MAK 4050 OPTIMIZATION of THERMAL SYSTEMS

OPTIMIZATION of THERMAL SYSTEMS

MAK 4050 E

2026-2027 Fall

CRN: 15088

 

Lecturer                         :  Seyhan ONBAŞIOĞLU

Room                              :  537

e-mail                              : onbasiogl1@itu.edu.tr

Office Hours                :  Any time by appoinment

Course Hours             :  Monday 09.30-12.30

Course Description                :  Principles of engineering design and system boundaries. Economic and cost analysis: Net Present Value (NPV), Levelized Cost of Energy (LCOE), thermoeconomic evaluation. Empirical modeling and curve fitting: Regression analysis of thermal equipment data. Solution of linear and non-linear equation sets: Newton-Raphson method and numerical solvers. Steady-state mathematical modeling of thermal components: Pumps, fans, compressors, turbines, heat exchangers, and binary mixture processes. Property integration and phase-change thermodynamics. Dynamic analysis of thermal systems: Thermal capacitance, fluid delay, differential-algebraic equations (DAE). Dynamic system simulation: Causal block-diagram modeling in Simulink, transient mass and energy balances. Object-oriented dynamic modeling: Acausal physical modeling in Modelica, component libraries, fluid network simulation. Control integration: PID loops, dynamic response, actuator dynamics, thermal stability. Fundamentals of system optimization: Objective functions, design constraints, Lagrange multipliers, Kuhn-Tucker conditions. Single- and multi-variable optimization algorithms: Gradient-based search, heuristic methods, genetic algorithms. Dynamic optimization and Model Predictive Control (MPC): Time-varying boundary conditions, off-design performance, operational energy management. Industrial applications and capstone case studies.

 

 

 

Course Plan:

 

1

Bejan et al.

Fundamentals of Thermal System Design & System Boundaries

2

Bejan et al.

Economic & Cost Analysis in Energy Systems

3

Jaluria

Empirical Modeling & Curve Fitting

4

Jaluria

Linear & Non-Linear Equation Systems

5

Jaluria

Steady-State Mathematical Modeling of Thermal Components

6

Jaluria

Binary Mixtures & Phase-Change Processes

7

Kulakowski et al.

Introduction to Dynamic Thermal Systems

8

Kulakowski et al.

Dynamic System Simulation using Simulink

9

Fritzson

Other Modelica Documents

Object-Oriented Dynamic Modeling with Modelica

10

Kulakowski et al.

Control Entegration & Transient Response

11

Jaluria

Principles of System Optimization

12

Jaluria

Single & Multi-Variable Optimization Algorithms

13

Camacho&Bordons

Dynamic Optimization & Model Predictive Control (MPC)

14

Jaluria

Industrial Applications & Project Presentations

 

 

Primary Textbooks (Core Syllabus & Fundamentals)

1.      Bejan, A., Tsatsaronis, G., & Moran, M. J. (1995). Thermal design and optimization. John Wiley & Sons.

2.      Camacho, E. F., & Bordons, C. (2007). Model predictive control (2nd ed.). Springer-Verlag.

3.      Fritzson, P. (2014). Principles of object-oriented modeling and simulation with Modelica 3.3: A cyber-physical approach. Wiley-IEEE Press.

4.      Jaluria, Y. (2020). Design and optimization of thermal systems (3rd ed.). CRC Press.

5.      Kulakowski, B. T., Gardner, J. F., & Shearer, J. L. (2007). Dynamic modeling and control of engineering systems (3rd ed.). Cambridge University Press.

 

NOTES

Bejan, Tsatsaronis, & Moran (1995) – Thermal Design and Optimization

  • Core Application: Weeks 1–2 (Economic & Thermoeconomic Analysis).
  • Role in the Course: Serves as the primary reference for thermoeconomics, Levelized Cost of Energy (LCOE), Net Present Value (NPV), and exergy-based cost allocation. It bridges thermodynamic principles with real-world financial evaluation.

Camacho & Bordons (2007) – Model Predictive Control

  • Core Application: Week 13 (Dynamic Optimization & Model Predictive Control).
  • Role in the Course: Formulates optimization problems over a moving time horizon subject to dynamic system constraints. It provides the theoretical foundation for operational optimization, real-time demand response, and predictive control in thermal energy management.

Fritzson (2014) – Principles of Object-Oriented Modeling and Simulation with Modelica 3.3

  • Core Application: Week 9 (Object-Oriented Dynamic Modeling with Modelica).
  • Role in the Course: The definitive guide for acausal, equation-based physical modeling. It equips students to construct complex, multi-domain fluid and thermal networks using Modelica without manually deriving explicit state equations.

Jaluria (2020) – Design and Optimization of Thermal Systems

  • Core Application: Weeks 3–6, 11–12, and 14 (Steady-State Modeling & Classic Optimization).
  • Role in the Course: The central textbook for the steady-state portion of the syllabus. Covers empirical curve fitting, non-linear system solving, steady-state equipment modeling, objective function formulation, and optimization algorithms (gradient-based and genetic algorithms).

Kulakowski, Gardner, & Shearer (2007) – Dynamic Modeling and Control of Engineering Systems

  • Core Application: Weeks 7–8 and 10 (Dynamic Analysis, Simulink & Control Integration).
  • Role in the Course: Focuses on dynamic thermal energy balances, lumped parameter methods, thermal capacitance/resistance, and block-diagram (causal) modeling in MATLAB/Simulink. It also covers PID control loops and transient response analysis.

 

Assessment Criteria:

 

Homeworks  

4 x

20

Midterm           

 (Core Theory

 & Steady-State Math)

1

(7 th or 8 th Week)

20

Project and Essay 

(Report, Code

& Presentation)   

1

20

Final

1

40

 

70% Attendance and delivering the Project and Essay are assets for having the right of final exam!

Project Scope & Requirements

  1. System Selection: Choose a real-world complex thermal system (e.g., Data Center Liquid Cooling Circuit, Heat Pump with Phase Change Thermal Storage, EV Battery Thermal Management System, or Solar ORC Microgrid).
  2. Phase I – Steady-State Sizing & Costing: Size the components and estimate CAPEX/OPEX using thermoeconomic formulations.
  3. Phase II – Dynamic Simulation (Modelica or Simulink): Build the dynamic model to evaluate performance under time-varying boundary conditions (e.g., 24-hour ambient temperature profiles or fluctuating power demands).
  4. Phase III – Dynamic Optimization / MPC: Implement an operational strategy (e.g., rule-based vs. Model Predictive Control / dynamic optimization) to minimize daily operational cost while satisfying operating constraints.

Project Milestones & Deliverables

  • Week 5: Project Proposal & System Architecture Definition (10%)
  • Week 9: Midterm Progress Report (Steady-State Model & Initial Dynamic Setup) (20%)
  • Week 14: Final Written Report (formatted as a peer-reviewed conference paper) & Oral Presentation (70%)

 

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