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# BSEN30530

#### Numerical Methods for Agricult (BSEN30530)

Subject:
Biosystems Engineering
College:
Engineering & Architecture
School:
Biosystems & Food Engineering
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Assoc Professor Paddy Grace
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?

Curricular information is subject to change.

To solve many agricultural technical problems they need to be modelled mathematically. These mathematical models can then be solved sometimes analytically but more often numerical solutions are required. In this module students will learn how to apply numerical methods to solving a range of such problems.
Equation roots: bisection, Newton-Raphson. Optimisation: simplex method, curve fitting. Simultaneous equations: Gaussian elimination, LU decomposition, Gauss-Seidel. Numerical differentiation and Integration: Simpson’s rule. Differential equations: Euler, central difference, Runge-Kutta, finite differences, Crank-Nicolson, stability.
Simulation techniques will also be introduced.
This module will take place over a 7-week period and the final examination will be during the March break.

###### Learning Outcomes:

On completion of this module students should have acquired:
• knowledge and understanding of a range of numerical techniques;
• knowledge and understanding of the defects of numerical techniques especially stability and accuracy issues;
• skills and competency in numerically solving a range of problems;
• some insights into simulation techniques.

###### Student Effort Hours:
Student Effort Type Hours
Lectures

21

Computer Aided Lab

12

Specified Learning Activities

40

Autonomous Student Learning

42

Total

115

###### Approaches to Teaching and Learning:
To get the maximum out of this module a student should attend all lectures and take appropriate notes. Some of the notes are on Brightspace but these is not sufficient to pass the examination.

Students are expected to attend the practicals and do the assignments, which are assessed.
###### Requirements, Exclusions and Recommendations
Learning Recommendations:

MATH10230 and MATH10240 or equivalent. Also, knowledge of a programming language such as Pyton or Mathlab is necessary.

###### Module Requisites and Incompatibles
Incompatibles:
EEEN30150 - Modelling and Simulation

###### Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): End of 7-week trimester examination in March break Week 7 Graded No
70
No
Practical Skills Assessment: A number of practical assignments are carried out throughout the trimester. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6 Graded No
30
Yes

###### Carry forward of passed components
Yes

Resit In Terminal Exam
Autumn Yes - 2 Hour