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New Undergrad Course, ABE 301Engineering Faculty Document No. 23-04 March 1, 2005 TO: Engineering Faculty FROM: The Faculty of Agricultural and Biological Engineering RE: New Undergraduate Level Course The faculty of the Department of Agricultural and Biological Engineering has approved the following new undergraduate level course. This action is now submitted to the Engineering Faculty with a recommendation for approval. ABE 301 - Modeling
and Computational Tools in Biological Engineering Sem. 2, Class 3, cr. 3 Prerequisites: MA 265 and MA 266 or MA 262 and ABE 202 Description: Reasons: While undergraduate engineering students take a wide range of mathematics courses and learn to apply these to previously developed models, they have relatively little direct education on how to develop quantitative models. The purpose of this course is to provide students with knowledge and skills needed to develop quantitative models from physical/industrial phenomena and the use of appropriate numerical methods for obtaining solutions to such models, as needed. Bernard A. Engel Professor and Acting Head Agricultural and Biological Engineering Department ABE 301 - Modeling and Computational Tools in Biological Engineering Sem. 2, Class 3, cr. 3 Prerequisites: MA 265 and MA 266 or MA 262 and ABE 202 Description: Suggested reference and/or textbooks: Applied Numerical Methods with Matlab for Engineers and Scientists by Steven C. Chapra (2005) McGraw Hill. Course Learning Objectives: In this course the student will learn numerical modeling skills for interpolation, cubic splines, finding roots, statistical regression modeling, and numerical solution of differential equations. Emphasis is placed on the use of computational tools for modeling and solution of problems. Engineering problem solving skills will be developed via MathCad/Matlab software programming. At the end of the course the student will be able to: 1.
understand principles of mathematics and computation
used to develop numerical models of food and biological phenomena, 2.
understand the modeling limitations related to
computational accuracy/error and statistical precision, 3.
gain an understanding of principles and techniques of
data modeling, numerical approximation, maxima/minima determination, solutions
of linear algebraic systems, IV/BDV ODE/PDE systems, and non-linear dynamic
systems, 4.
develop skills to create numerical models from
natural/biological systems using fundamental physical and chemical phenomena,
such as reaction kinetics, transport phenomena, and thermodynamics, and 5. develop skills for creating computational tools to quantify numerical models. Topics Week 1-2 What are models and why are they useful? Empirical vs. Theoretical Algebraic vs. Calculus/Differential Linear vs. Nonlinear Simulation vs. Optimization Numerical Modeling Computational, graphical Numerical Accuracy/Precision/Error Principles for developing models from physical phenomena Numerical Computational Tools (MathCad/MatLab) Week 3-4 Introduction to Concepts of Biological
Engineering Thermodynamics Material balances/Energy balances Chemical Potential/Vapor-Liquid Equilibrium Reaction Kinetics Batch, Mixed Flow, Plug Flow reactors Separations Chromatography Vapor-Liquid Equilibria Economics Week 5-6 Data Modeling Water Vapor Pressure
Modeling Approximation methods using tabular data Interpolation Differentiation Integration Steam tables Empirical modeling of data Statistical regression Linear, polynomial Antoine’s equation, production economics, pharmaceutical quality control Non-statistical Cubic splines Chromatographic separations, Computational Tools Development Week 7 Optimization Gibbs Energy Minimization/Equilibrium Finding Roots Bisection/false position Derivative slope methods Computational Tools Development Week 8 Linear systems Biomass Separation
Processes Distillation/Vapor-Liquid Equilibria; Binary mixture separations Substitution, Gaussian elimination, matrix algebra Linear Programming/Optimization Computational Tools Development Week 9-10 Unsteady
State Biological Processes Enzyme/Biochemical
Reactions Fermentation Kinetics Conductive/Convective Heat Transfer in Cooking Foods Ordinary Differential Equations Initial value/boundary conditions Euler Runge-Kutta Computational Tools Development Week 11-12 Unsteady
State Biological Processes Diffusion of Moisture
in Biological Materials Finite Element Difference equations Multidimensional elements Computational Tools Development Week 13-15 Dynamic
Biological Processes Microbial Population
Dynamics, Metabolic Cycles, Imaging Introduction to Non-Linear Dynamic systems Fractal graphics Prey-Predator models Attractors Bifurcation/Stability Computational Tools Development Prerequisite
knowledge: Calculus/differential equations Mass/energy balances Basic descriptive statistics Concurrent with transport phenomena Grading Policy % Notes: Examples from biological and food process systems Teach basic concepts in unsteady state transport phenomena, reaction kinetics |
