A Methodology to Compare Different Co-simulation Interfaces:

A Thermal Engineering Case Study

Georg Engel, Ajay S. Chakkaravarthy and Gerald Schweiger

AEE - Institute for Sustainable Technologies, Feldgasse 19, Gleisdorf, Austria

Keywords:

Co-simulation, FMI, BCVTB, Trnsys, Simulink, Compact Thermal Energy Storage.

Abstract:

A method is presented to compare different co-simulation interfaces and applied to a case study of thermal

engineering. Different interfaces providing loose and strong coupling based on the Functional Mockup In-

terface (FMI), the Building Controls Virtual Test Bed (BCVTB) and a Component Object Model (COM)

are compared with respect to user-friendliness and ﬂexibility, computational costs and accuracy. The case

study considered includes a compact thermal energy storage modelled in Trnsys and a heat sink modelled in

Simulink. The implemented strong coupling scheme is a factor of 10 more accurate while a factor of almost

100 computationally more demanding than the loose coupling one.

1 INTRODUCTION

1.1 Motivation and Background

The need for co-simulation is a pragmatic one:

Complex models are usually decomposed into sub-

systems, where different tools and methods are used

by different teams to implement these sub-systems.

This can be seen in many application areas such as the

design of energy-systems, automotive industry or any

interdisciplinary ﬁeld. The need for co-simulation in

the ﬁeld of energy systems arises from the goal of the

energy transition: (i) existing systems must become

more efﬁcient and (ii) as ﬂuctuating energy sources

such as wind and solar energy expand, other parts

of the energy systems must become more ﬂexible to

match the available energy from renewable resources

with the demand in terms of location, time and quan-

tity. There are a number of options for increasing

energy system ﬂexibility, including combining differ-

ent energy domains, increasing supply and demand

ﬂexibility or integrating energy storage technologies

(Lund et al., 2015; Schweiger et al., 2017). In the

automotive sector, the transition to e-mobility poses a

variety of challenges. Considering the lack of waste

heat and the narrow temperature window required by

the battery, a smart thermal management of vehicles

including thermal storage becomes increasingly im-

portant (Bandhauer, 2011; Engel et al., 2017). This

leads to new requirements for simulation approaches

and tools. To increase the efﬁciency of existing sys-

tems, detailed models of all sub-systems that capture

all important dynamics are required.

In order to study different system solutions that

increase the system ﬂexibility, completely new chal-

lenges need to be overcome such as (i) the coupling

of different domains and (ii) increased sub-system

dynamics as a result of this coupling; this leads to

new challenges for some domains. The simulation

of speciﬁc solutions provides the necessary insights

and information to support the transformation pro-

cess towards sustainable energy systems. There are

already tools for all domains and aspects of district-

scale energy systems, but no single tool can cover

all domains and aspects in order to simulate the en-

tire system (Allegrini et al., 2015). Co-simulation

approaches allow for the combination and reuse of

existing tools and methods that are robust and well-

suited for their particular domain. Models of different

sub-systems require different modelling approaches

and hugely differing step sizes or even solver algo-

rithms. Further advantages of co-simulation are (i)

it facilitates cross-discipline and cross-company col-

laborations, (ii) the possibility to protect model intel-

lectual property rights of sub-systems, (iii) robust co-

simulation frameworks can signiﬁcantly shorten the

innovation cycle (robust prototyping) of novel system

and control concepts. F.i. different control algorithms

can be tested virtually at a system model without fur-

ther modiﬁcation. A main drawback of co-simulating

is that numerical stability problems may arise (Tr-

cka et al., 2009), code optimizations within a partic-

410

Engel, G., Chakkaravarthy, A. and Schweiger, G.

A Methodology to Compare Different Co-simulation Interfaces: A Thermal Engineering Case Study.

DOI: 10.5220/0006480204100415

In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2017), pages 410-415

ISBN: 978-989-758-265-3

ular tool may be lost (Wetter et al., 2015) and some

co-simulation frameworks have inconvenient appli-

cation programming interfaces so that such methods

are inappropriate for engineering applications. An

overview of co-simulation approaches and tools, re-

search challenges, and research opportunities are pre-

sented in (Gomes et al., 2017; Trcka, 2008; Atam,

2017; Mathias et al., 2015). In (Gomes et al., 2017),

co-simulation approaches are divided into three cat-

egories: discrete event, continuous time and hybrid

co-simulations. The standard is stated to be FMI for

continuous time co-simulations and High Level Ar-

chitecture for discrete event ones, while no standard is

yet available for hybrid co-simulation. (Arnold et al.,

2013) present an error estimation for co-simulations

based on classical Richardson extrapolation, and a

modiﬁed algorithm for a reliable communication step

size control based on an extension of the step size con-

trol of classical time integration. They conclude that

the numerical efﬁciency of co-simulation algorithms

may be improved by higher-order approximations of

subsystem inputs.

The present work discusses a comparison of dif-

ferent free-of-charge co-simulation interfaces for con-

tinuous time between Trnsys and Simulink for a case

study in thermal engineering. The main contributions

of this paper are:

• A methodology to compare different co-

simulation interfaces is proposed.

• A strong coupling co-simulation interface be-

tween Trnsys and Simulink based on Type155 is

discussed.

• The methodology and the new strong coupling in-

terface are discussed for a case study typical for

thermal engineering.

• The results serve for a qualitative evaluation and

recommendation.

1.2 Tools and Interfaces

FMI (Blochwitz et al., 2009) is a tool independent

standard that has been developed in the ITEA2 Euro-

pean Advancement project MODELISAR. FMI sup-

ports both model exchange and co-simulation of dy-

namic models using a combination of xml-ﬁles and

compiled C-code. FMI is currently supported by 95

tools and is used by various industries and universi-

ties.

Trnsys is a simulation environment for the dy-

namic simulation of thermal systems, originally writ-

ten in the Fortran programming language (Klein et al.,

1976). Trnsys Type 155 implements a direct link

with Matlab. The connection uses the Matlab en-

gine, which is launched as a separate process. The

Fortran routine communicates with the Matlab engine

through a COM interface. Type 155 can be used in

different calling modes (standard component called in

each iteration or real-time controller called only after

convergence).

BCVTB is a software environment developed

at Lawrence Berkeley National Laboratory (Wetter,

2011). It allows connecting different simulation tools

to exchange data during the time integration. BCVTB

is based on Ptolemy II, an open-source software

framework supporting experimentation with actor-

oriented design. BCVTB has interfaces to Energy-

Plus, Dymola, Functionl Mock-up Units (FMU), Mat-

lab and Simulink, Radiance, ESP-r, Trnsys and BAC-

net.

The coupling between the different tools can be

done by either loose (also known as quasi-dynamic or

ping-pong coupling) or strong coupling (also known

as fully-dynamic or onion coupling) (Trcka, 2008). In

loose coupling, the data exchange between simulators

is realized only at certain points in time. There is no

iteration between the coupled simulators. Strong cou-

pling methods iterate the values needed from other

partial systems in every time step. Generally, the

strong coupling shows higher accuracy and higher sta-

bility at the costs of a higher computational time con-

sumption (Hafner et al., 2013).

2 METHOD

2.1 System Design

Interface for

Co-Simulation

Compact thermal

energy storage (T

)

Heat exchange via

heat transfer fluid

Heat sink - one

thermal node (T

)

Trnsys

model

Simulink

model

s,out

s,in

Figure 1: The physical system to be discussed as case study

for different co-simulation interfaces. A compact thermal

energy storage is connected to a heat sink with one thermal

node via a heat transfer ﬂuid. The storage is modelled in

Trnsys, while the heat sink is modelled in Simulink.

In order to present the method and to compare

different co-simulation interfaces, we introduce a

toy example where a sorption-based compact ther-

mal energy storage is coupled thermally to a sim-

A Methodology to Compare Different Co-simulation Interfaces: A Thermal Engineering Case Study

411

ple heat sink. The corresponding system design is

shown in Figure 1. We discuss continuous time co-

simulation only, which is why discrete events like

control switches are avoided. Therefore, only dis-

charging of the storage is considered, where the sorp-

tion process releases heat, increasing the temperature

of the storage. The heat is extracted via a heat tranfer

ﬂuid to the heat sink, which is represented by a simple

body with one thermal node.

2.2 Comparison with a Reference

Simulation

The different interfaces are compared with respect to

user-friendliness and ﬂexibility, accuracy and compu-

tational costs. The user-friendliness and the ﬂexibility

is judged only on a qualitative basis.

The model is implemented also entirely in Trnsys,

referred to as “reference simulation”, employed with

improved solver parameters (time step of 0.1 sec and

solver tolerance of 10

−7

) to ensure high accuracy re-

sults. These serve for a discussion of the accuracy of

the various co-simulations. The variables communi-

cated via the co-simulation interface (inlet and out-

let temperature of the heat transfer ﬂuid) as well as

the temperatures of the heat storage and the body are

compared to the corresponding time-series results ob-

tained in the reference simulation. The maximum de-

viation is considered as measure for the accuracy.

To discuss the computational costs, a simple

batch-script is used to measure the overall simulation

time. This includes overhead like starting Matlab etc.,

but this is in most cases the relevant timing for the

user. Replica simulations serve to estimate the conﬁ-

dence interval.

3 MODEL

3.1 Heat Storage - Trnsys Model

The compact thermal energy storage is modelled in

Trnsys as depicted in Figure 2. A more detailed de-

scription of the model is found in (Engel et al., 2017),

results were presented also in (Engel et al., 2016).

The following system of ordinary differential

equations is used to model the inner states of the sorp-

tion store, i.e. store temperature T

(energy balance,

Equation (1)) and water load of the sorption material

(mass balance, Equation (2)) (Engel et al., 2017):

tot

vap,in

ads

amb

(1)

Figure 2: Model of the thermal energy storage in Trnsys

shown examplarily for the interface based on the Type155.

Type851 represents the sorption reactor, Type 852a the

evaporator/condenser, Type39 a water reservoir and Type22

and the equation block serve to calculate the vapour pres-

sure between the reactor and the evaporator/condenser. For

the reference simulation, Type155 is replaced by a Type rep-

resenting a counter-ﬂow heat exchanger with one thermal

node at the secondary side. For the FMU-export, Type155

is replaced by Type6139a and Type6139b for input and out-

put, respectively.

= k

LDF



s,equ

vap

, T

) − x



, (2)

where t denotes time, C

tot

the total (sensible) heat ca-

pacity of the sorption store, and k

LDF

the linear driv-

ing force parameter for adsorption and desorption, re-

spectively (Glueckauf, 1955). x

s,equ

= x

s,equ

vap

, T

)

is the equilibrium water load of the sorption material,

calculated for the current store temperature T

and va-

por pressure p

vap

, e.g. by the Dubinin approach (Du-

binin, 1967). The different terms on the right hand

side of Equation 1 represent the heat ﬂows for the

sorption store. The heat ﬂow via the heat exchanger

(subscript “HX”), using the one-node approximation,

i.e., constant temperature T

= const., is calculated by

= UA

∆T

log

, T

s,in

) (3)

1 − e

−UA

s,HX

˙m

HTF

p,HTF

˙m

HTF

p,HTF

s,in

− T

)

˙m

HTF

denotes the mass ﬂow of the heat transfer ﬂuid,

and c

p,HTF

its heat capacity. T

s,in

(and T

s,out

) are the

inlet (and outlet) temperatures of the sorption reactor

ﬁxed bed heat exchanger. The vapour mass ﬂow is

given by ˙m

vap

= m

The evaporation/condensation kinetics is mod-

elled linear in the driving pressure difference, result-

ing in a vapor mass ﬂow according to

˙m

vap

) =

(βA)



vap

− p

sat

)



vap

, (4)

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

412

where R

vap

denotes the gas constant of water vapor,

βA is the mass transfer coefﬁcient characterizing the

linearized kinetics, and p

sat

) is the saturation va-

por pressure for a given temperature T

. The vapour

mass ﬂow between the sorption store and the evap-

orator/condenser is ﬁnally determined using an addi-

tional iterative solver (Type22).

3.2 Heat Sink - Simulink Model

As heat sink, a simple body with one thermal node

and a counter-ﬂow heat exchanger is modelled by

˙m

HTF

p,HTF

p,b



1 − e

−UA

˙m

HTF

p,HTF



s,out

− T

)

(5)

s,in

= T

s,out

−



1 − e

−UA

˙m

HTF

p,HTF



s,out

− T

) . (6)

denotes the temperature of the body, m

its mass

and c

p,b

its heat capacity.

3.3 Co-simulation

The interface of the co-simulation is situated phys-

ically in the circuit of the heat transfer ﬂuid. Cor-

respondingly, the inlet and outlet temperatures T

s,in

and T

s,out

of the sorption reactor heat exchanger are

the variables communicated via the interface between

Trnsys and Simulink.

4 SETTINGS

Table 1: The various solver parameters are listed.

Trnsys time step 1 sec

Trnsys solver successive;

modiﬁed Euler

Trnsys relaxation factor 1

Simulink solver variable-step

autom. solver selection

BCVTB time step 1 sec

Tolerances 10

−6

(relative)

4.1 Type155 - Strong Coupling

The solver parameters used in this study are given in

Table 1. Type155 establishes a communication be-

tween Trnsys and Matlab. On the Matlab side, a

script is executed, where input and output variables

and also all Trnsys-speciﬁc solver informations (“info

array”) are communicated. In order to build a cou-

pling between Trnsys and Simulink, a Matlab-script

was developed to start and stop Simulink simulations

at each iteration to ensure a strong coupling scheme.

In this case, the Simulink’s simulation start and end

time match the current and the next time step of the

Trnsys simulation, respectively.

4.2 BCVTB with FMU - Loose Coupling

BCVTB allows to integrate simulation tools like

Trnsys and Simulink directly as “simulator”, or al-

ternatively as FMU. We considered several setups,

and present here the results for Simulink integrated

as standard simulator and Trnsys as FMU, which

was created using an open-source tool (Widl, 2015).

The corresponding simulation scheme for BCVTB is

shown in Figure 3. BCVTB provides a loose coupling

co-simulation, where all input variables are extrapo-

lated as constants from one communication point to

the next one.

Figure 3: Simulation scheme for BCVTB. The Trnsys FMU

and the Simulink simulator are represented by so-called

“actors”.

5 RESULTS

The reference results produced by the reference Trn-

sys simulation are shown in Figure 4. Figure 5 shows

the deviation of the results of the co-simulation based

on Type155 when compared to the results of the ref-

erence simulation. The deviation is fairly small at all

times, indicating a good accuracy achieved by the co-

simulation. The initial peak in the deviation is related

to the strong dynamics of the system in the initial

phase where the state of charge of the storage is still

high. The deviation diminishes towards later times,

indicating that the errors of the co-simulation inter-

face do not dangerously accumulate. Figure 6 shows

the deviation of the results of the co-simulation based

on BCVTB and FMU when compared to the results

of the reference simulation. It should be noted that

the deviation does not diminish towards later times,

indicating that the errors of the co-simulation inter-

A Methodology to Compare Different Co-simulation Interfaces: A Thermal Engineering Case Study

413

Figure 4: Results for the temperatures of the heat sink T

the heat storage T

, the outlet of the heat storage T

s,out

and

the inlet of the heat storage T

s,in

. The reaction increases the

temperature of the heat storage up to roughly 39

C, which is

in the further progress cooled through the thermal coupling

to the heat sink, until the different temperatures eventually

converge.

Figure 5: Deviation of the different temperatures from the

co-simulation based on the Type155 compared to the ones

of the reference simulation. For declaration of the variables

see Figure 4.

Figure 6: Like Figure 5, but for the interface based on

BCVTB and FMI.

face might dangerously accumulate depending on the

speciﬁc model under consideration.

A comparison of the performance of the different

co-simulation setups in terms of accuracy and com-

putational costs is shown in Table 2. In replicated

simulations, the accuracy was reproduced, while the

computational time consumed ﬂuctuates up to 10%.

The strong coupling implemented with Type155 al-

lows for high accuracy results, the maximum devia-

tion found is 0.015 K (for T

). The maximum devia-

tion found for the loose coupling implemented with

BCVTB is about 0.18 K (for T

s,in

), which is more

than a factor of 10 worse. The computional time con-

sumed, on the other hand, appears almost a factor of

100 better for the loose coupling (about 1800 seconds

for Type155 and 20 seconds for BCVTB). The size-

able computational time for the strong coupling via

Type155 is related to the fact that the Simulink simu-

lation is executed in each iteration of Trnsys using the

values at the previous time step as initial values.

Table 2: The deviation of the different co-simulation setups

compared to the reference simulation and the corresponding

computional times are listed.

Type155(strong) BCVTB(loose)

max(∆T

) 0.005 0.15

max(∆T

) 0.015 0.14

max(∆T

s,out

) 0.008 0.17

max(∆T

s,in

) 0.007 0.18

time [sec] 1840 22

6 CONCLUSIONS AND

OUTLOOK

A simpliﬁed thermal system involving a compact heat

storage modelled in Trnsys and a heat sink modelled

in Simulink has been employed to assess different co-

simulation setups which are available free of charge.

Strong coupling was implemented using the Type155

of Trnsys and a custom Matlab-script. Loose coupling

was implemented using BCVTB and FMI.

Considering the handling of the interface, the

Type155-based interface offers a lot of ﬂexibility to

the user, allowing to implement loose and strong cou-

pling co-simulation. However, sufﬁcient know-how

of the user at the Matlab-scripting level is required.

BCVTB, on the other hand, offers out-of-the-box

models for the various interfaces, while its ﬂexibil-

ity is limited. In particular, only loose coupling with

a constant extrapolation of the input variables is sup-

ported.

The accuracy of the implemented strong and loose

coupling co-simulations differ signiﬁcantly. Strong

coupling is about a factor of 10 more accurate than

loose coupling in this case study. For many appli-

cations, however, the accuracy of the loose coupling

SIMULTECH 2017 - 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

414

might sufﬁce. The computational time for the loose

coupling is almost a factor of 100 less compared to

the strong coupling scheme. The user-friendliness of

loose coupling and its capability to quickly produce

results can be expected to be prefered by many users.

We remark, however, that the communication pattern

of loose coupling schemes introduces a new kind of

uncertainty, which cannot be derived from the solver

tolerances. Hence, the corresponding inaccuracies are

usually unkown, which questions the reliability of the

loose coupling co-simulation results.

In the near future, the presented comparison will

be reﬁned. Variation of the coupled models (com-

plexity, stiffness etc.), the impact of different solver

settings and a direct coupling based on FMI will be

investigated. Error estimation based on Richardson

extrapolation according to (Arnold et al., 2013) shall

also be considered.

ACKNOWLEDGEMENTS

The authors acknowledge funding from the Aus-

trian FFG Programme Energieforschung under grant

agreement no. 845020, Research Studio Austria

no. 844732 and valuable discussions with W. Glatzl,

H. Schranzhofer and G. Lechner.

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