r'$\frac{du}{dx}$') plt.plot(x, dTidx, label = r'$\frac{dT_{i}}{dx}$') plt.plot(x, dTedx, label = r'$\frac{dT_{e}}{dx}$') plt.xlabel('Position') plt.grid(True) plt.legend() plt.show() """ Now, plot each terms of each equation """ # Terms for Equation (1a) plt.figure() plt.plot(x, rho*u, label = r'$\rho u$') plt.xlabel('Position') plt.ylabel(r'$\rho u$') plt.set_title('Equation (1a)') plt.legend() plt.grid(True) plt.show() # Terms for question (1b). All terms in the same Figure # p + gam*M**2.*rho*u**2. - eta*dudx fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(7,7)) axes[0].plot(x, p, label = r'$p$') axes[0].plot(x, gam*(M**2)*rho*(u**2), label = r'$\gamma M^{2}\rho u^{2}$') axes[0].plot(x, -eta*dudx, label = r'$-\eta \frac{du}{dx}$') axes[0].plot(x, eq1b, 'r--', label = r'$p + \gamma M^{2}\rho u^{2} - \eta \frac{du}{dx}$') axes[0].grid(True) axes[0].set_title('Left-side terms of Equation (1b)') axes[0].legend() axes[1].plot(x, gam*(M**2)*np.ones(len(x)), label = r'$\gamma M^{2}$') axes[1].plot(x, p[0]*np.ones(len(x)), label = r'$p_{0}$') axes[1].plot(x, eq1b_right*np.ones(len(x)), 'r--', label = r'$\gamma M^{2} + p_{0}$') axes[1].grid(True) axes[1].set_title('Right-side terms of Equation (1b)') axes[1].legend() plt.show() # Terms for Equation (1c). # E*u + p*u - ki*dTidx - ke*dTedx - eta*u*dudx = E[0]/rho[0] + p[0] fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(7,7)) axes[0].plot(x, E*u, label = r'$Eu$') axes[0].plot(x, p*u, label = r'$pu$') axes[0].plot(x, -ki*dTidx, label = r'$-k_{i} \frac{dT_{i}}{dx}$') axes[0].plot(x, -ke*dTedx, label = r'$-k_{e} \frac{dT_{e}}{dx}$') axes[0].plot(x, -eta*u*dudx, label = r'$-\eta u \frac{du}{dx}$') axes[0].plot(x, eq1c, 'r--', label = r'$Eu + pu - k_{i} \frac{dT_{i}}{dx} - k_{e} \frac{dT_{e}}{dx} - \eta u \frac{du}{dx}$') axes[0].grid(True) axes[0].set_title('Left-side terms of Equation (1c)') axes[0].legend() axes[1].plot(x, E[0]/rho[0] *np.ones(len(x)), label = r'$\frac{E_{0}}{\rho_{0}}$') axes[1].plot(x, p[0]*np.ones(len(x)), label = r'$\rho_{0}$') axes[1].plot(x, eq1c_right*np.ones(len(x)), 'r--', label = r'$\frac{E_{0}}{\rho_{0}} + \rho_{0}$') axes[1].grid(True) axes[1].set_title('Right-side terms of Equation (1c)') axes[1].legend() plt.show() fig = plt.figure(figsize=(7, 5), dpi=120) plot = plt.plot(x, (eq1b-eq1b_right)/eq1b_right*100., 'royalblue', linestyle='--', label='Eq. (1b) percent error') plot = plt.plot(x, (eq1c-eq1c_right)/eq1c_right*100., 'tomato', linestyle='-', label='Eq. (1c) percent error') plot = plt.plot(x, (eq1a - 1.0)/1.0 *100, 'purple', linestyle='-', label = 'Eq. (1a) percent error') plt.ylabel(r'Percent error [%]') plt.xlabel(r'Position $\hat{x}$') #plt.xlim([25.0, 32.5]) plt.xlim([min(x), max(x)]) legend = plt.legend(loc='best', shadow=False, fontsize='small') #plt.savefig('equation_error.eps', format='eps', dpi=1000) plt.grid(True) plt.show() plt.close() #========================================================================== #==========================================================================

Similar Samples

Explore our diverse range of programming samples at ProgrammingHomeworkHelp.com. From Java to Python, C++, and beyond, our samples demonstrate proficiency across various languages and concepts. Each solution exemplifies our commitment to accuracy, clarity, and timely delivery. Whether you're a student or professional seeking guidance, our samples serve as a testament to our expertise in solving complex programming challenges. Visit us today to view our comprehensive samples and experience our top-notch programming assistance firsthand.