Content

Information security and TISAX
Focus on climate goals: simulation of hydrogen systems
A hydrogen technology center near Braunschweig?
Everything automated with MoBA Automation
New control technology in our test rigs
Development support to series production
Calculation of multiphase flow in innovative cooling systems
Education in the home office: online training courses


Information security and TISAX

To protect the information and data entrusted to us, we are using an information security management system. The TISAX results are now available for this system.
Further information on ISO 9001 and TISAX.


Focus on climate goals: simulation of hydrogen systems

In our TIL add-on library Hydrogen Energy Systems, further fluid data, component and system models are created, which enable the design and evaluation of future hydrogen energy systems.

We at TLK can rely on many years of experience from various consulting and research projects in the field of modeling and simulation of hydrogen systems. With our multifunctional software tools, we offer suitable solutions for the diverse challenges in this field.

Together with our customers, we applied this knowledge and the respective tools for the model-based analysis of various hydrogen filling station concepts. Figure 1 shows a simplified illustration of these concepts. Based on the implemtation of models for

  • liquid (LH2) and gaseous (CGH2) filling station-side hydrogen storage
  • cascading buffer storage
  • direct compression as an operating strategy

and different on-board pressurized gas tank systems (Type III/ Type IV), we simulated and analyzed a variety of concepts already.

Simplified P&I flowchart of a hydrogen refueling station concept in DaVE.
Figure 1: Simplified P&I flowchart of a hydrogen refueling station concept in DaVE.

Many of our software tools are put to good use in the simulation of hydrogen systems. For the model-based design of these systems, the exact calculation of fluid data is essential. For example, when considering hydrogen in a cryogenic state, it must be taken into account that it occurs with two different nuclear spin configurations - as ortho-hydrogen and as para-hydrogen. The differences can be seen, for example, in the specific isobaric heat capacity (see Figure 2 a). The necessary exact fluid data calculation is accomblished with our fluid property data library TILMedia 3.9.1, which is included in TIL Suite 3.9.1. Not only for hydrogen but also for a huge variety of pure fluids and fluid mixtures, TILMedia provides the efficient and robust calculation of the required thermophysical properties.

Figure 2 a) Temperature dependent specific isobaric heat capacity of para-, ortho- and normal hydrogen. b) Temperature dependent composition of equilibrium para- and ortho-hydrogen.
Figure 2 a) Temperature dependent specific isobaric heat capacity of para-, ortho- and normal hydrogen. b) Temperature dependent composition of equilibrium para- and ortho-hydrogen.

The TIL Suite and its add-ons are continuously improved and expanded. We determine the need for new functionalities and specifications with regard to industrial applicability in the course of daily use in our research and service projects. This concerns also customer-specific developments of model libraries for the design and optimization of PEMFC and SOFC systems.

Our software DaVE is used for the comfortable evaluation and visualization of stationary and dynamic effects in complex systems. MoBA Automation allows the flexible creation and processing of extensive simulation studies and workflows. In combination with the Optimization Suite, an efficient system and component design is achieved.

Based on our expertise, we support you in the modeling and design of your hydrogen systems. Do you have specific requirements and wishes - e.g. for the development of a component and system model library, or a workflow we can realize in MoBA Automation? We will be happy to support you in this matter as well.

For further information please contact Ingo Frohböse (i.frohboese@tlk-thermo.com) or visit our product pages.


A hydrogen technology center near Braunschweig?

Together with the Technical University of Braunschweig we are engaged in the Technology- and Innovation Center Hydrogen Technology in Braunschweig-Salzgitter.
Further information can be found in the press release (german) from Allianz für die Region.


Everything automated with MoBA Automation

MoBA Automation 2.1 simplifies the parameterization of complex workflows, results are accessible more quickly. The new version offers additional use cases for test bench automation, measurement data evaluation and model regression testing.

Software for automated workflows

Our software MoBA Automation enables the automation of individual workflows for model or measurement data based development, analysis and optimization of technical systems. For example, users can automatically evaluate complex simulations or measurements, create individual automated reports and documentation (PowerPoint, PDF, etc.), or run simulations in a network. Further examples can be found on our homepage. More about MoBA Automation.

Release of version 2.1

MoBA Automation version 2.1 is available for download since December 2020. The most important new features are summarized below.
We continue to work with great passion and enthusiasm on new features to improve the ergonomics of our MoBA Automation. If you have any feature requests, please contact us: E-mail to Philipp Ebeling (p.ebeling@tlk-thermo.com).

Simplified handling of complex workflows with new interface tasks

In complex workflows, many tasks typically use the same input values (for instance parameter file paths or target directories). The adjustment of parameters due to workflow changes in all individual tasks, which was necessary in the previous version, proved to be inconvenient. In addition, the output values of the workflow were only accessible via the respective tasks, which made it difficult to quickly access important results.

ew Input and Output Interface Tasks
New Input and Output Interface Tasks (far left and far right) in our Dymola Simulation and Check Steady State Results workflow. Connections to and from the tasks are hidden.

Via new interface tasks, input and output values can be defined globally and linked to individual tasks via connections. The connections are marked by a colored accent but remain hidden. Thus, the clarity of the graphic representations is ensured. Furthermore, the values of the interface tasks provide a filter for the inputs and outputs of the workflow. If the workflow is instantiated as part of an entire work process in a comprehensive workflow, the parameters or connectors of the interface tasks are displayed primarily.

The Dymola Simulation and Check Steady State Results workflow as part of a control-oriented system analysis process.
The Dymola Simulation and Check Steady State Results workflow as part of a control-oriented system analysis process. When parameterizing the workflow, only the input interface inputs are now displayed, which significantly simplifies the handling of extensive workflows.
Further new features
  • A new command prompt allows for instance the installation of new Python modules for a Python environment registered in MoBA Automation (see "Edit" in the toolbar)
  • Access to the output files via the context menu in the Status monitor
  • Faster reading of large Mat files in the data processing tasks
  • MDF files (MF4) can now be read by all data processing tasks in the Standard Task Library
  • Many new features for easier and more intuitive workflow development in the Configurator View

Usage of MoBA Automation in test benches

In recent months, MoBA Automation has found new applications in many fields of work, not only at TLK. Especially in tests on our test benches, it increasingly takes over the complete evaluation, analysis and PDF documentation of the measurement data of a single day and relates it to the entire measurement campaign.

In a workflow with recognition of stationary states, MoBA Automation offers the possibility to validate measurement points during operation and to trigger the initiation of the next measurement point. This can save valuable test bench time.

Automated measurement campaigns.

Model regression tests for quality control

For quality control of model libraries, regression tests are available in MoBA Automation in the form of predefined workflows in a task add-on. The repeated execution of model simulations and corresponding evaluations means that errors in modifications can be quickly detected and corrected. The regression tests provide evaluations regarding the following aspects:

  • Simulation success
  • Comparison of time series or simulation end values to a reference
  • Comparison or validation of validity ranges of the models
  • Structural analysis of Modelica models (system of equations, translation log, etc.)
  • For further information please contact Philipp Ebeling (p.ebeling@tlk-thermo.com).


New control technology in our test benches

Using innovative concepts we are able to perform highly dynamic control in our refrigerant compressor test benches. They are used, for example, for time-lapse tests in long-term operation.

Due to our many years of experience with refrigerant compressors, we are able to use our test benches in continuous operation for highly dynamic time-lapse tests or thermal cycling investigations. Degradation effects are evaluated online on the test bench and compared in real time with empirical values or as a software-in-the-loop model. This allows aging effects as well as damage to be detected at an early stage.
For further information please contact Christian Seck (c.seck@tlk-thermo.com).

Development support until series production

Many do not know: Our services also include experimental support for the development of overall thermal management systems - in the area of heating and cooling of passenger compartment and battery.

We adapt our measurement concept to your task. The measurement of the individual components of the overall system represents the basis of our cooperation. The subsequent measurement and evaluation within an overall concept as well as the support up to series production builds on this. Especially actuators and sensors that communicate in the bus system can also be evaluated by us in the overall system. Only the LIN or CAN bus configuration of the target system provided by you is necessary for an integration of the components.
For further information please contact Norbert Stulgies (n.stulgies@tlk-thermo.com).

Pressure reducer on a nitrogen gas cylinder at a test bench

In addition, we offer the following measurement options on our 820 m² test bench area. In the field of components, we perform tests on the following components, among others:

  • Refrigerant compressors for R-744, R-134a, R-1234yf, R-290 and other refrigerants (performance measurement, detailed analysis, development support etc.)
  • Heat exchangers refrigerant-air, refrigerant-miscellaneous fluids (performance, icing detection, de-icing strategies, de-heating etc.)
  • Expansion valves
  • Pumps
  • Fans and blowers
  • Refrigerant collectors (charging curves)
  • Leakage measurements
  • Humidifiers for fuel cells
  • Materials (explosive decompression of elastomers, etc. under R-744)
  • Heat pipes for heat transfer
  • Thermal properties of battery modules
  • Temperature control of battery modules / battery packs
  • Investigation of refrigerant-oil mixtures
  • Support for measurement system testing

In the field of testing complete systems, we have experience and testing capabilities for, among others:

  • Test bench for the investigation of switchable heat pumps for mobile applications (also R-744, incl. battery conditioning)
  • High temperature refrigeration cycle for butane and R-1234ze(Z)
  • Acoustic investigation of flow noise in refrigeration cycle
  • Climatic chamber for long-term tests of components or e.g. for the investigation of stationary heat pumps
  • Support for test bench design (control strategies, etc.)
  • Water extraction from ambient air (research project)

If required, we supplement the measurements carried out with detailed and thermodynamically based post processing of the measurement data using our in-house software tools.

It is our objective to implement the requests of our customers quickly and flexibly. According to your requirements, we realize new test benches in our test laboratory, with which we investigate your problems and collect the measurement data you require.

For further information about our test facilities please contact Norbert Stulgies (n.stulgies@tlk-thermo.com) or Nicholas Lemke (n.lemke@tlk-thermo.com).


Calculation of multiphase flow in innovative cooling systems

As part of the e-Tractiv research project, TLK is expanding its CFD expertise in the area of multiphase flows. STAR-CCM+ is used to model innovative cooling systems.

In the area of field calculations, we recently started to use STAR-CCM+ in-house, to model complex multiphase flows. A special focus is set on disperse multiphase flows, as they occur in heat exchangers. By modeling the fluid flow in detail, we can improve geometries and the refrigerant distribution.

Flow paths of liquid particles.
Flow paths of liquid particles when distributed on flat tubes of a heat exchanger.

Interoperability

Interoperability is a very important aspect of our 3D models. Using TISC or the FMI standard, we can solve complex thermal problems by coupling 3D component simulations with 0D/1D system models. For example, we are currently working on innovative cooling concepts as part of the publicly funded project e-Tractive with our partners Siemens, Infineon and the University of Bayreuth. Together, we are researching efficient coolers for power electronics used in the operation of regional trains and are linking detailed 3D-CFD simulations with 0D/1D system models.

Optimization

In the automotive sector, we recently successfully completed the SiCModul project, expanding our range of expertise in modeling and simulation of active cooling concepts for power electronics. Our central research topic of the project was the fully automated optimization of a pin-fin-geometry considering heat transfer and pressure loss of the cooling element. In this context, the TLK Optimization Suite was used to find an optimal size based on the results of a conjugate heat transfer simulation of the cooling device.

For further information please contact Björn Flieger (b.flieger@tlk-thermo.com).


Education in the home office: online training courses

We are organizing an online training course on Modelica and TIL from March 15 - 19, 2021.
Further information on training.


Newsletter overview

Contents

Hydrogen liquefaction
Visualization of thermal systems with DaVE 2.3
Release of MoBA Automation 2.2 with Draft-Mode and Ready-to-Use-Workflows
Determination of refrigerant leakage
Expanding our competence on oil in refrigerant circuits
Training courses for Modelica and TIL


Hydrogen liquefaction

Using the Linde-Hampson process as an example, we demonstrate the simulation of hydrogen liquefaction with the TIL Add-on Hydrogen-Energy-Systems as well as the visualization of the results in DaVE.

The importance of hydrogen as an energy carrier keeps growing, especially for mobile applications. For efficient storage, hydrogen can be liquefied at a temperature of about 20 K. Such low temperatures can be reached using the Linde-Hampson process. The overall process can be modeled - at first in a simplified form - with standard TIL components (figure 1). Both steady-state and dynamic simulations can be performed. TLK also provides the TIL Add-On Library Hydrogen-Energy-Systems (in short "TIL H2"). It contains additional reservoirs and pressure tanks as well as hydrogen refueling systems. In the fall release, models for fuel cell systems with additional BoP components will be added, and TILMedia will contain substance data extensions for hydrogen liquefaction. In subsequent releases we will provide further models, for example electrolyzers. We will be happy to offer customer-tailored solutions in this respect.

Figure 1: Linde-Hampson process for hydrogen liquefaction modeled with TIL.
In the following, we explain the individual steps of hydrogen liquefaction shown in figure 1.

Compressed-gaseous-Hydrogen supply (CGH2 supply)

First, gaseous (normal) hydrogen from standard gas cylinders is throttled with a pressure reducer to a chosen process high pressure. Hydrogen heats up during this throttling due to the (at this point still) negative Joule-Thomson coefficient.

Precooling

This is followed by roughly isobaric heat transfer in a countercurrent heat exchanger. The heat exchanger enables an energy exchange between the inflowing warm hydrogen and the outflowing cold hydrogen.

Precooling with liquid Nitrogen (LN2)

The already precooled hydrogen is now cooled down further with the aid of vaporized nitrogen in a subsequent countercurrent heat exchanger before it flows through a container with liquid nitrogen. The cooled gaseous hydrogen now has a temperature of approximately 77 K and is at about the pressure level set with the pressure reducer.

Linde-Hampson process

The hydrogen flows into a third countercurrent heat exchanger. Now the Linde-Hampson process begins. In the stationary case, the hydrogen is precooled with hydrogen vapor (state changes 1-2 and 5-6 in figures 1-3). This is followed by isenthalpic throttling, during which the hydrogen is expanded from the process high pressure into the two-phase region (2-3). During throttling, the hydrogen crosses its Joule-Thomson inversion line and thus initially warms up but then cools down altogether. While the vaporous portion of the hydrogen (state 5) is used to precool the hydrogen entering the countercurrent heat exchanger (state 1), the liquid portion (state 4) can be extracted. The returning gaseous hydrogen then passes through all of the above mentioned countercurrent heat exchangers to precool the CGH2 and is released near ambient pressure and temperature into the environment. Alternatively, it can be reintroduced into the process with a compressor.

Using the TIL model of a hydrogen liquefaction plant as presented here, a wide range of information can be obtained. In addition to the component and system design, the modeling also allows a detailed analysis of the influencing variables, e.g. the heat exchanger geometries, the pressure levels and the throughput on the efficiency of the process. Furthermore, suitable control concepts can be designed and tested with TIL.

The results can then be visualized and evaluated over time in DaVE. For this purpose, p-h- or T-s-diagrams, among others, are suitable. In figure 2 and figure 3, these diagrams are shown as an example for the stationary Linde-Hampson process with normal hydrogen after liquid nitrogen precooling. The labeled states can also be found in figure 1.

Figure 2: Illustration of the Linde-Hampson process in a p-h-diagram using DaVE.
Figure 3: Illustration of the Linde-Hampson process in a T-s-diagram using DaVE.

For further information on hydrogen please contact Dr. rer. nat. André Thüring
For further information on TIL please contact Dipl.-Ing. Ingo Frohböse
For further information on DaVE please contact Dr.-Ing. Roland Kossel

We would be happy to support you with your questions about hydrogen-based energy systems. We also welcome your suggestions and ideas.

Visualization of thermal systems with DaVE 2.3

With the new release 2.3 of our visualization and simulation environment DaVE, it is now possible to create Mollier-h-s diagrams, set time offsets of different data sources and integrate DaVE with LabVIEW even better.

DaVE 2.3 release

Soon we will provide you with the DaVE installer for version 2.3 for download. The current version 2.3 contains numerous innovations and improvements, of which only the most important are mentioned below.

Mollier-h-s diagram

With the h-s diagram, you now have another state diagram at hand with which information on specific enthalpy and entropy as well as temperature, pressure and vapor fraction can be displayed. The Mollier-h-s diagram is used - in addition to the p-h diagram - for example in power plant or refrigeration technology.
Extract of an exemplary Rankine process in the Mollier-h-s diagram.

Time settings

New options for data connectors and the DaVE timeline allow for a targeted selection of the data to be handled. By specifying an offset for the time data, individual data connectors can be shifted relatively to each other. This makes it easier to match simulation results with measurement data, for example. The global setting of permissible minimum and maximum times makes it possible to work with slices of long data sources.

New options for instruments

Various new features are available for the instruments in DaVE. For example, vector plots of TIL components can be created very easily via a new automatic feature. New options - such as the definition of decimal character to be used or the autoscaling of plot axes - allow changing the graphical display to fit the individual needs.
Exemplary temperature curves in a counterflow heat exchanger, plotted over the dimensionless length.

DaVE-LabVIEW-Interface

The new version of the interface between DaVE and LabVIEW simplifies the data transfer from LabVIEW to DaVE. Any visualizations such as state diagrams, line diagrams and RI schematics with current states can be continuously adapted during measurement operation without changing the LabVIEW measurement program. During the development of the interface, special attention was paid to simple integration into existing LabVIEW programs. Experiences from our in-house measuring operation have also been incorporated in this process.

Thank you for your valuable feedback. With your suggestions, you have contributed significantly to the further development of DaVE. The DaVE team is always looking forward to a continued good cooperation. Please feel free to contact us.

For further information please contact Dr.-Ing. Roland Kossel


Release of MoBA Automation 2.2 with Draft-Mode and Ready-to-Use-Workflows

The new version 2.2 of MoBA Automation simplifies the development of extensive workflows. Thanks to standardized ready-to-use workflows, you can reach your goal even faster.

The new draft mode for continuous workflow development

Our MoBA Automation enables the automation of individual workflows for model- or measurement data-based development, analysis and optimization of technical systems. Version 2.2 is available for download since the end of April.

The new draft mode makes it possible to build and test a workflow step by step without having to re-run successful tasks. This shortens development time, since the results of longer simulation tasks do not have to be created again, for example. Draft mode can be activated in the toolbar of the configuration view for the respective workflow. If the tasks are successful (green status ring), they can be frozen via the Freeze command in the context menu (gray filling). New tasks can now be added to the workflow and executed.

The new draft mode facilitates the implementation of complex workflows.

New concept of ready-to-use workflows for e.g. steady-state simulations

With predefined input tables, ready-to-use workflows simplify implementations for typical task classes. These include, for example, steady-state simulations in Dymola or for FMUs, file comparisons for regression tests as well as measurement data matching and control analysis.
The simple configuration of the ready-to-use workflow allows existing workflows to be flexibly adapted to new projects.
Ready-to-use workflows can be combined with each other using standardized inputs and outputs. For instance, the outputs of the workflow of a steady-state simulation study can be used as input of the workflows for control analysis or for measurement data validation with only one task connection. The steady-state simulation workflows for Dymola models and FMUs allow to address the different problems within a simulation study with only one workflow. Only the file path to the completed table template, the model file paths and the number of parallel simulation instances need to be specified for parameterization. The workflow offers:
  • Storage of all relevant simulation and result files with timestamp in the output folder
  • Extraction of all time series of defined input and output variables in CSV format
  • Fast visualization of each individual simulation result in our software DaVE
  • Checking of results for steady-state and limit compliance
  • Summary of the last simulation values and the check in a data sheet
  • Display of all invalid or aborted simulations on one data sheet

Other customized solutions

In addition to the standard tasks, we offer our customers further customized solutions that can be used for special automation jobs. Please contact us, if we can support you in the realization of your workflows.

The list of all new features of release 2.2 can be found in the release notes.

We would like to thank you for your valuable feedback, which has contributed significantly to the further development of MoBA. Of course, we continue to work on improving the ergonomics of our MoBA Automation with great pleasure and enthusiasm.

If you have any suggestions and for further information, please contact Dr.-Ing. Philipp Ebeling


Determination of refrigerant leakage

With the help of our new SHED chamber test stand, we can now also support you in determining specification-compliant annual leakage rates.

Components of refrigerant and heat pump circuits should - with regard to environmental sustainability and maintenance intervals - allow for the lowest possible release of refrigerant into the atmosphere. The system manufacturer's specifications therefore define the maximum tolerable leakage rates. With our new SHED chamber test rig, we can measure the refrigerant leakage of various components for ambient conditions from -30°C to 80°C and different specified charge levels. To do this, we first determine the increase in refrigerant concentration in the chamber volume surrounding the component over time. On this basis, we calculate a value for predicting the expected annual leakage.

Refrigerant compressor in SHED chamber with demonstrated leakage and increase in concentration at different times.

The system is designed for measuring components. In the future, it will also be possible to measure the leakage rate of entire systems. Please feel free to contact us.

In addition, we offer a wide range of other measurements. Recent additions are the examination possibilities of the following components and complete systems:

  • Thermal conduction and insulation layers
  • Filling curves, overall performance, start-up behavior, oil distribution on our test bench for the investigation of switchable heat pumps for mobile applications (also R-744, incl. battery conditioning)
  • Compact units for mobile use

If desired, we supplement the measurements performed with comprehensive and thermodynamically founded processing of the measurement data using our in-house software tools.

It is a special concern of ours to implement the wishes of our customers quickly and flexibly. According to your ideas, we realize new test stands in our test laboratory, with which we investigate your questions and collect the measurement data you require. Please contact us.

For further information please contact Dipl.-Ing Norbert Stulgies and Dr.-Ing. Nicholas Lemke


Expanding our competence on oil in refrigerant circuits

TLK is continuing its R&D activities on the subject of oil in refrigerant circuits. After completion of the research project KÖVER, we start another research project in this field with ERNI.

About three billion refrigeration, air-conditioning and heat pump systems are operated worldwide that contain oil to lubricate the refrigerant compressor. The energy efficiency, performance and system behavior of these systems are strongly influenced by the refrigerant charge, which itself depends significantly on the amount of lubricant oil. In the recently completed KÖVER project, TLK researched refrigerant oil distribution in refrigeration circuits together with Technische Universität Braunschweig. Methods for measuring oil distribution were successfully developed and used in first follow-up projects.

Within the framework of KÖVER, new measurement methods for balancing the refrigerant-oil distribution were developed inter alia. For example, the developed method for gravimetric determination of the mass distribution of refrigerant and oil in the system shows good reproducibility and high quality of the measurement results.

Oil flow with strongly inhomogeneous R744 concentration, resulting in streaks and even areas with gaseous R744 (dark area).

Furthermore, new calculation models were created in KÖVER. Among other things, we implemented new methods for calculating mixture behavior in the TILMedia library. An extensive interface to the external fluid property data library Multiflash was also created here.

Using real-time computational methods, we can now determine oil circulation rates (OCR) based on models using sensor signals.

Regarding the effects of oil filling levels on cooling, air-conditioning and heat pump systems, further questions arose in the process of KÖVER, which TLK and TU Braunschweig will examine more closely in the research project ERNI (Erfassung der Nichtgleichgewichtszustände von Kältemittel-Öl-Gemischen zur energetischen Optimierung und Emissionsreduktion von Kompakt-Kaltdampfsystemen = detection of non-equilibrium states of refrigerant-oil mixtures for energetic optimization and emission reduction of compact vapor compression systems). The objective of the new project is the research and development of an innovative model library, which enables the dynamic simulation of compact refrigeration systems under consideration of the non-equilibrium states of the refrigerant-oil mixture. Furthermore, a simulation-based oil filling quantity reduction method is to be made available. Finally, the prototypical model library will be tested on different heat pump systems.

For further questions on the KÖVER and ERNI projects and on the subject of oil in refrigerant circuits, please contact Dipl.-Ing Norbert Stulgies and M.Sc. Andreas Mecklenfeld


Training courses for Modelica and TIL

Together with our partner TLK-Energy, we will be offering online and on-site training courses for Modelica and TIL starting in September 2021. You can find the dates here.


Newsletter overview

Contents

Control oriented system analysis using MoBA Automation 3.0
Topology optimization with VEOTOP
Neural networks for the control of CO2 refrigeration cycles
News from the TLK laboratory: Experiments with membrane humidifiers
New add-ons in the release TIL Suite 3.12.0
Training on Modelica, vehicle air conditioning, TIL and other TLK products


Control oriented system analysis using MoBA Automation 3.0

With the new MoBA 3.0 add-on library "Control Oriented Analysis", a control-oriented system analysis can be performed automatically on the basis of simulation or measurement data. For example, the process gains and time constants at various operating points can be evaluated from simulated step responses, and suitable PID controller parameters can be calculated for them.

MoBA Automation Version 3.0

Our MoBA Automation software enables the automation of individual workflows for the model-based or measurement data-based development, analysis, and optimization of technical systems. Version 3.0 will be available for download in April 2022. With this version, in addition to the TLK Standard Task Library, we also offer optional add-on libraries for special use cases, including the add-on library "Control Oriented Analysis" for control engineering system analysis.

Control oriented system analysis

Systems, such as refrigeration systems in vehicles, must operate in a large operating space. If this operating space has a large variance of ambient temperatures, volume flow rates at heat exchangers and power requirements, and coupled control tasks are necessary at the same time, coordinated controllers are needed at all operating points. When designing these controllers, the following questions arise:

  • Which controlled variables are useful?
  • Which manipulated variable is suitable for influencing a controlled variable?
  • How large are the nonlinearities?
  • Are model-based controllers, gain scheduling or other concepts suitable?
Add-On Library "Control Oriented Analysis"

The MoBA Automation add-on library "Control Oriented Analysis" supports you with its workflows in answering the above questions. The starting point of the workflow can be either measurement data or simulation models. For the control-oriented analysis of a system model, the model must be modified in such a way that the operating point to be analyzed can be set, a step can be applied to an actuator, and the uncontrolled step response can be recorded. The workflow for control oriented analysis evaluates all step responses. It fits first order behavior, higher order behavior, and dead time using various methods. From this, gain factors and time constants can be determined. Nonlinearities can be quantified via the operating points. The evaluation - e.g., via relative gain arrays - allows the assignment of actuators to controlled variables. Furthermore, interactions between controlled variables can be represented in this way.

MoBA workflow and evaluation diagrams of the control oriented system analysis
Parametrizing the controllers

Using the calculated system properties such as PTx behavior and the dead time, a workflow task determines the parameters for PI controllers using various methods and validates them on the model. All results of the workflow are summarized in a PowerPoint report.

Further innovations in MoBA Automation Version 3.0

In addition to control oriented system analysis, MoBA Automation Version 3.0 also offers other add-on libraries for further use cases, such as model reduction or machine learning, model regression testing, model validation, optimization, and measurement data evaluation. In addition, the new release contains numerous ergonomic improvements, such as an accelerated start of the task or better assistance when parameterizing complex workflows. Licensees will soon be receiving detailed information about the new version. A list of all new features of version 3.0 can also be found in the release notes.

Customized solutions for process automation

In addition to the standard tasks and the add-on libraries for special applications, we also offer our customers customized solutions on request, which can be used for individual automation tasks. Please contact us if you need support for the implementation of your workflows.

Your Feedback

We thank you for your valuable feedback, which has contributed significantly to the further development of MoBA. Of course, we will continue to work on improving the ergonomics of MoBA Automation with great enthusiasm. If you have any suggestions, please do not hesitate to contact us.

Contact Control Oriented System Analysis: Dr.-Ing. Andreas Varchmin
Contact MoBA Automation: Dr.-Ing. Philipp Ebeling


Topology optimization with VEOTOP

As part of the funded VEOTOP R&D project, which was completed in autumn 2021, TLK, together with Technische Universität Braunschweig, developed software tools for topology optimization of complex thermal energy systems.

The Topology of Thermal Energy Systems

Thermal energy systems must perform an ever-increasing number of thermal processes: cooling, heating, deep-freezing, air-conditioning, dehumidifying, etc. - and all under changing operating conditions! For the topology of the energy systems, i.e., the selection and combination of the technical components, there are an unmanageable number of options. To evaluate these topologies, concepts for operating strategy and control are also necessary.

VEOTOP

To address this need, TLK developed methods and software tools in the VEOTOP project, together with the Technische Universität Braunschweig (Labs for thermodynamics, control engineering and mathematical optimization), to design and optimize topologies and to evaluate them including operating and control strategies.

The newly created methods and tools make it possible to generate several topologies of a thermal system based on user specifications and to optimally design these topologies taking dynamic boundary conditions into account. The developed systems can then be automatically simulated and visualized.

We would like to thank the Federal Ministry of Education and Research for funding and supporting the project.

Synthesis and topology optimization
Effects of the results on TLK software

The newly developed methods and algorithms are incorporated into various TLK software products:

  • Optimization Suite: Model-based design and control optimisation.
  • MoBA Automation: Controller design and parametrization based on a control oriented system analysis
  • DaVE: Interactive visualization of topologies

Contact: Dr.-Ing. Andreas Varchmin


Neural networks for the control of CO2 refrigeration cycles

The use of neural networks to create computationally efficient surrogate models can produce high quality approximations to physics-based models. Through their use in the control of a CO2 refrigeration cycle, TLK was able to achieve COP-optimized operation as well as an improvement in system behavior during load changes.

The energy-efficient operation of thermal management systems through advanced control concepts is an important element of the vehicle of the future. As part of the PHyMoS (Proper Hybrid Models for Smarter Vehicles) research project, TLK is investigating the use of machine learning models for model-based control of thermal management systems for passenger cars. Compared to physical-mechanistic models, these models can be characterised by a significantly lower computational load for model evaluation, which is why their use is particularly advantageous when there is little available computing capacity.

TLK has investigated the use of neural networks to determine optimal process parameters and for model-based feedforward control of a CO2 refrigeration cycle.

Determination of optimal process parameters

The determination of optimal process parameters is a significant challenge for complex refrigeration cycles. Due to their high complexity, detailed physical-mechanistic models of refrigeration cycles are only suitable for online optimization to a very limited extent. Thus, optimization calculations are usually carried out separately and the results are stored - e.g., in the form of a characteristic diagram - for further use. The use of machine learning methods, on the other hand, enables online optimization calculations to be carried out on the basis of a reduced, approximate model of the refrigeration cycle. For this purpose, a neural network is trained to map the correlations between the process boundary conditions, the process parameter to be determined and the variable(s) that are relevant for the optimization task. This method, shown in Figure 1, was used to determine the optimal high pressure of a switchable CO2 refrigeration cycle with parallel evaporators. With the help of the neural network, the maximum possible COP (Coefficient of Performance) was achieved within a mean relative deviation of approximately 0.4 % compared to the simulatively determined maximum COP (tested for 80 random boundary conditions).

Determination of optimal process parameters of a CO2 refrigeration circuit with the aid of a neural network
Model-based feed forward control

A significant improvement of the control behaviour can be achieved by a model-based determination of the controllers’ actuating variables. Typically, simplifying model assumptions are made when creating surrogate models required for such a model-based feedforward control, which means that nonlinearities in the correlation between actuating variables and boundary conditions are unlikely to be represented adequately.

For this reason, TLK has tested the suitability of neural networks as approximate surrogate models for use in model-based feedforward control. A neural network was used for the feedforward control of the compressor speed of a CO2 thermal management system. This resulted in a significant improvement of the control behaviour compared to a simulatively designed PI controller. As a result, the maximum control error of the supply air temperature was reduced by 50 % when the air mass flow rate in the air conditioning unit was changed.

Provision of neural networks

TLK can provide the generated neural networks either as code in the programming language C or as a DLL. The neural networks can thus be integrated into both Modelica and Matlab/Simulink models or compiled and executed on any system.

Future research on machine learning methods

TLK is constantly researching and developing new ways of using various machine learning methods to solve control engineering problems. In particular, the determination of trajectories to improve control behaviour and the use for icing prevention are to be investigated more intensively in the future.

Contact Machine-Learning: M.Sc. Henrik Schatz
Contact Control: Dr.-Ing. Andreas Varchmin


News from the TLK laboratory: Experiments with membrane humidifiers

For efficient operation and to promote long lifetimes of PEM fuel cells, a good water balance is crucial. To optimize this, membrane humidifiers are used in PEM fuel cell systems. TLK is investigating such membrane humidifiers on a complex test rig.

The thermodynamic losses of a fuel cell are significantly influenced by moisture-dependent proton conductivity. Water content also affects fuel cell degradation. To minimize thermodynamic losses as well as slow the degradation process, so-called membrane humidifiers are used.

In membrane humidifiers, water contained in the air is driven from the wet exhaust air to the dry supply air of the fuel cell by the moisture concentration gradient. The humidifier membrane used for this purpose is highly permeable to water, but blocks the transport of other gases.

Hollow fiber membrane humidifier and experimentally determined mass transfer efficiency.

In TLK's laboratory, the investigation of heat and mass transfer in membrane humidifiers under various boundary conditions - e.g., air mass flow rates, relative humidities, temperatures and pressures - is possible. The greatest challenge is to precisely adjust the mass flows and at the same time accurately measure the power. Even low quantities of only a few watts are relevant. And when it comes to balancing, every drop of water, no matter how small, counts.

In addition to individual investigations for our customers, TLK also carries out its own measurements as part of the publicly funded AUTO-GEN project. AUTO-GEN stands for "Partially automated generation of physics-based and data-based thermodynamic models for the design and operation of complex systems in e-mobility". In AUTO-GEN, among other things, humidifier models for fuel cell systems in commercial vehicles are created in collaboration with the Technische Universität Braunschweig (Markus Pollak, m.pollak@tu-braunschweig.de). The models are then validated with measurement data obtained from the test bench.

These humidification experimental investigations are just one example of the interaction between experiment and simulation at TLK. A wide variety of experimental investigations are carried out in labspace of around 1000m², both for our customers and for the optimization of our model libraries.

Contact: Dr.-Ing. Nicholas Lemke


New add-ons in the release TIL Suite 3.12.0

The next release of TIL Suite 3.12.0 for the simulation of thermal systems will be available in April 2022. Many add-ons - new as well as extended - offer additional application possibilities in the areas of heat exchangers, adsorption, hydrogen energy systems, automotive, cabin, and heat storage.

The TIL Suite provides versatile thermal component and system models for steady-state and transient simulations. Some of the essential innovations of the TIL Suite 3.12.0, which will be released in April, are presented in the following. Several add-ons are new, while others have been extended. There are also improvements in the TIL Suite Basic and TILMedia. Compatibility with Modelon Impact and OpenModelica has been optimized for the entire TIL Suite.

Add-On NTU-HX

The new add-on NTU-HX provides heat transfer models with particularly high computational speed. For this purpose, a new calculation methodology is used, which TLK has developed together with the Lab for Thermodynamics at the Technische Universität Braunschweig (Hamidreza Hassani, h.hassani-khab-bin@tu-braunschweig.de) on the basis of the NTU approach. The analytical calculation methodology used means that the temperature curves within the heat exchangers can be mapped with a high degree of accuracy, even for phase change phenomena. A comparable accuracy is also possible with the heat exchanger models used to date in TIL; however, the finite volume method they use requires a high discretization, which results in a higher calculation effort.

The heat exchanger models in the new add-on are designed for the combination of the following media:

  • Refrigerant-gas
  • Refrigerant-liquid
  • refrigerant-refrigerant
  • liquid-liquid
  • Liquid-gas
  • Gas-Gas

The respective heat exchanger can be parameterized either very simply with a single global UA-value or also with αA-values on both sides. In addition, the next release will also allow consideration of local heat transfer relationships, the calculation of pressure losses and condensation of moist air.

We will be happy to advise you on finding the right heat exchanger model to answer your individual modeling needs.

Temperature curve over heat flow and position in a counterflow heat exchanger with R1336MZZ and R134a. The temperature curves for the five zones were determined analytically using the NTU method.
Add-On Adsorption

The new add-on Adsorption enables the simulation of gas purification and gas drying plants. The library contains a universal basic model for gas mixtures and adsorption equilibria. With the help of the modular models, complex adsorption plants and processes - such as PSA plants (Pressure Swing Adsorption), TSA plants (Temperature Swing Adsorption) or Direct Air Capture processes - can be modelled. These models are designed to analyze breakthrough curves and adsorption kinetics and to draw conclusions for optimization (e.g., of cycle times).

The substance database of available adsorbents can be easily expanded.

Temperature Swing adsorption system for gas drying or gas cleaning as Modelica TIL model
Add-On Hydrogen-Energy-Systems

Hydrogen-based energy systems can be modelled with the already existing add-on Hydrogen-Energy-Systems (referred to as "TIL H2"). What is new is that the PEM fuel cell model can be discretized along the gas channels. Models for describing the water balance have also been added. The system example with anode and cathode supply has been extended to include humidifier bypass control. A jet pump model is now available for recirculation on the anode side. More types of stack models have been added. A reversible SOC (solid oxide cell) for operation as SOFC (fuel cell) or SOEC (electrolyser) can be flexibly parameterized and simulated for both pure hydrogen operation and operation with methane. Examples for the operation of a SOFC with methane steam reformer and a SOEC with reformer for methanation were created.

As an additional example, hydrogen refuelling using a tube trailer is modelled. An adaptation of individual boundary conditions for the subsequent simulation is possible via various settings.

Add-On Automotive and Cabin

Thermal management systems in vehicles can be modelled with the already existing add-on Automotive and Cabin. One of the innovations is that the refrigerant R-1234yf is used instead of R-134a in the examples for refrigeration circuits in automotive applications, as this corresponds to current practice. In addition to reciprocating compressors, scroll compressors are also used in the refrigeration circuits. The detailed heat exchanger with MPET geometry "Detailed MPET", which was previously only available for VLEFluid/Moist-Air, is now also available for Liquid/Moist-Air. It includes all thermal masses for headers as well as several airflow layers.

Add-On HeatStorage

Thermal energy storage can be modelled with the already existing add-on HeatStorage. One of the innovations is that an internal heat exchanger can be optionally activated for the hot water storage model with temperature stratification. The pipe coil through which an incompressible fluid flows can be individually parameterized with geometry and heat transfer data and connected to the outside via liquid ports.

TIL Basic and TILMedia

In the new release of TIL Suite 3.12.0, two examples for vehicle air conditioning, among others, are adapted to the refrigerant R-1234yf, according to the innovation in the add-on Automotive and Cabin. A pressure loss model with nominal point for mass flow and density is now available for all refrigerant heat exchangers. With the help of the parameters for nominal values and exponents of mass flow and density, fitting the model to measured data is usually very easy.

With the new release of TILMedia 3.12.0 methanol as a VLEFluid and updated TILMediaSpline media are available. Further fast-calculating TILMediaSplines are available on request.

TILMedia substance properties of methanol shown in DaVE

Contact NTU: Dr.-Ing. Nicolas Fidorra
Contact Hydrogen: Dr. rer. nat. André Thüring
Contact TIL Suite: Dipl.-Ing. Ingo Frohböse


Training on Modelica, vehicle air conditioning, TIL and other TLK products

We offer our proven training courses for Modelica and TIL both online and in person. We also offer individual training courses on vehicle air conditioning and our products upon request.

Together with our partner TLK-Energy, we will again offer training courses for Modelica and TIL from April 2022. Online and face-to-face events are planned. The exact dates and additional information can be found here.

In addition, we would like to point out our individual course offering on the subject of vehicle air conditioning. In a one or two-day training course, in addition to the basics of thermodynamics, heat transfer and climate physiology, the special requirements in passenger compartments and various refrigerants are discussed. The different air-conditioning requirements of conventional and electrified passenger cars and other means of transport are also addressed. It is possible to adapt the course to your individual requirements and questions. For those interested in this topic, we would also like to draw your attention to the Vehicle Air Conditioning Conference (HdT and TAE) on 27 and 28 April 2022 at the Haus der Technik in Essen.

We offer further training options for our products in the areas of simulation technology, visualisation, optimisation, and automation on request. If you have any suggestions or questions, please do not hesitate to contact us.

Contact:

Dr.-Ing. Wilhelm Tegethoff
Dr.-Ing. Sven Försterling


Newsletter overview

Contents

H2 system simulations featuring fuel cell, electrolyzer, humidifier, water separator and other components

CFD simulation involving cryogenic hydrogen

Development of hybrid physical-data based modeling techniques


H2 system simulations featuring fuel cell, electrolyzer, humidifier, water separator and other components

Models for various types of fuel cells and electrolyzers are being developed in our TIL add-on library "Hydrogen Energy Systems". Together with the additionally included balance-of-plant models - for humidifiers, water separators, valves, pipelines as well as various turbomachines - the library enables steady-state and dynamic system simulations all around the fuel cell.

At the heart of the simulation of a fuel cell system - based on our TIL add-on library "Hydrogen Energy Systems" - is a flexible and easy-to-parameterize stack model. Figure 1 shows such a system: The oxygen supply to the stack is provided by a compressor, heat exchanger and humidifier (cathode, figure 1 right). The hydrogen supply includes recirculation and a purge valve to vent undesired nitrogen from the anode (Figure 1, left). The PEM fuel cell stack is operated within the desired temperature range using internal cooling channels and an external cooling system.

Figure 1: Fuel cell system diagram with stack model (center) anode (left) and cathode supply (right) and simple cooling (top).

Due to the numerically robust modeling of the component and system models, such a system can be efficiently simulated under both steady-state and dynamic operating conditions. A highly dynamic example is the purge of the anode circuit with a two-point control. Figure 2 shows the curve of the nitrogen concentration as well as the system loss resulting from the purge over time in an exemplary simulation.

Figure 2: Time curve of the nitrogen concentration in the anode circuit (top) and corresponding system loss resulting from the purge (bottom) obtained from an exemplary simulation.

Flexible base models for PEMFC and SOFC stacks are available in the TIL add-on. These contain basic, fundamental reaction equations of the electrochemical cells. For example, the clamping voltage is calculated based on the reversible cell voltage. To do this, the relevant losses from the activation overvoltage, from the electrical and ionic ohmic overvoltage, and from the mass transfer overvoltage are considered (see Figure 3). For the description of these loss mechanisms as well as the heat, mass and charge transport, exchangeable and expandable correlations are provided. These options allow individual model adaptation and parameterization based on customer-specific requirements.

Figure 3: Cell voltages and losses versus electric current density

The calculation and use of thermophysical material properties is indispensable in all models. Thanks to the TILMedia substance data library, chemical and electrochemical reactions in the fuel cell models can be balanced precisely and extremely quickly in terms of energy. Reaction heats as well as thermoneutral and reversible cell voltages as a function of temperature, pressure and concentration can be determined from the stored functions for calculating the Gibbs Energy (see Fig. 4).

Figure 4: Calculation of the thermoneutral and reversible cell voltage of silent water formation at 1 bar (left) and 10 bar (right).

TLK-Thermo can rely on many years of experience from various customer service and research projects for the development of fuel cell stack and system models. Fuel cell simulations and component measurements for the automotive and aerospace sectors are carried out either directly at TLK or also in cooperation with the Institut für Thermodynamik at the Technische Universität Braunschweig. The PEMFC and SOFC stack models of the upcoming October release TIL 3.11 are also based on our many years of programming experience with Modelica in Dymola. In the future, we will develop further types of fuel cell and electrolyzer models, whereby we will be happy to meet your requirements. Please contact us also for other questions in the field of hydrogen, for which we can model, simulate or measure for you.

Contact Hydrogen: Dr. rer. nat. André Thüring
Contact TIL Suite: Dipl.-Ing. Ingo Frohböse


CFD simulation involving cryogenic hydrogen

Hydrogen has an increasingly important role in our energy supply. For various applications of storage and transport of H2, precise models are needed to simulate complex phenomena. Using the example of a vortex tube, we show capabilities of 3D field computation, taking into account real-gas behavior at high velocities.

In the future, liquid hydrogen (LH2) will play an important role in the transport, storage, and refueling systems of hydrogen for aircraft and trucks. Efficient processes for cooling and liquefying hydrogen are a fundamental requirement for hydrogen storage. The so-called vortex tube - or a Ranque-Hilsch vortex tube (Figure 1) - is one component suitable for these processes. It is well suited because it has no moving parts and allows an incoming gas stream (green arrows) to be split into two streams with a significant temperature difference (red arrow, blue arrow).

The range of applications of the vortex tube extends from compressed gaseous hydrogen to liquid hydrogen: At hydrogen filling stations, the vortex tube can be used for the conditioning of compressed gaseous hydrogen. For the storage of LH2, it can be applied for boil-off gas reduction as well as liquid subcooling by means of a para-ortho catalyst integrated in the vortex tube (so-called Heisenberg Vortex Tube).

Figure 1: Streamlines of the flow velocity and contour of the temperature in the cross section of the vortex tube.

In order to be able to represent the behavior of the vortex tube with the aid of a 3D flow simulation, we use Star-CCM+. Due to the high flow velocities (approx. 0.8 Ma) on the one hand and the simultaneously low temperatures on the other hand, we use a coupled flow solver with a real gas model according to Redlich-Kwong in Star-CCM+. We use TILMedia to determine the necessary parameters of the material properties at a Para/ortho ratio of 1 to 3.

The results of the simulation are shown in Figure 1. On the basis of the streamlines, the resulting flow field as well as the temperature profile show the expected separation of the mass flows into a cold and a warm flow. This picture agrees qualitatively with the observations from the publications in [2] and [3]. The temperature field calculated in the simulation shows high consistency with the results of the publication by Matveev [1]. The drop in total gas temperature between inlet and cold outlet is 8.94 K in the simulation performed by TLK. This corresponds to the results of Matveev with a deviation of 0.05 K or 0.6%.

The high accuracy of our simulation results provides the basis for system simulations as well as potential analyses for the integration of the vortex tube into the hydrogen industry.

Contact CFD: Dr.-Ing. Björn Flieger
Contact TILMedia: Dipl.-Ing. Ingo Frohböse

[1] Matveev, 2021, Numerical Simulations of Cryogenic Hydrogen Cooling in Vortex Tubes with Smooth Transitions
[2] https://en.wikipedia.org/wiki/Vortex_tube
[3] Taha et al., 2013, Vortex Tube Air Cooling: The Effect on Surface Roughness and Power Consumption in Dry Turning


Development of hybrid physical-data based modeling techniques

Within the PHyMoS research project, TLK is developing models and methods that allow a purposeful combination of physical and data-based approaches. Hybrid modeling techniques enables flexible solutions – based on the data situation, the necessary speed and the required accuracy.

The aim of the currently running PHyMoS project (Proper Hybrid Models for Smarter Vehicles) is to develop methods and tools for the automated generation of so-called "Proper Models". In this context, the term "proper" refers to achieving the required model accuracy while keeping complexity to a minimum. Proper models can be generated using hybrid modeling methods in which models are combined from physical knowledge and data-based approaches. The consortium of the BMBF-funded project includes three universities (University of Augsburg, TU Braunschweig and FH Bielefeld) and six companies (Bosch, ESI ITI, LTX, Modelon, XRG and TLK), whereby we mainly contribute our knowledge in our core competence field of thermal systems modeling.

When modeled in appropriate detail, purely physical models have high accuracy and good extrapolation properties. They are based on a system of physically motivated equations, which is solved by a suitable simulator. Data-based models, on the other hand, are based on the use of extensive data sets. The modeling techniques used here range from simple polynomial fitting to Machine Learning techniques (e.g. Deep Learning) and model reduction (e.g. using Proper Orthogonal Decomposition - POD). Data-based models compared to physical models often allow a significantly more efficient model evaluation. However, this high computational efficiency is usually accompanied by insufficient extrapolability. A targeted combination of the advantages of both models should lead to a Proper Model.

The PHyMoS project partners are therefore working on hybrid physical-data-based modeling techniques. We develop methods to increase the computational speed of detailed physical models by using data-based approaches. Furthermore, methods to improve the accuracy and range of validity of existing models using both physical and data-based modeling techniques are investigated. For example, the accuracy of a physical model can be increased by adding previously non-modeled phenomena to this model - based on data. On the other hand, the information content of data-based models - and thus also their accuracy and extrapolation capability - can be increased by incorporating physical knowledge. With the help of the methods and tools developed within the framework of PHyMoS, an application-specific optimization of existing models should be made possible.

Figure 1: : Process of generating a Proper Model with input mixture and possible applications. [source: Bosch on behalf of consortium PHyMoS]

At TLK, we provide our customers with methods, models and software using open standards. In the future, we also want to use findings from the PHyMoS project and the Proper Models approach. If you have any questions about the project or about modeling thermal systems with Proper Models, please do not hesitate to contact us.

Contact: Dr.-Ing. Andreas Varchmin and B.Sc. Henrik Schatz


Newsletter overview