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Analysis, Modelling and Simulation of Energy Systems. Brief CV, Mads Pagh Nielsen:. 1999 (June): Master of. Science in Mechanical Engineering at Aalborg University , IET . 1 999 (August): Hired at Intecon A/S in Aalborg, Denmark, as advisory engineer.

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analysis modelling and simulation of energy systems
Analysis, Modelling and Simulation of Energy Systems

Brief CV, Mads Pagh Nielsen:

  • 1999 (June): Master of. Science in Mechanical Engineering at Aalborg University, IET.
  • 1999(August): Hired at Intecon A/S in Aalborg, Denmark, as advisory engineer.
  • Among other things being in charge of following larger projects:
        • Design and operational optimization of an industrial combined heat and power plant.
  • -Design & optimization of an efficient wood-chip removal plant (information- and
  • research project done in collaboration with the Danish Energy Ministry and the Wood-
  • Industry).
  • - Energy efficient air-filters (demonstration-project done for the Danish Energy Ministry).
  • 2000 (October):Employed and enrolled as Ph.D. student at Aalborg University working with the project “Modelling of Thermodynamic Fuel Cell Systems”.

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

course outline m m 1 3
Course outline (m.m. 1-3)
  • Mini-module: “Introduction to Engineering Equation Solver (EES) part I”. Introduction to the modelling software EES – basic purpose, functionality and examples. Solution of the general non-linear problem of multiple equations using the general multi dimension Newton Raphson method. Usage of guesses and limiting of variable values.
  • Mini-module: “Introduction to Engineering Equation Solver part II”. Further work with EES. Working with tables, plots, using array variables and using array operators.
  • Mini-module: “Introduction to Engineering Equation Solver part III”. Introduction to procedures, sub programs and modules – advantages and drawbacks.
  • Literature: Lecture notes about EES + Reference Manual.

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

course outline m m 4 6
Course outline (m.m. 4-6)
  • Mini-module: “Basic conservation equations”. Derivation of the stationary forms of 1st law of thermodynamics for open- and closed systems and definition of basic thermodynamic relations useful in modelling of energy systems. The continuity equation (conservation of mass). How to use the 1st law of thermodynamics and the continuity equation on a system control volume? Calculation of thermodynamic- and calorimetric properties; i.e. use of diagrams, tables and use of EES to attain these properties and the importance of choosing appropriate unit system.
  • Mini-module: “Modelling of system components”. Developing stationary models of thermodynamic components such as heat exchangers, wind turbines, pumps etc.
  • Mini-module: “Advanced conservation laws and structured modelling”. Use of multi-gate methods in the modelling of energy systems. Conservation of energy in the presence of chemical reaction (in particular combustion) and calculation of properties of ideal gas mixtures.
  • Literature: Lecture notes about modelling of energy systems written by the lecturer (me!)

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

course outline m m 7 10
Course outline (m.m. 7-10)
  • Mini-module: “Modelling of part load conditions part I”. Part-load characteristics for components (i.e. pumps, turbines, compressors etc.). Usage of characteristics in computer modelling introducing numerical techniques such as interpolation in ordered tables and multi dimension non-linear regression using the method of least squares.
  • Mini-module: “Modelling of part load conditions part II”. An example on an advanced utilisation of part load modelling. Usage of models in optimisation. Sensitivity- and uncertainty analysis in evaluating sensitive parameters.
  • Mini-module: “Advanced cycles”. Combined-cycle co-production plants, chemical plants (exemplified by a fuel cell system). Discussion of the term ‘complexity’ and the necessity of detailed- contra lumped modelling. Briefly about classification of losses (exergy or 2nd law analysis).
  • Mini-module: “A primer on optimisation of energy systems”. Choice of objective function (for example economy, volume, effectiveness operational conditions etc.) and usage of parameter analysis in determining free parameters.
  • Literature:Additional lecture notes made by the lecturer still due to be written!

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

formal definition of modelling
Formal definition of modelling:

“The activity of translating a

real problem into mathematics

for subsequent analysis”

(Some might disagree?)

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

people interpret things differently
People interpret things differently 

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

motivation why model
Motivation: Why model?
  • Design and Optimization
    • How do we make our system optimal from scratch or how can we improve the existing system?
  • Validation
    • Will the proposed system we have designed work correctly subjected to the environment it operates in, is it feasible to construct it compared to other alternatives and does it fulfill its purpose?
  • Interpolation
    • Usage for filling in missing data – for instance parameters we can not measure from experiments.
  • Extrapolation
    • Predictions into the future: How is our plant economy in 20 years?

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

what is modelling the process
What is modelling? The process:

?

1. Identify the

real problem

2. List the factors

and assumptions

Did assumptions hold?

5. Compare with

the real world

3. Formulate and solve the

mathematical problem

4. Interpret the

mathematical

solution

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

problem identification
Problem identification

”Design is within a scientific context the art of describing

predictable systems”.

MPN, 2002

Should we attempt to re-design this pencil sharpener??!

Junk in equals junk out! Be critical!

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

assumptions
Assumptions

”Simplicity is beautiful but it is extremely complicated to attain it”.

MPN, 2002

Parameter estimation and choice of the phenomena we would

like to model is difficult! The model should reflect this. A

complicated model with inaccurate input is not making us any

smarter – rather often more confused!

Uncertainty of models and inputs have to be analyses

And compared accordingly! Do not expect 1.9823673467 to be

the correct answer nor a correct input variable.

Always keep models as simple as possible!

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

solution of the problem
Solution of the problem
  • When we have formulated our problem we use our theoretical
  • skills to develop a solvable mathematical model.
  • In almost any case his turns out to be a number simultaneously
  • coherent mathematical expressions we need to solve. This is the
  • (relatively) easy part of modelling!
  • Subsequently, we need to be able to interpret and validate the
  • result critically against empirical knowledge or experiments.

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

thermodynamics is strongly non linear
Thermodynamics is strongly non-linear!

Where fiis either linear or non-linear functions depending on

the unknown x’s

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

a few notable points
A few notable points:
  • There is hardly any real thermodynamic process that can be modelled without solution of non-linear equations!
  • In order to have a consistent system the number of equations and variables has to be similar. If not, the system will be either over- or under determined and we cannot find a general solution.
  • If the system consists of n linear functions and the system is said to be numerically consistent we can always find a unique solution. However, if we CANNOT check this generally for non-linear equations nor guarantee one unique solution! We can often have several feasible solutions, which make solution of the system anything but trivial. The numerical complexity mentioned above is due to the non-linearity.
  • In general, systems of non-linear equations have to be solved iteratively using numerical methods. Analytical solution is rarely possible.

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

taylorisation of functions
»Taylorisation« of functions

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

iterative algorithm newton
Iterative algorithm (Newton):

(*)

1.Choose initial values and set (‘0’ denote initial values).

2.Substitute the x’s into (*).

3.Solve for the x’s (linear algebra).

4.Is convergence reached? If yes, output result. If not, continue.

5.Set all

6.Set all and go back to point 2.

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen

sir isaac newton
Sir Isaac Newton!

Analysis, Modelling and Simulation of Energy Systems, SEE-T9

Mads Pagh Nielsen