Finding Buried Targets Using Acoustic Excitation Zackary R. Kenz - PowerPoint PPT Presentation

Math Modeling Acoustic Model Simulations Conclusion Finding Buried Targets Using Acoustic Excitation Zackary R. Kenz Advisor: Dr. H.T. Banks In collaboration with Dr. Shuhua Hu, Dr. Grace Kepler, Clay Thompson – NCSU, L3 Communications team led by Dr. Jerrold Levine, and Dr. Richard Albanese Center for Research in Scientific Computation Department of Mathematics North Carolina State University October 7, 2010 Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Math Modeling and Differential Equations 1 1D Model Formulation 2 Simulation Setup and Results 3 Conclusion 4 Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Goals for Math Modeling Portion Introduction to math modeling Introduce differential equations Examine how changing parts of a differential equation can change resulting behavior Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Goals for Target Detection Portion Acoustic Dynamics Given an impact to the soil, what sort of wave propagation dynamics are expected? How do changes in soil properties affect dynamics? Electromagnetic Signal Dynamics Given an arbitrarily moving target in the soil, what will a reflected radar signal look like? Device Development Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Math Modeling and Differential Equations 1 1D Model Formulation 2 Simulation Setup and Results 3 Conclusion 4 Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Math Modeling Overview Modeling is developing equations to explain some phenomenon, and then using the equations to make predictions or answer questions about the phenomenon. Validating the equations with real life data is of particular interest. Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Modeling Cycle The Iterative Modeling Process (iii) Abstraction or Mathematization resulting in a mathematical model (ii) Formalization of properties, (iv) relationships and mechanisms Formalization of which result in a biological or Uncertainty/Variablity in model and physical model data resulting in a statistical model (i) Empirical Observations (v) Model Analysis (experiments and data collection) (vii) Changes in understanding of (vi) mechanisms, etc., in the real Interpretation and Comparison system. (with the real system) Formation Stage: (i),(ii),(iii),(iv) Solution Stage: (v) Interpretation Stage: (vi), (vii) Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Differential Equations and Modeling Modeling often uses differential equations to describe relationships between variables There are "ordinary" and "partial" differential equations Question: What are differential equations? Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion What is an ODE? A differential equation is a mathematical equation for an unknown function of one or several variables that relates the values of the function itself and its derivatives of various orders. An ordinary differential equation means the function of interest depends only on one independent variable. Instead of solving for numbers, like solving for x in x 2 − 1 = 0, we are solving for a function Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion ODE Example 1 dy ( t ) = 2 dt The left hand side is the rate of change of the solution y ( t ) . The equation says that we want the rate of change of the unknown function y ( t ) to be a constant. What simple function has a constant rate of change (slope)? Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Example 1 Solution dy ( t ) = 2 dt If we integrate both sides with respect to t , the solution is y ( t ) = 2 t + c c is a "constant of integration" This could describe the motion of someone walking down the sidewalk at a constant rate. Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Adding an Initial Condition y ( t ) = 2 t + c Solution from previous slide: In a particular problem, we might want the person to start walking at position 5. Mathematically, we write y ( 0 ) = 5. Applying that condition to the problem: y ( 0 ) = 2 · 0 + c = 5 . Solving gives c = 5. The full solution would then be y ( t ) = 2 t + 5 Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Components of an ODE Problem A full ODE problem is both the main equation (which includes the derivatives) and also some conditions like our y ( 0 ) = 5. The number of conditions depends on the highest number of derivatives you have. Now we’ll look at a slightly more complicated example. Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion ODE Example 2 dy ( t ) = 2 y ( t ) dt This equation says that the rate of change of the solution is proportional to the value of the function at every point t . It’s a little more complicated to get the solution for this case, so we won’t solve it here but just state the answer: y ( t ) = e 2 t + c . Changing the right hand side of the ODE from a constant to the unknown function changed the solution behavior. Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion ODE Example 3 dz ( t ) = − 2 z ( t ) dt By changing the constant to − 2, the solution is now z ( t ) = e − 2 t + d . Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Example 2 vs Example 3 We’ll set the constants of integration equal to 0 (arbitrary). We can compare the two solutions y ( t ) = e 2 t and z ( t ) = e − 2 t in the figure below. y(t) 6 z(t) Function Value 4 2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 t Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion A Little More on ODEs These equations can get more complicated. For example, Higher derivatives can be in the equation Parameters can be in the model. The spring system is a common example: md 2 y ( t ) + c dy ( t ) + ky ( t ) = 0 dt 2 dt m : mass c : damping k : stiffness Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Example Solution for Spring System Damped Spring System 2 1.5 Postion x(t) 1 0.5 0 −0.5 0 2 4 6 8 10 Time t Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Brief PDE Discussion What if we want the solution to depend on multiple independent variables, like the three spatial dimensions? A partial differential equation (PDE) seeks a solution with more than one independent variable. For example, we might want to track the temperature of a room over time. We would then be trying to find a function like T ( x , y , z , t ) . Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Brief PDE Discussion Derivatives are now "partial" derivatives (denoted with ∂ ), meaning with respect to one variable holding the others constant. For example, if we wanted the rate of change of temperature with respect to time, we’d denote that quantity as ∂ T ( x , y , z , t ) . ∂ t Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion The Wave Equation An equation that can be used to describe simple wave motion in one spatial dimension is ∂ 2 u ( z , t ) = c 2 ∂ 2 u ( z , t ) . ∂ t 2 ∂ z 2 Upon solving analytically, the constant c represents the speed of the wave. This particular form of the wave equation can be solved by hand, but more complicated forms are solved numerically. There is a shorthand notation for partial derivatives that turns the wave equation above into u tt = c 2 u zz . Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Moving Forward With this brief introduction to modeling and differential equations, we can start discussing my research project of detecting buried targets. The project is an example of the modeling cycle. Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Math Modeling and Differential Equations 1 1D Model Formulation 2 Simulation Setup and Results 3 Conclusion 4 Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Definitions Elastic material: returns to its original shape after applying stress forces (rubber band) Viscous material: Resists stresses (tar) Viscoelastic material: Has both properties (rubber) Z.R. Kenz Acoustic/EM Target Detection

Math Modeling Acoustic Model Simulations Conclusion Problem Situation Open field where we want to detect buried objects Thump in one area, study wave propagation outward from thumper Likely need a 2D or 3D model to capture full dynamics As a first approximation, we’ll use a 1D model see which features of wave propagation we can capture Z.R. Kenz Acoustic/EM Target Detection