Predicting Landing Solutions for a Rocket in a Two- Body System Spring 2014 · Honors Professor Thorp · C++

Slides:



Advertisements
Similar presentations
Slides for Parallel Programming Techniques & Applications Using Networked Workstations & Parallel Computers 2nd ed., by B. Wilkinson & M
Advertisements

Modeling Basics: 4. Numerical ODE Solving In Excel 5. Solving ODEs in Mathematica By Peter Woolf University of Michigan Michigan Chemical Process Dynamics.
Experiments and Variables
Quality Management, 6 th ed. Goetsch and Davis © 2010 Pearson Higher Education, Upper Saddle River, NJ All Rights Reserved. 1 Quality Management.
Ordinary Differential Equations
Data mining and statistical learning - lecture 6
Read Chapter 17 of the textbook
Section 4.2 Fitting Curves and Surfaces by Least Squares.
Nazgol Haghighat Supervisor: Prof. Dr. Ir. Daniel J. Rixen
Dr. Jie Zou PHY Chapter 9 Ordinary Differential Equations: Initial-Value Problems Lecture (I) 1 1 Besides the main textbook, also see Ref.: “Applied.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Ordinary Differential Equations Equations which are.
AE/ME 339 Computational Fluid Dynamics (CFD) K. M. Isaac Professor of Aerospace Engineering.
ECIV 301 Programming & Graphics Numerical Methods for Engineers.
Mech300 Numerical Methods, Hong Kong University of Science and Technology. 1 Part Seven Ordinary Differential Equations.
Curve-Fitting Regression
11 September 2007 KKKQ 3013 PENGIRAAN BERANGKA Week 10 – Ordinary Differential Equations 11 September am – 9.00 am.
Modeling The quadratic integrate and fire follows from a reduced form (1) where F(V) is a voltage dependant function which aims to capture the voltage.
Initial-Value Problems
ECIV 301 Programming & Graphics Numerical Methods for Engineers REVIEW III.
The Islamic University of Gaza Faculty of Engineering Numerical Analysis ECIV 3306 Introduction.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 1-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 1 Introduction.
NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS
IE-331: Industrial Engineering Statistics II Spring 2000 WEEK 1 Dr. Srinivas R. Chakravarthy Professor of Operations Research and Statistics Kettering.
CISE301_Topic8L31 SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2,
Differential Equations and Boundary Value Problems
Numerical Solution of Ordinary Differential Equation
PART 7 Ordinary Differential Equations ODEs
Ordinary Differential Equations (ODEs) 1Daniel Baur / Numerical Methods for Chemical Engineers / Explicit ODE Solvers Daniel Baur ETH Zurich, Institut.
Professor Walter W. Olson Department of Mechanical, Industrial and Manufacturing Engineering University of Toledo Solving ODE.
Least-Squares Regression
Lecture 35 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
Numerical Methods Adam Keaney Aisling Butler Jody Monahan
Erin Catto Blizzard Entertainment Numerical Integration.
Chapter 17 Boundary Value Problems. Standard Form of Two-Point Boundary Value Problem In total, there are n 1 +n 2 =N boundary conditions.
Analytical vs. Numerical Minimization Each experimental data point, l, has an error, ε l, associated with it ‣ Difference between the experimentally measured.
Improved Gene Expression Programming to Solve the Inverse Problem for Ordinary Differential Equations Kangshun Li Professor, Ph.D Professor, Ph.D College.
Computational Physics Introduction 3/30/11. Goals  Calculate solutions to physics problems  All physics problems can be formulated mathematically. 
MECN 3500 Inter - Bayamon Lecture 9 Numerical Methods for Engineering MECN 3500 Professor: Dr. Omar E. Meza Castillo
Curve-Fitting Regression
Professor Walter W. Olson Department of Mechanical, Industrial and Manufacturing Engineering University of Toledo Performance & Stability Analysis.
Regression Regression relationship = trend + scatter
Solution of a Partial Differential Equations using the Method of Lines
Modeling motion subject to drag forces PHYS 361 Spring, 2011.
Numerical Methods for Engineering MECN 3500
Numerical Solutions of ODE
The Islamic University of Gaza Faculty of Engineering Civil Engineering Department Numerical Analysis ECIV 3306 Introduction Course Outline.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 7 - Chapter 25.
Numerical Analysis – Differential Equation
MTH 204 NUMERICAL ANALYSIS Spring Term MTH 204 NUMERICAL ANALYSIS Spring Term DEPARTMENT of INFORMATION TECHNOLOGIES Assoc. Prof. Dr.
Dr. Mujahed AlDhaifallah ( Term 342)
Lecture 39 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
SCIENTIFIC DISCOVERY EXPERIMENT THEORY SCIENTIFIC COMPUTING 1.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 6 - Chapters 22 and 23.
1 NUMERICAL METHOD. 2 Introduction Definition : The study of approximation techniques for solving mathematical problems, taking into account the extent.
Higher Order Runge-Kutta Methods for Fluid Mechanics Problems Abhishek Mishra Graduate Student, Aerospace Engineering Course Presentation MATH 6646.
© 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
CHAPTER 3 NUMERICAL METHODS.
ZCE 111 Assignment 11.
A Computational Analysis of the Lotka-Volterra Equations Delon Roberts, Dr. Monika Neda Department of Mathematical Sciences, UNLV INTRODUCTION The Lotka-Volterra.
Chapter 23.
Make interpolations and extrapolations related to how long it will take for the candle to burn to ____ cm tall or to completely burn out. line of.
Chapter 27.
Graphing Techniques.
Numerical Solutions of Ordinary Differential Equations
Solve the differential equation using the method of undetermined coefficients. y " + 4y = e 3x 1. {image}
Solve the differential equation using the method of undetermined coefficients. y " + 9y = e 2x {image}
Numerical Computation and Optimization
Reading Between the Lines!
Recapitulation of Lecture 12
Presentation transcript:

Predicting Landing Solutions for a Rocket in a Two- Body System Spring 2014 · Honors Professor Thorp · C++

Overview & The Model Conduct a parameter study to: identify the relationships between parameters in a mathematical model. Investigate the reliability of curve fitting numerical landing solutions from the model (e.g. y=f(x)). Edwards, C. Henry, and David E. Penney. Differential Equations & Boundary Value Problems Computing and Modeling. 4th ed. Upper Saddle River: Pearson Custom, Print.

Numerical Methods & C++ Advantages of numerical methods: Provide an alternative for solving non-linear ODEs. Easily implemented as a computer program. Commonly found in industry (aerospace & finance). Our numerical methods: The Euler method 4 th Order Runge-Kutta. Using the C++ programming language we: Added additional code to handle systems of equations. Reduced trial times and human input error.

Case Study & Parameter Study Case Study Used a 2 nd order non-linear ODE. Validated the RK4 integrator using the expected case study values. Parameter Study Collected 21 data points. Plotted the points and applied a quadratic fit to the data set using Microsoft Excel. Interpolated 6 points and extrapolated 8 points. Drew conclusions based on metrics such as percent error. Extrapolated using different curve fits.

Results & Future Investigations Identified the conditions needed to obtain reliable curve fits. This technique can be used for non-aerospace models. Our project received positive feedback from academia and industry. Future Investigations Implement more complex numerical methods. Develop a more complex aerospace model by introducing the following parameters. Drag using standard atmosphere data at multiple altitudes. Mass reduction due to fuel burn. Drag and mass reduction from rocket staging.