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Unlocking Potentials of Building Energy Systems’ Operational Efficiency: Application of Digital Twin Design for HVAC systems

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Building energy systems are complex because of their nonlinear behaviour and stochastic environmental interactions. Thus, their efficiency depends on both component design and control strategy. Aiming for high efficiencies over the whole lifecycle, we apply Product Lifecycle Management and Digital Twin Design to HVAC systems, initially using an energy recovery ventilation (ERV) simulation model. We develop a Digital Twin Prototype in Modelica of the ERV unit to predict physical system behaviour. Verifying functionality and suitability, we set up a Digital Twin Instance (DTI) by calibrating the model against measurement data of the physical twin. DTI and physical twin communicate via MQTT interfaces with each other. The approach enables DTIs to calculate predictions for different scenarios per instance, which increases systems’ efficiency significantly. In conclusion, this framework allows an application of innovative and predictive control and maintenance strategies to HVAC systems.
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Unlocking Potentials of Building Energy Systems’ Operational Efficiency: Application of
Digital Twin Design for HVAC systems
Christian Vering1, Philipp Mehrfeld1, Markus Nürenberg1, Daniel Coakly2,
Moritz Lauster1, Dirk Müller1
1Institute for Energy Efficient Buildings and Indoor Climate, Aachen, Germany
2Mitsubishi Electric, Edinburgh, Scotland
Abstract
Building energy systems are complex because of their
nonlinear behaviour and stochastic environmental
interactions. Thus, their efficiency depends on both
component design and control strategy. Aiming for high
efficiencies over the whole lifecycle, we apply Product
Lifecycle Management and Digital Twin Design to
HVAC systems, initially using an energy recovery
ventilation (ERV) simulation model.
We develop a Digital Twin Prototype in Modelica of the
ERV unit to predict physical system behaviour. Verifying
functionality and suitability, we set up a Digital Twin
Instance (DTI) by calibrating the model against
measurement data of the physical twin. DTI and physical
twin communicate via MQTT interfaces with each other.
The approach enables DTIs to calculate predictions for
different scenarios per instance, which increases systems’
efficiency significantly. In conclusion, this framework
allows an application of innovative and predictive control
and maintenance strategies to HVAC systems.
Introduction
Digitalization of energy systems enables the application
of sophisticated control strategies increasing operational
efficiencies significantly (Afram, 2014). Unlocking the
entire potential, there is an urgent need to design systems
carefully by simultaneous consideration of functional
requirements and control strategies with digital interfaces
in early development stages (Dongellini 2017; Poppi,
2016). However, integral descriptions that face these
concepts for HVAC systems do not exist, yet. On the one
hand, an integral product development approach
regarding the whole lifecycle is required. We propose
Product Lifecycle Management to be suitable (Stark,
2015). On the other hand, the digital twin approach has
proven great suitability for complex system designs and
their operation (Grieves, 2017). A combination of both
concepts for HVAC Systems has currently not been found
in literature.
Building energy systems are complex because of their
nonlinear behaviour and stochastic environmental
interactions (Staffel, 2012). Furthermore, HVAC systems
make up the majority of commercial building energy
consumption, and are directly responsible for maintaining
the health and wellbeing of occupants in Germany
(Arbeitsgemeinschaft Energiebilanzen e.V., 2017).
However, their efficiency strongly depends on both
component design and control strategy (Afram, 2014).
Aiming for high efficiencies, we apply systematically
Digital Twin Design (DTD) to HVAC systems, initially
using a detailed model of an energy recovery ventilation
(ERV) unit.
Thereby, this work contributes to the application of the
digital twin approach to HVAC systems by example of an
energy recovery ventilation unit considering Product
Lifecycle Management:
We combine the approaches of Product Lifecycle
Management and Digital Twin Design (DTD) to
demonstrate its potential for the use in realistic,
manufacturing applications in Chapter 2.
In Chapter 3, we show the modelling approach of the
ERV unit in Modelica and a room model to apply it
to a use case. We calibrate the ERV model using
measurement data.
A use case of predictive maintenance is evaluated in
Chapter 4 were we evaluate different energy
consumptions due to different maintenance
strategies.
Finally, we summarize and conclude our findings.
Digital Twins in Product Lifecycle
Management
Product Lifecycle Management (PLM) is a production
engineering approach for the holistic control and
administration of product-related information along the
entire products’ lifecycle. Methods, models and tools
(especially IT systems) support the product development
process and the product usage phase in order to increase
productivity and efficiency. PLM integrates all data and
information collected along the product lifecycle. (Stark,
2015)
The first use of the term in literature dates back to the
beginning of the 21st century. A uniform definition of the
exact components that a PLM system must present in
order to be declared as such does not exist. Previous
applications refer mainly to physical products and
processes (Tao, 2018). In the context of this work, PLM
is to be additionally implemented for virtual models in the
context of digital twins. (Stark, 2015)
PLM distinguishes between fife phases of products’
lifecycle. In the planning phase, a product is defined on
the basis of customer requirements, market analyses,
innovative ideas and knowledge from former products. A
preliminary design is carried out in which technical
parameters and functions are defined. These are
concretized in the create-phase by first models or
prototypes. In the build phase, production is finally
started based on the defined product design. The
following use of the product is described in the sustain
phase. This also includes services such as maintenance
and repair. Ultimately, the disposal phase after the end of
use includes processes for disposal or recycling. (Yoga
Mule, 2012)
At the moment, there is no standardized definition of what
exactly a digital twin is. A widespread understanding
describes the term digital twin as an intelligent, digital
image of a real product or process. Against this
background, a separate approach to the implementation of
a digital twin will be developed within the presented
framework. The first approaches to the implementation of
a digital twin come from the field of aeronautics and space
technology. In this context, NASA in particular should be
mentioned as a "pioneer" in the development and use of
digital twins. Their intention to use digital twins is to
monitor the functional condition of an aircraft or space
shuttles as an overall system as well as individual
components. (Schleich 2017; Tuegel, 2011)
NASA defines a digital twin e.g. as follows: "A Digital
Twin is an integrated multiphysical simulation of a
vehicle or system actually built, using the best available
physical models, sensor technologies, fleet history, etc.,
to reflect the life of the corresponding flying twin
(Schleich, 2017). Grieves, who worked in an advisory
capacity for NASA, is considered the founder of the term
digital twin, which he describes in the context of PLM
(Grieves, 2014). It defines a digital twin as "a set of virtual
information that completely describes a potential or
actually manufactured physical product from the micro
atomic plane to the macro geometric plane". (Grieves,
2017)
Combining both approaches, PLM and digital twins, and
applying them to HVAC systems, we propose Digital
Twin Design.
Digital Twin Design has PLMs’ fife phases. These phases
are combined with digital twin definitions. Regarding
(Grieves, 2017), we combine three PLM phases with
existing digital twin definitions:
Create phase Digital Twin Prototype (DTP)
Build phase Digital Twin Instance (DTI)
Sustain phase Digital Twin Aggregate (DTA)
In addition, we propose two further Digital Twins to be
on PLM phases:
Dispose phase Digital Twin Knowledge (DTK)
Plan phase Digital Twin Concept (DTC)
The first step in product development is the creation of a
Digital Twin Concept (DTC). The conception takes place
in the PLM plan phase. It comprises the entire planning
and design of a concept for the implementation of a digital
twin for HVAC systems. Based on customer needs,
market analyses and individual ideas, goals are derived
which are pursued by the implementation of a digital twin.
Once these objectives have been defined, a requirements
analysis can be carried out to define product
characteristics, functions and operating strategies.
Besides former typical requirements such as physical
concepts, in this phase requirements for the virtual model
need to be set up.
Figure 1: Applying Product Lifecycle Management and
digital twins to Digital Twin Design for HVAC systems.
The first requirement of the virtual model is the definition
of model’s inputs and outputs. Applied to HVAC systems,
typical inputs are measurement data points like volume
flows, temperatures and pressure levels. Based on this
inputs our model needs to predict outputs. Typically these
are power consumptions e.g. of fans, pumps or heaters.
If inputs and outputs are defined, a second requirement
needs to be met. Regarding the application it has to be
defined, which level of accuracy the model should has. In
general, we can distinguish between
black box,
grey box and
white box model.
Black box models are mathematical models, whereas grey
box models use physical equations and some simplifying
assumptions to represent physical system behaviour.
White box models are a perfect description of the physical
system. With an increasing level of detail, calculation
durations of model predictions increase as well.
Consequently, the trade-off between accuracy and
simulation speed needs to be clarified in an early design
stage. (Mueller, 2016)
Creating the Digital Twin Prototype (DTP) in the PLM
create phase, a simulation model of the HVAC system
must be created. In addition to modelling, the subsequent
parameterization of the model is also carried out in the
DTP phase. In our use case of an ERV system, we need
simulation models of heat exchangers, ventilators, air
filters, heater, cooler, temperature control, sources, sinks
and the target room. Therefore, we use Modelica because
of both modelling of dynamic systems as well as a high
degree of modularity and reusability.
After modelling all components, a parametrisations needs
to be done. For this purpose, safe, known parameters are
transferred into the model. In particular, geometric
boundary conditions must be taken into account, such as
heat-transferring surfaces, wall thicknesses, room
dimensions, etc. The materials used for the individual
components are also among the parameters known in
many applications.
Assumptions can be made for parameters that are not
known or are subject to uncertainties. However, it must be
borne in mind that this creates uncertainties in the overall
system, which can lead to a deviation between simulation
and measured values. Due to uncertain parameters, a
subsequent calibration is of enormous importance when
modelling real systems. With the completion of the
simulation model, the prototype of the digital twin exists.
The mentioned calibration of the simulation model is
intended as a transition to the build phase according to
DTD.
The creation of a Digital Twin Instance (DTI) in the PLM
build phase requires a calibrated simulation model and a
communication structure between physical and digital
twins. The calibration of the simulation model serves to
map selected output variables of the real system through
the simulation. Any deviations between measured values
and simulation results should be as small as defined in the
planning phase.
Several methods exist for the calibration of HVAC
systems. Bayesian calibration has proven to be suitable
for dynamic systems. This is mainly due to the good
predictability of output variables, so that there is a high
probability that measured values will be well represented
by the simulation after calibration (Li, 2017).
The disadvantages of the high complexity and numerous
required simulations are accepted. There is a promising
approach in the literature for the implementation of
Bayesian calibration (Chong, 2018):
1) Generate/procure measurement data
2) Sensitivity analysis/parameter testing
3) Generate simulation results
4) Combining measurement data and simulation results in
Gaussian process
5) Distribution determination (Markov Chain Monte
Carlo method)
6) Check convergence and assess the accuracy of the
calibrated model.
In the course of the development of the DTD approach, a
(partially) automated implementation of this
methodology for digital twins is considered to be
promising. A practical implementation for the simulation
model of the ventilation system has not yet taken place.
The existing data basis was not sufficient in this respect.
Instead, a manual calibration of the model is carried out.
The target values of the model and therefore for
calibration are power consumption of the fan (Pel) on the
one hand and the effectiveness of the heat transfer in the
cross-flow heat exchanger () on the other hand.
Measured values from the manufacturer were available in
this respect. For several operating points, different
volume flows per stage and related power consumptions
are available. The calibration was conducted fitting the
heat transition coefficient of supply air, heat exchanger
material and exhaust air in order to match the both target
values.
The Root Mean Square Error (RMSE) is used as the
criterion for assessing the quality of the calibration. The
RMSE is calculated as follows (Mehdiyev, 2016):
 
. (1)
First, simulation results  are generated with the
non-calibrated model. By comparison with
measured  values, a deviation for the target values
can be determined. The lower the RMSE the better a
calibration was conducted. With this calibrated model,
Digital Twin Design is ready to use.
In order to exploit the benefit of a Digital Twin Design,
digital and physical twins are constantly exchanging data.
Ensuring this requirement, an infrastructure is required on
which communication can take place. In the following,
we will show how such a communication interface can be
implemented to enable data transfer between digital and
physical instances as well as data storage in a cloud.
Based on the results of extensive literature research,
MQTT (Message Queuing Telemetry Transport) has
proven to be suitable for implementing communication
structures for IoT applications. (HiveMQ, 2019)
Notably, low demands on the digital infrastructure, such
as network and bandwidth, make the almost universal use
of the digital twin possible. In addition, an enormous
number of end devices can participate in this
communication structure. The intention of executing
several instances of the digital twin in parallel, MQTT has
many advantages. Hirmer et al. (Hirmer, 2016) present an
approach for the implementation of an architecture for
automated communication for IoT environments. The
easily scalable communication protocol MQTT is used.
Hence, the access to sensor data of physical systems is
realized. Communication with digital instances takes
place on a publish/subscribe architecture. Haag and
Anderl (Haag, 2018) describe the implementation of a
digital twin based on an MQTT architecture.
In addition to the communication between the physical
and digital instance, a link is provided to display data and
results in real time via a graphical user interface. Based
on this implementation, potentials with regard to
monitoring and the resulting ease of use for users can be
derived.
The MQTT-Broker as central instance for communication
manages the data transfer between physical and digital
instances as well as the storage of data in a cloud. All
subscribers (terminals) send requests to the MQTT broker
using the commands publish and subscribe, which ensures
their implementation. Data is made available by publish,
while data is requested by subscribe.
Meeting requirements of communication services, the
Quality of Service (QoS) level should be at least 1, as this
ensures that data arrives. In order to ensure that the data
arrive exactly once, QoS level 2 must be selected, which
however results in increased data traffic due to increased
communication. If the resource requirement plays a minor
role due to increased data traffic, this disadvantage can be
ignored and level 2 can be implemented. (HiveQM, 2019)
With regard to communication with the physical instance,
an approach to implementation has been developed as part
of further work. A transfer of the developed approach to
the DTD is planned, but must still be tested in practice. To
implement communication on the part of the digital
instances, an MQTT-Dymola interface is currently under
development at the Institute for Energy Efficient
Buildings and Indoor Climate (EBC) at RWTH Aachen
University. This is to serve as an interface to read
simulation results from Dymola (Modelica) and to
introduce parameters into Dymola.
The implementation of the communication interface
completes the build phase. A functional instance of the
Digital Twin (DTI) is available. In the following, the
developed DTD approach provides for the aggregation
and execution of several instances the sustain phase.
The simultaneous execution of several DTIs is necessary
to achieve some of the objectives of the DTC. In the PLM
sustain phase several DTIs are aggregated to Digital Twin
Aggregate (DTA). To enable simultaneous, stable
execution of the aggregates, a suitable software
environment is required. In this respect, known
approaches were examined within the scope of this work
and a theoretical implementation possibility was
integrated into the DTD approach developed.
Virtual machines are a well-known advocate for creating
an environment in which software can be run in isolation.
Due to some disadvantages of virtual machines,
especially in the areas of efficiency and performance,
alternatives have gained in importance in recent years. An
example of this is Docker (Bauer, 2018). Docker uses
containers to create an environment in which software can
be run in isolation. Hence, Docker is suitable for the
implementation of the DTD approach in the DTA phase
due to simple possibilities for instantiating processes.
Individual instances in the Docker containers can simulate
different scenarios. These can vary in many ways.
Various weather data are just as conceivable as simple
parameter variations. These include, for example, a
decreasing mass flow due to leakages or additional
internal heat gains due to deviating user interaction. This
corresponds to carrying out various case studies. In
addition, more extensive instances can also be executed,
such as complete annual simulations. As mentioned in the
description of the DTC phase, the implementation of the
DTD approach pursues several objectives like operational
optimization or predictive maintenance.
On the one hand, the digital twin can be used to optimize
the operation of the physical Twin. Different scenarios of
the DTIs are of central importance here. Due to the
simulations of the DTIs carried out parallel to the real
operation, simulation results are available for various
parameters under the currently prevailing boundary
conditions. Based on these results, the digital twin can
determine the optimal scenario for the operation and
transfer the parameters used to the real system via the
developed communication structure. The implementation
of the ERV system looks as follows:
Parallel to the operation of the real system, several
instances are executed that simulate different scenarios.
An instance recreates the current operation of the real
system. The other instances vary single or multiple
parameters, such as air mass flow or pressure drop due to
the use of a new air filter. Different scenarios lead to the
fact that the instances deliver different simulation results.
Within the framework of operational optimisation, the
efficiency of the operation can be determined based on
predefined assessment variables (e.g. consumption of
electrical energy).
Through the communicated comparison between the
individual instances, the most efficient operating point
can be determined. The instance that simulated it,
transfers the operating parameters to the real system in
real time, so that it is executed at the optimum operating
point. The required valuation parameters can be very
complex in this context. Accordingly, the realization of a
digital twin for operational optimization is a complex
process. Nevertheless, the idea in connection with
resource-saving energy supply in the HVAC sector seems
to be promising for the creation of more efficient systems.
Another possible use of the digital twin is predictive
maintenance. This is closely related to the ability of the
digital twin to monitor the operation of the real system.
The digital twin as a virtual image of the real system has
the current operating data of the system at its disposal.
This makes it possible to monitor the functionality and
ensure operation in real time. Based on the evaluation of
current operating data, the digital twin can draw
predictive attention to maintenance and repair measures.
Problems can thus be recognised in advance and ideally
solved before they become acute.
By implementing predictive maintenance, maintenance
routines can be planned and downtimes are avoided
consequently. Both technical and personnel resources can
thus be used in an optimised manner. As a result, an
increase in the life cycle efficiency of products is
achieved. (Schreiner, 2018)
The prerequisite for implementing predictive
maintenance is simulation models that provide results in
real time and predictively. In addition, the simulation
models must have the ability to predict malfunctions
based on characteristic values. (Schreiner, 2018)
Large amounts of data are generated during the entire
sustain phase of the digital twin. Following the DTD, all
operating data of the digital twin are stored. After the end
of the use in the DTA phase, a data collection is available
which is finally used in the last phase of the life cycle.
The last step of the product life cycle (PLM dispose
phase) is the disposal of the product. Since the digital twin
has processed, generated and stored an amount of data and
information in the course of its life cycle, Digital Twin
Knowledge (DTK) phase provides evaluation and
utilization of the data. In the following, some ideas are
shown, how and for which purpose the evaluation of the
data generated and stored over the entire life cycle can
take place. During the operation of the Digital Twin
Aggregate, generated operational data is stored in a cloud
via the implemented communication structure. The data
thus generated over the entire life cycle can be analysed
after active use in the DTK phase. Various software
fundamentals are required for the entire data evaluation
process. (Meier, 2018)
At its core is an architecture that provides an environment
for cloud computing a database that can manage
generated time series data from simulations and sensor
readings, and a user interface that enables real-time
monitoring of operational data. Such an architecture
ensures that data generated over the lifecycle of the digital
twin can be meaningfully stored, managed and used.
PLM is a closed loop process. After the last phase, the
cycle starts again from the beginning. In the DTD, the
entire available data basis is consequently transferred to a
new life cycle via the evaluation step. This integration
takes place in the form of information, experience and
knowledge. This step accounts for a large part of the
added value of using a digital twin. Based on the
transferred information, a second generation of the real
product or the corresponding digital twin can be
produced, for example. With the help of evaluation as part
of the DTK phase, both positive and negative
characteristics of the first generation can be evaluated.
The aim for the second generation should be to eliminate
negative aspects and to strengthen positive aspects.
The DTK phase thus leads to improved products and
associated digital twins. To illustrate the DTD, an ERV
system will be evaluated using dynamic simulation
models, in order to be able to simulate various scenarios
for the operation and thus demonstrate the applicability of
the DTD. Ultimately, this approach is intended to identify
previously hidden potentials with regard to energy and
resource efficiency in the life cycle of HVAC systems.
Modelling and Calibration
Within this work, DTD is applied to an ERV system in
order to evaluate its potential of predictive maintenance.
Therefore, dynamic simulation models are necessary.
These are developed in Modelica and Dymola as
compiler. The ERV system is shown in Figure 2.
Figure 2: ERV system in Modelica consisting of a
ventilator, cross flow heat exchanger, filter, heater,
cooler and a room model.
The core component of the ventilation system is the heat
exchanger. This is designed as a cross-flow heat
exchanger. To create the simulation model for the
ventilation system, a heat transfer model is integrated for
cross-flow design. According to the Association of
German Engineers (VDI), the following equation applies
to the effectiveness of heat transfer in cross-flow heat
exchanger with fluid mixed on both sides (VDI, 2013):



 (2)
This effectiveness calculation  is implemented as a
function in Modelica using the number of transfer units
(NTU) and heat capacity flow R. In addition, the
manufacturer's instructions state that the heat-transferring
surface is made of paper. In this respect, a new record is
implemented for paper, which converts the material
properties into Modelica.
An existing room from the Modelica Buildings model
library is used as the room model (Wetter, 2019). This
model offers various possibilities for parameterization.
These include: geometric dimensions of the room wall,
floor and ceiling materials external and internal heat gains
port for reading in weather data. The current room
temperature is implemented by means of an energy
balance over the room.
The filter is modelled as pressure drop  (in Pa) between
inlet and outlet of the filter. Assuming particles to be in
the air, the filter is getting defiled over time by dust feed.
Hence, it pressure drop increases time-dependently
described as follows (Trox, 2012)
     . (3)
For the other components simple energy and mass
balances are introduced to describe the physical behaviour
of the system.
Regarding the calibration of the system, five ventilation
stages are used. In order to meet these target values for all
five ventilation stages and the associated volume flows, a
third-degree polynomial is stored, which takes into
account the volume flow dependence of the pressure
losses in the model. This results in an acceptable
calibration quality of 0.861 W based on a determined
RMSE. Table 1 summarises the associated electrical
performances.
Table 1: Calibration of electric output power.
Stage
Pel,sim
[W]
Pel,meas
[W]
Pel
[W]
1
163
165
-2
2
91
90
1
3
42
41
1
4
21
22
1
5
15
14
1
In the second step, the effectiveness of the heat transfer is
calibrated against the measured values given for the five
ventilation stages. As before, the generated simulation
results are compared with the manufacturer data. It can be
stated that the effectiveness of the simulation is
underestimated. This is due to the calculation of (VDI,
2013). Real efficiencies are therefore better than the
calculated ones. For the model, the effectiveness must be
corrected upwards in the course of calibration. Based on
the individual deviations between simulated and
measured efficiencies, a mean deviation is determined,
with which the correction of the calculation is made in the
model. This allows a quality of 0.021 to be achieved on
the basis of the RMSE. Table 2 shows the associated
efficiencies.
Table 2: Calibration of effectiveness.



0.921
0.9
0.021
0.904
0.880
0.024
0.858
0.855
0.003
0.797
0.815
-0.018
0.746
0.775
-0.029
In all cases, we have a well modelled and calibrated
Digital Twin Instance that can be studied regarding
predictive maintenance.
Predictive Maintenance of Filter systems
As shown in Figure 2, the ventilation system has a filter
to cut off particles from the air that is delivered in the
room. During operation, the filter is filled with particles.
The pressure drop increases. To ensure the adjusted mass
flow, the ventilation systems’ power consumption
increases.
A typical maintenance action would be to exchanger the
filter. Using DTD we study different schedules to find the
best time for an exchange. During operation, we consider
four different scenarios for annual simulations. Herein,
different criterions must be met for an exchange:
1) Exchange every year (Baseline scenario)
2) Exchange at 150 Pa pressure drop
3) Exchange at 200 Pa pressure drop
4) Exchange every 2.5 years
Assuming a price for electrical power of 29.5 ct/kWh and
55 € per exchanger, we calculate for a lifecycle of ten
years total operation costs and energy consumption.
Referred to the baseline scenario, the results are shown in
Figure 3. (Farr, 2006; Statistische Bundesamt, 2018)
Exchanging the filter once a year, the lowest electric
consumption is archived with 3827 kWh. The total costs
are 1129 €.
However we calculated the lowest electric energy
consumption in the baseline scenario, it is not the scenario
with lowest total costs. Scenario 2 and 3 show lower total
costs by about 5 to 6 % savings. Simultaneously, the
energy consumption increases by about 6 to 10 %. Both
the highest electric consumption as well as the highest
total costs is archived with scenario 4. Here, total costs
increase by about 1 % and electric energy consumption
increases about 30 %.
Figure 3: Total costs and electric energy consumption of
four different Digital Twin Aggregates.
These fictive scenarios indicate the potential of DTD. If
we can provide the digital twin with measurement data
and apply fault detection, filter exchanges can be
predicted and both total costs and electric consumption
can be decreased significantly.
Using these results, Digital Twin Design shows potential
for the whole systems’ lifecycle efficiency. Applying this
to other, more sophisticated control and maintenance
scenarios higher savings can be reached, respectively.
Hence, we showed that DTD, consisting of PLM and
digital twins, was successfully applied to HVAV systems
and the lifecycle efficiency could be increased due to
predictive maintenance.
Conclusion
This work shows that Digital Twin Design can be applied
to HVAC systems using Product Lifecycle Management
and digital twins for life cycle analysis. On the basis of
five life cycle phases, Digital Twin Concept, Digital Twin
Prototype, Digital Twin Instance, Digital Twin Aggregate
and Digital Twin Knowledge, the presented approach
serves as a detailed framework for the implementation of
a digital twin to a whole product lifecycle.
To demonstrate the feasibility of the developed approach,
a grey box model of a ventilation system was created and
manually calibrated. In addition, a communication
structure based on MQTT was presented.
The potential of predictive maintenance with various
routines with regard to air filter replacement for the
ventilation system was successfully demonstrated. These
are based on the existence of an optimum of energy
consumption and total costs. Savings of total costs up to
10 % are predicted using filter exchanges at a pressure
loss of 200 Pa. By integrating a digital twin for HVAC
systems, it is therefore possible to increase lifecycle
efficiency in terms of both energy and total costs.
0 2000 4000 6000
2,5 years
200 Pa
150 Pa
1 year
Total costs [€] Energy consumption [kWh]
Due to the complexity and interdisciplinary of the topic of
digital twin, not all phases of the DTD approach could be
tested for applicability and practicability in this work.
Consequently, there is a need for continued research
work, particularly in the areas of machine communication
and software technology.
Overall, the full potential of digital twins is not exhausted,
yet. Rather, the technology is still in its infancy. In the
future, further developments in and with digital twins are
to be expected. Their use is highly likely to multiply in the
coming years, as the basic infrastructure for
implementation is already in place.
In addition, some interesting approaches and ideas for the
use of digital twins have already emerged. Only the
implementation in practice is still open in many cases. It
will also be exciting to observe the extent to which new
business models with digital twins will occur. The market
for applications with digital twins is, in any case, almost
endlessly large in all sectors as digitalization progresses.
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... Operational optimization has proven to be promising for existing as well as new systems. One possibility to implement operation optimizations are digital twins (Vering, 2019). Thus, digital twins gain increasing interest in research and industry since recent years (Bauer, 2020). ...
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