A Virtual Engine Laboratory for Teaching Powertrain
Engineering
Burke, R.D.1, De Jonge, N.1, Avola, C.1, Forte, B.2
1. Powertrain and Vehicle Research Centre, Dept. Mechanical Engineering, University of Bath, Bath, UK
2. Dept. Electrical Engineering, University of Bath, Bath, UK
Abstract
A virtual engine laboratory application for use in automotive engineering education is proposed to allow
the practical teaching of powertrain calibration. The laboratory software is built as a flexible Matlab tool
that can easily be transferred for applications in other disciplines and promotes the link between
teaching and research.
Keywords: Virtual Laboratory, Automotive Engineering, Matlab, Design of Experiments, Diesel Engines
1 Introduction
Engineering education needs to incorporate many practical aspects that are key to the profession [1]. In
early years of engineering education, laboratory sessions can be simple to demonstrate the basic
physical principles. Small and inexpensive apparatus can be used to illustrate basic principles in in fields
such as thermodynamics, mechanics, or fluid mechanics. In later years of engineering education, for
example at master’s level, the taught concepts are more complex. The practical application of these
concepts requires larger, more sophisticated, and often expensive laboratories. A teaching example in
automotive engineering is the topic of engine controller calibration. This skill requires engineers to
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optimise the parameters of a control algorithm based on experimental data measured from an engine or
vehicle test facility. However, the use of such a test facility for education purposes is prohibitively
expensive and impractical for most universities. The result is that the education resorts to class based
activities which fail to stimulate higher levels of learning and, in the worst cases, only encourage
memorizing without promoting understanding [2].
The aim of this paper is to create a virtual laboratory application for use in automotive engineering
education and demonstrate how it can be used to improve teaching of the subject of engine calibration.
2 Background
2.1 Learning objectives in powertrain calibration
The topic of powertrain calibration is an example of engineering practice in the field of automotive
engineering. The task requires the use of design of experiments (DoE), experimental data capture and
processing, mathematical regression modelling, and optimisation techniques [3-5]. Students
participating in this course have a background in basic control theory, but for many this will represent
the first time they are exposed to its application to a real system. This application could be considered a
threshold concept that is difficult to teach without practical experience [6].
The motivation for implementing the virtual engine test laboratory stems from a historical analysis of
assessment formats and student feedback for a master’s level course. This course format was fully
lecture-based with the contents composed primarily of definitions and paper based worked examples.
Review of the course highlighted an emphasis on “remembering” as the major learning activities. This is
located in the knowledge or remembering level of Bloom’s Taxonomy and crucially misaligned with the
intended learning outcomes (ILOs) of this master’s level course which requires students to “analyse”.
With this format, it is not possible to achieve the higher levels of learning without allowing the students
put theory into practice [7, 8]. In fact, analysis of the preceding 5 years’ examination showed that 60-
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80% of marks were awarded for simply describing aspects introduced within the lecture material . To
create an environment where students can do this, the activities and assessments need to be aligned
with the application of the methods.
Ideally, each student would be able to apply the engineering theory on a real engine test facility,
spending many hours practicing developing their understanding. However, the cost of such facilities as
well as all the overhead knowledge in running a full powertrain test facility are prohibitive to this option.
A virtual laboratory approach was therefore chosen.
2.2 Review of virtual laboratories
Using virtual laboratories has been shown to be effective in all but the youngest of learners [9]. The key
shortfall is that some concepts need to be experienced to be fully accepted and understood. A good
example of this is the boiling of water at temperature below 100oC at lower pressures.
Studies of virtual laboratories have shown that if the experience is sufficiently realistic then the benefit
is like that of the equivalent real laboratory [7]. This is particularly the case if the virtual laboratory can
provide sufficient levels of realism [8] and avoid deterring students through unfriendly programming
environments [6].
Three categories of virtual laboratories can be found in the literature:
1. Virtual reality laboratories which emulate part or all the laboratory environment. These tools
can be used alone or in combination with real laboratory sessions and examples include the
LabSkills chemistry e-learning tools [1] and a geology based laboratory from the University of
Arizona [9].
2. Laboratories where the session is conducted using only part of the experimental equipment
with a computer simulation providing the rest. This approach is still conducted in a laboratory
setting, but reduces the overall equipment costs. Examples from the literature include an engine
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calibration lab at the University of Bradford [10] and a cruise control lab at the University of
Michigan [11]. Although these examples can be classified as virtual laboratories, they still
require a dedicated laboratory space and equipment which restricts student access to the
learning environment. Such laboratories can also be adapted into remote laboratories, where
the students can operate the equipment remotely via internet link.
3. Fully software based virtual labs which provide a computer based interaction with a simulation
model. Racing Academy [12] is an example of such a lab currently widely used, but is
constructed as a game and therefore does not give students the laboratory feel. A gas turbine
example can also be found in the literature [13].
The laboratory types 1 and 3 above have the advantage of being software based and as such have the
opportunity to be changed into fully online activities, subject to the computer overhead that they
require.
There are few examples of virtual engine laboratories in the literature, most probably because the
creation of the engine models required for these are themselves a topic of research or commercial tools
[14, 15]. However, the topic is gaining popularity and universities are needing to respond to a demand
from industry for expertise in this area [16].
The virtual laboratory from the University of Bradford [10] is a semi-virtual lab and still takes place in a
laboratory environment. A real engine controller is used, but linked to a specialist computer which hosts
the real-time engine model. The laboratory in fact represents some real installations at automotive
manufacturers who use hardware in the loop approaches to develop their control strategies [17]. The
advantage here is that the students are still in a laboratory environment and have to engage with some
degree of real hardware. However, the downside is that as a laboratory facility is still required and
student access is consequently limited.
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2.3 The suitability of a virtual laboratory for powertrain engineering
The experimental aspects of powertrain calibration are typically conducted on an engine test facility.
The test facility itself comprises of a test cell which includes the engine linked to a host computer system
that drives the test cell and records the data (see Figure 1). When the test cell is operational, the
engineer’s role is primarily interacting with the computer screen to set the operating conditions
according to a test plan and record data. On most facilities, there are safety systems in place that will
shut down the facility in the case of dangerous running conditions.
Figure 1: Typical layout of an internal combustion engine laboratory facility
In this work, it is recognised that there is an opportunity to recreate the engineer’s experience (the host
system interface) without the need for a real engine test facility. By replicating the computer based
user-interface and linking it to a mathematical model of the engine, the engineering tasks can be
performed with no cost on any computer. This will enable significant student access to such facilities to
put powertrain calibration theory into practice. The on-demand availability of a virtual laboratory will
encourage both independent and peer-supported learning [10, 13, 18]. Laboratory sessions will no
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longer be constrained to set timetabled periods and locations allowing both on- and off-campus
learning. The computer models required for this configuration are readily available within the research
groups providing the teaching and therefore this approach also creates a natural exchange platform
between research and teaching.
It is further recognised that this configuration of test facility host system is not unique to engine test
facilities. The user interface could be created in a flexible way to allow it to be applied to other
applications across different disciplines.
This work therefore aims to create a generic user interface that can be linked to mathematical models of
engineering and science systems to provide students with the experience of operating sophisticated
experimental equipment on any desktop computer.
3 Virtual lab description
3.1 Real engine laboratories
Test facilities are common in industry and universities to evaluate the performance of engine systems.
They are designed to measure the behaviour of the engine without the need for a full vehicle. This gives
more control over the testing but also allows engines to be developed concurrently with the vehicle.
A typical test facility is shown in Figure 2. The engine is used without the gearbox, drivetrain or vehicle
and its output shaft instead drives and dynamometer (motor/generator). The dynamometer is used to
brake the engine and thus replicate the resistance friction and inertia forces of a vehicle. The
dynamometer can be controlled to maintain a target rotational speed and will absorb or provide power
to maintain that speed. The amount of power the motor needs to absorb depends on how hard the
engine is working which is adjusted by actuating the accelerator pedal (just like when the engine is in a
car).
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The test rig is linked to a computer system known as the host system which controls the dynamometer
speed and accelerator pedal, but also:
- Controls the cooling fans and cooling water flows,
- Records data from instrumentation installed on the engine,
- Communicates with the engine’s controller to modify set-points such as the timing of
combustion, the opening of exhaust gas recirculation valves and the operating of the
turbocharger.
Figure 2: Engine test cell layout
It is the host system that is of key interest for the virtual laboratory as this is the interface between the
engineer and the test rig. The host system displays a graphical user interface (GUI) comprising the
following elements (an example is shown in Figure 3):
- Buttons to switch test bed systems on/off (dynamometer, fuel supply, cooling fans…);
- A live stream of measured values from the various sensors;
- Dials and gauges to monitor key engine operating conditions;
- Oscilloscopes for observing time history of selected data channels;
- Features for logging data to a data file;
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- Alarms that alert the user to certain conditions of the test cell (such as engine too hot…).
Figure 3: Typical user interface for the host system
3.2 Interface construction
The Mathworks Matlab was chosen as the environment to create the user interface. This was selected
because it is a universal tool used across disciplines and widely available within universities. In addition,
many of the graphical components already exist within Matlab such as buttons, graphs, and data
storage. Matlab is also a common environment for computational models of systems used for research
which are another key input to the virtual laboratory. By hosting the user interface in Matlab, this will
ease the linkage to the models. Finally, this will encourage students to engage with this universal tool to
develop their coding abilities and to make contributions to virtual lab.
To facilitate the use of the tool across disciplines, the user interface has been built as a library of
software components that can easily be arranged by an intermediate-level programmer to create new
interfaces for future virtual laboratory applications. The structure of the virtual laboratory environment
is illustrated in Figure 4 which has been constructed to promote future uses. The base interface
components are stored and documented as programming objects that can easily be personalised for
future applications. The actual application of the virtual engine laboratory is stored as a case study and
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application example to inspire future users. The core user interface and application were created by two
mechanical engineering undergraduate students with an interest in computer programming.
Figure 4: Programming Structure of the Virtual Laboratory Environment
Figure 5 shows some example screen from the user interface that have been designed to mimic the
screens from the test cell interface shown in Figure 3. The user interface interacts with the engine
simulation model which has minimal modifications compared to the research version. In fact, the model
will run without the user interface, allowing it to be updated independently to provide future features.
The model and its interaction with the GUI will be detailed in the following section.
The user interface exists as a script that the students must run in Matlab. Specific guidance for installing
and launching the script and once activated, the student work only with the GUI. In this way, the code is
openly available for students to explore without deterring student who have less interest in computer
programming.
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Cell Services Page Engine Control input page
Measurements page Oscilloscope page
Figure 5: Screenshots from the virtual laboratory
3.3 Engine Model
The engine model is issued from a number of sub-models which originate from research activities. The
full engine model is a combination of physics based and empirical models that capture different aspects
of engine operation. For the virtual laboratory application, the final choice of model type for each
component was a compromise between:
- Model availability: it must be available for open distribution to students and not be protected by
commercial restrictions. The model must also be able to run without costly software licenses on
all computers to allow full and unlimited access to the tool;
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- Computational effort: the model must be able to run faster than real-time on a standard
desktop computer;
- Accuracy: the absolute accuracy is of less importance than the model exhibiting correct trends.
This ensures that the model maintains a good level of realism and allows students to explore
topics taught across the automotive engineering degree, such as combustion effects.
An overview of the engine model is shown in Figure 6. This consists of the following sub-models which
have been combined mathematically to simulation the complete engine:
1. A semi-physical model of the turbocharger [19],
2. A mean value engine model describing the flow of air, burning of fuel and creation of torque in
the engine cylinders [20]. The mean value model is built as neural networks fitted to data issued
from a 1D gas dynamics model of the engine,
3. Dynamic polynomial or neural network models of the emissions formation in the cylinder [21,
22],
4. Physical models off the intake and exhaust manifolds as single control volumes [23].
5. Simple heat loss model of the intercooler.
Figure 6 also highlights the information that is exchanged between the different sub-models. The engine
model required simplification to reduce the calculation times such that is ran fast enough on a standard
desktop machine. This is a vital requirement for the students to have the perception of running a real
laboratory. The run time was improved by replacing differential equations describing the engine
operation for every degree of engine crank revolution with look-up tables describing the average
behaviour over two full revolutions. These look-up tables were constructed as neural networks. The
neural networks were fitted to data from a higher order mathematical model which was too slow for
this application and required specialist software licensing.
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Figure 6: Outline of the engine model showing the main parts.
The engine model uses look up tables to predict the mass flow in and out of the cylinders (meng ¿ based
on the intake pressure (P3) and temperature (T 3), exhaust pressure (P4), and engine rotational speed (
N eng). The engine model also predicts the exhaust temperature (T 4), torque and emissions based on the
mass flow and the combustion settings provided from the user interface. The emissions look-up
functions were derived and combined from a range of different engine models:
- Pilot combustion model from results presented by Tanka et al. [24],
- Soot model from Grahn et al. [22],
- Diesel injector characteristics from Dowell [25],
- NOx model in low speed/torque region [23] and high speed region [24].
The turbocharger model is composed of a compressor map, turbine map and shaft model. The shaft
model calculates the rotational speed of the turbocharger based on a power balance (equation 1).
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d NTC=PT−PCNTC
dt1
The compressor and turbine look-up tables (maps) contain mass flows (mC and mT) and isentropic
efficiencies (ηC and ηC) as a function of pressure ratio and turbocharger speed. These maps allow the
prediction of compressor and turbine power and compressor and turbine outlet temperatures (T 2 and
T 5 respectively). This is illustrated for the compressor through equations 2 and 3.
T 2=T 1(1+ (P2P1 )γ−1γ −1
ηC)
2
PC=mC c p (T2−T 1 ) 3
The intercooler is a simple heat loss model based on a thermal energy balance to calculate the inlet
manifold temperature (T 3, see equation 4). The external heat loss (P IC¿is imposed by the user interface
and the pressure drop in the intercooler is neglected (meaning P3=P2).
T 3=T 2−PICmC c p
4
The intake and exhaust manifolds act as control volumes which can accumulate mass based on the flows
into and out of them. Their temperature is imposed by the gas entering the manifold (T 3for inlet
manifold and T 4 for exhaust). The total mass accumulated in the manifolds is determined from equation
5 and 6. The pressures P3 and P4 are then calculated using the perfect gas law equation 7.
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d m¿=( mC−meng )dt 5
d mex=(meng−mT )dt 6
Pi=miRT iV i
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All the models are linked to the user interface to supply data to replicate measurements that would be
taken on the test rig. By combining these different models, the students can explore these mathematical
formulations which, although not the core topic of this laboratory, will be useful for them across their
degree programme. These mathematical formulations that can be used as templates for future work.
The simplification of the models ultimately resulted in a compromise of the model accuracy. However,
high precision of the outputs is not vital for a teaching environment, and the most important
requirement is that the model behaves in a realistic way. Figure 7 compares the virtual laboratory
prediction to published results from experimental work on Diesel engines operating at a similar
condition. Apart from Figure 7b, the virtual lab captures the trend of engine behaviour well, even if
there are clear differences in the absolute magnitude. For Figure 7b, although the shapes of the curves
are not comparable, the trend for increased specific fuel consumption with increased EGR rates is
maintained which is acceptable for this teaching application.
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(a) (b)
(c) (d)Figure 7: Model prediction of specific fuel consumption and specific NOx emissions compared to measured data for different injection timings and EGR rates. The injection timings are compared to data from a similar sized engine [26] and the data for EGR rate from a larger engine [27], both operating at similar speed and specific load points.
One of the major drawbacks of virtual laboratories is the lack of realism which can be off-putting for
students. Some of this realism can be addressed by the way the virtual laboratories are used within the
course and this will be addressed in the following section. However, some of the realism is inherent to
the software model and will be discussed here.
Most simulation models are deterministic, meaning that for a given set of initial and boundary
conditions, the model will calculate the same outcome every time the model is run. This is the case of
the models used in this application. However, experimental work always includes a degree of
randomness due to:
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- Random variation and error in the control and instrumentation equipment,
- Time based evolution of the test piece that is not typically captured by simulation models.
These variations are a key aspect of engineering education in the early years where much time is spent
teaching students that with experimental work there is no single, precise, and specific correct answer.
However, with a virtual laboratory, this exact answer may well seem to exist. To improve the realism,
random variation from the sensors was included into the virtual laboratory. This was included as an
addition to the model rather than in the user interface by mean of a random noise added to the model
signal. The amplitude of the noise was chosen to be of similar order of magnitude to the uncertainty of
the measurement equipment typically used in the real test cell. Uncertainty in the actuators was not
considered here, but could be introduced in a similar way to the model inputs.
4 Virtual model use in student assessment
The virtual engine laboratory was used as part of a coursework assessment as part of a master’s level
course. The students were given 6 weeks to complete the task in their own time, being able to access
the virtual laboratory at any time.
The coursework was aimed at teaching them the methodology of engine calibration. This important step
in engine development requires engineers to determine the optimal settings of the engine actuators to
meet fuel consumption, emissions, and performance targets. This engineering task is essentially an
optimisation problem of a complex, non-linear system with many input parameters and multiple targets
and constraints. It is typically conducted once engine hardware is available and the industry state-of-
the-art approach makes use of design of experiments methodology [2]. The optimisation process is
typically conducted according to the commonly called “Z-process” [2] which combines the following six
steps:
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1. Problem definition defining the targets and acceptable ranges of actuator settings based on
expert and prior knowledge,
2. Design of experimental test plan using specialist engineering software,
3. Experimental test campaign to collect data in the engine test facility,
4. Regression modelling to generate mathematical functions capturing the measured behaviour of
the engine (in specialist software),
5. Search of optimum controller configuration using optimisation algorithms and functions
generated in step 4,
6. Validation of optimal controller configuration on engine test cell.
The exercise created in this case study aims to allow students to put into practice steps 2-4 of this
process. In undertaking this task, the students are:
- Learn the underlying principles of DoE which is a useful statistical and experimental skill,
- Experience real engineering software for the application of DoE,
- Learn the functionality of engine test cells through the virtual laboratory.
The first of these points is achieved through the coursework design. Students were asked to undertake a
“one factor at a time” experiment, followed by a DoE approach. Through the same number of test
points, they will learn that the DoE approach gives them a far richer data set and much more
information on the behaviour of their system. Because they are required to collect the data from the
virtual laboratory, they will appreciate the time gain this approach offers.
The second is achieved by requiring the students to use an automotive calibration software tool to plan
their experiment and to build their regression models. It is not the aim of the course to teach any
particular software tool, however it is important to expose students to these tools as most available on
the market are similar in structure. This is analogous to teaching engineering drawing through computer
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aided design (CAD) software. A particular software package must be chosen, but the overall goal is to
teach the process.
The final step is achieved through use of the virtual laboratory and an accompanying session on engine
test cells delivered by a post-doctoral researcher. In addition to running the laboratory, the students are
also exposed to the post-processing of data and the conversion of measured quantities into physical
parameters. For example, exhaust emissions can only be measured as volumetric concentrations, and a
conversion process must be undertaken. This is an integral part of the exercise and students need to
create their own tools for doing this.
Figure 8 illustrates the intended workflow for the students’ assignment and clearly highlights the need
for the virtual engine laboratory. The engineering methods section are the key learning objectives of the
course, however without the virtual engine laboratory, it is not possible to complete the logical steps.
Any attempt to encourage students to undertake the tasks on the left-hand side of Figure 8 without the
virtual engine laboratory would require the provision of pre-recorded data and removes the feedback
from the test planning stage. The linking top the virtual engine lab allow the students to repeat and re-
try different approach.
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Figure 8: Outline procedure for students undertaking the coursework on calibration methods using the virtual laboratory environment
5 Reflection on the use of the virtual laboratory
5.1 Student attitude towards the virtual laboratory
The assignment deliverable was a short report capturing the student activities and a total of 58 students
participated and submitted a completed exercise.
The assignment asked students to compare the process of design of experiments with a simple one
factor at a time approach. The students were prescribed an experimental design and a regression model
structure and suggested they explore one additional design or model. As most student studied either
different experimental designs or regression models, it makes sense also to assess the total items
compared (nb. regression models + nb. experimental designs). Thus, the students were therefore asked
to compare 3 items. Figure 9a shows the number of students that compared different numbers of
experimental designs and regression models. Around 45% of student compared more than 3
models/designs.
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Whilst using the virtual laboratory, the students are exposed to the behaviour of the engines: they could
see the physical response of the engine to changes in set-points they were prescribing in the user
interface. For example, increasing the amount of fuel injected into the engine increases the exhaust
temperature and increases the speed of the turbocharger. This encourages the students to explore the
behaviour of the engine. In the activity, the students were asked to explore the trade-off between
engine fuel consumption and NOx emissions which is a key issue faced by engineers in the automotive
industry. Although the physical processes behind this trade-off are not the focus of this module, the
virtual lab intrigued many students to seek an understanding of this. Figure 9b shows the distribution of
students who cited relevant published material related to these physical processes. The figure also
shows the references explaining the statistical processes.
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(a)
(b)
Figure 9: Quantification of student work with respect to (a) design of experiments methodology and (b) the physical behaviour of internal combustion engines
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Both explaining the physical processes and comparing different experimental designs demonstrates that
the activity has stimulated the curiosity of many students to go beyond the core taught content. In
summary:
- 28% of students cited 3 or more references to explain engine processes or DoE processes
- 47% of students compared 4 or more regression models or experimental designs
- 57% of students did one of the above tasks and can be categorised as engaging in significant
self-learning because of the virtual laboratory.
5.2 Benefits to student learning
Section 5.1 showed how the virtual laboratory session allowed students to explore engine calibration
and statistical design in far more detail then previous cohorts taking the same course. It could be
postulated that the students who benefit from the virtual laboratory exercise would perform to a higher
level in a comparable examination situation relating to that topic. Figure 10 shows the normalised
examination scores from two cohorts, one having benefitted from the virtual laboratory session, the
other not. The distribution contains only information relating to the control and calibration part of the
examination. Although the examination question is different for each cohort, the question structure and
contents are comparable. Although in absolute terms there is an increase in examination performance,
historical analysis showed that this was within the year-to-year variability and therefore no direct
conclusion could be drawn.
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Figure 10: Normalised grades in examination of powertrain and calibration aspect of the course showing that there is a small but not significant improvement for students who have benefitted from the virtual laboratory.
The virtual laboratory coursework, which replaces part of the written examination, allows for the
assessment of the student’s application of theory and knowledge rather than their ability to remember.
This did show a marked impact on the performance of overseas students. Figure 11a shows the
normalised examination performance for students with a non-UK undergraduate education vs. those
with a UK-based undergraduate education. A marked difference can be seen between the two groups
with the overseas students underperforming compared to their UK-based counterparts. The
performance in the virtual laboratory coursework is shown in Figure 11b where the gap is significantly
less. Whilst the virtual laboratory is not the only method that could improve the inclusiveness of
teaching, the results do demonstrate the benefits of this learning technology. It is important to note that
without the virtual laboratory, only the written examination assessment would have been performed.
This also shows that the introduction of the virtual laboratory alone is not sufficient to improve the
performance in the examination: i.e. the undertaking of the virtual laboratory has not obviously
facilitated the learning of course contents. This reinforces that conclusion from the analysis of Figure 10
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which showed no significant difference in examination performance between the cohort having
benefitted from the virtual laboratory and those that did not.
(a)
(b)
Figure 11: Normalised grades of student whose undergraduate education was either in the UK or overseas for (a) the examination and (b) virtual laboratory assignment (Note: normalised grade shows the boundaries of marks achieved by students and does not indicate the pass/fail boundary. Both graphs a and b have the same x scale)
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5.3 Further skills development
Matlab was used as the platform for the user interface to encourage the development of generic
programming skills. It would have been possible to package the user interface as an independent
software tool for which access to the code and structure remains hidden for the user. Whilst this would
make the interface user-friendly, it is recognised that in engineering programming is becoming a key
generic skill and students should be encouraged to explore these tools. The open source nature of the
virtual laboratory empowers students to develop the ideas further incorporating new features such as
data analysis interfaces.
During the assessment period, most participants used the virtual laboratory as it was intended, i.e. by
using the GUI to drive the mathematical model. However, a small number of participants sought to
interact directly with the code. The primary motivation for this was to reduce the time required to spend
in front of the user interface during the data collection phases (Figure 8). Whilst at first glance this may
seem contrary to the objective of recreating the experience of operating a real engine laboratory, in
practice this approach was encouraged as requires direct interaction with the mathematical model of
the engine.
Students that engage with the mathematical model of the engine are in fact engaging with the research
work that has led to the creation of these models. This directly links teaching with research and has the
advantages:
- Encouraging the next generation of researchers in this community,
- Encouraging teaching to remain at the cutting edge of research.
5.4 Transferability of the virtual laboratory
The virtual laboratory interface was developed with an advisory board with membership from other
departments across the university (chemical engineering, health, electrical engineering, and physics).
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The involvement of academics from a range of disciplines was done to ensure that the interface library
would respond to their needs and be easily transferrable. Over the course of the virtual laboratory
development, three key applications were identified:
In chemistry/chemical engineering an application would enable students to experience the
control and monitoring of full scale chemical plants. This is important as students often have
difficulties in understanding the thermodynamic issues and the need to control reactions when
laboratories are scaled up to production,
In health education, distance learning students could benefit from the experience of monitoring
muscle activity of athletes breathing during exercise. These students have limited contact time
where they can visit the real laboratories: a virtual laboratory would allow them to experience
the data collection aspect from off-campus location,
In electrical engineering, the analysis of global positioning satellite (GPS) receiver technology
was identified to support learning in space science. This application has a similar motivation to
the virtual engine laboratory to provide students with practical experiences that cannot easily
be delivered in a real laboratory environment.
The user interface tool is hosted within an online repository, including full documentation and case
studies of the different applications.
6 Conclusions
A virtual laboratory for automotive applications is presented. The Virtual laboratory was built to
recreate the experience of the engineer when using an engine test cell, by replacing the real engine and
hardware with a mathematical model issued from research. The exercise has succeeded in changing the
learning experience of students by allowing them to put knowledge into practice. This has been
evidenced by:
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- The depth with which students have explored the topic of calibration,
- The engagement of student with research and external literature on engine physical processes,
- The improvement of performance of overseas students compared to assessments focussed on
remembering.
The open source nature of the user interface has successfully engaged students with the development
of the programming skills with a number of students exploring and modifying the application to suit
their needs. It is hoped that, along with the exposure to research, that this will encourage new young
researchers in this area in the future.
The virtual laboratory interface tool was built as a library of components that can easily be used to
create new interfaces for different applications. The project was undertaken with the advice from
academics from different disciplines to promote the transfer of the tool into other courses. This has
been a success as a second application is already underway in electrical engineering.
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