Engineering System Design: Modeling Techniques and Simulations Course

Lesson 1 • Design Objectives and Performance Metrics

Establishes how to translate stakeholder needs into measurable performance criteria. Grounds all subsequent modelling in quantifiable design goals.

Lesson 2 • Engineering Design Process Overview

Maps the iterative design cycle from problem definition to validation. Provides the procedural backbone applied in every subsequent section.

Lesson 3 • Introduction to Systems Thinking

Defines systems, boundaries, and emergent behaviour in engineering contexts. Connects holistic thinking to structured design decision-making throughout the course.

Lesson 1 • Governing Equations and Physical Laws

Derives governing equations from conservation laws and constitutive relations. Provides the mathematical foundation for all system models built later.

Lesson 2 • State-Space Representation

Introduces state variables and matrix formulations for multi-input, multi-output systems. Prepares students for simulation and control analysis in later sections.

Lesson 3 • Types of Engineering Models

Distinguishes empirical, analytical, and numerical model categories and their appropriate use cases. Sets the selection framework used throughout the course.

Lesson 4 • Model Simplification and Assumptions

Teaches principled reduction of model complexity while preserving essential behaviour. Develops judgement for balancing accuracy against computational cost.

Lesson 1 • Stress, Strain, and Material Behaviour

Connects applied loads to internal stress and deformation through material constitutive laws. Enables prediction of structural response under operating conditions.

Lesson 2 • Beam and Frame Analysis

Models bending, shear, and deflection in beams and frames under distributed and point loads. Directly applicable to structural component design.

Lesson 3 • Equilibrium and Free Body Diagrams

Establishes force and moment equilibrium as the basis for static analysis. Develops systematic diagramming skills essential for all structural models.

Lesson 4 • Failure Criteria and Safety Factors

Introduces yield, fracture, and fatigue failure criteria for structural design decisions. Connects model outputs to engineering safety and reliability requirements.

Lesson 1 • Nonlinear Dynamic Behaviour

Identifies and characterises nonlinear phenomena including limit cycles and bifurcations. Prepares students to recognise when linear models are insufficient.

Lesson 2 • Transfer Functions and Frequency Response

Derives transfer functions from differential equations and analyses frequency-domain behaviour. Connects time-domain models to frequency-domain design tools.

Lesson 3 • Electrical and Thermal System Analogs

Exploits analogies between mechanical, electrical, and thermal domains to unify modelling approaches. Enables cross-domain system integration.

Lesson 4 • Mechanical Dynamic Systems

Models mass-spring-damper systems and multi-degree-of-freedom structures. Establishes the canonical dynamic model used across engineering disciplines.

Lesson 1 • Finite Difference Methods

Discretises spatial and temporal derivatives to solve partial differential equations numerically. Extends simulation capability to distributed-parameter systems.

Lesson 2 • Simulation Verification and Validation

Establishes procedures to confirm that simulations are correctly implemented and accurately represent reality. Builds professional rigour into all simulation workflows.

Lesson 3 • Monte Carlo and Stochastic Simulation

Uses random sampling to propagate uncertainty through system models. Connects probabilistic inputs to distributions of system performance outputs.

Lesson 4 • Numerical Integration of ODEs

Covers explicit and implicit time-stepping schemes for solving ordinary differential equations. Directly enables simulation of dynamic models from previous sections.

Lesson 1 • Scripting and Automation in Simulation

Automates repetitive simulation tasks using scripting languages and APIs. Increases throughput for parametric studies and design iteration.

Lesson 2 • Simulation Results Visualisation

Applies visualisation techniques to extract engineering insight from large simulation datasets. Connects raw numerical output to actionable design decisions.

Lesson 3 • Multiphysics Simulation Environments

Couples mechanical, thermal, fluid, and electrical solvers within unified simulation platforms. Enables modelling of systems where multiple physical domains interact.

Lesson 4 • Data Management and Simulation Pipelines

Organises simulation inputs, outputs, and metadata for traceability and reproducibility. Establishes professional data governance practices for large-scale projects.

Lesson 1 • Robust and Reliability-Based Design

Incorporates uncertainty into optimisation to produce designs that perform well across variability. Connects probabilistic simulation to design decision-making.

Lesson 2 • Optimisation Problem Formulation

Translates engineering design goals into formal objective functions, constraints, and variable bounds. Establishes the mathematical structure required by optimisation algorithms.

Lesson 3 • Design Space Exploration

Maps system performance across design variable ranges using structured sampling methods. Identifies promising design regions before formal optimisation.

Lesson 4 • Gradient-Free and Evolutionary Methods

Uses population-based and heuristic algorithms for non-smooth or discrete design spaces. Complements gradient-based methods for complex engineering problems.

Lesson 1 • Digital Twin Concepts and Implementation

Connects live operational data to simulation models to create continuously updated digital replicas. Positions students at the frontier of model-based engineering practice.

Lesson 2 • Capstone System Design Project

Applies all course techniques to a comprehensive multidisciplinary design and simulation challenge. Demonstrates full professional competency in engineering system modelling.

Lesson 3 • Model Integration Across Subsystems

Assembles subsystem models into full system simulations while managing interface consistency. Addresses the technical challenges of large-scale model integration.

Lesson 4 • Model Credibility and Governance

Establishes organisational standards for model documentation, review, and approval. Ensures simulation outputs are trusted and defensible in engineering decisions.