Gas Exchange Modelling and Control Strategy Development for Spark Ignited Engine

IN

DEGREE PROJECT ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS

,

STOCKHOLM SWEDEN 2018

Gas Exchange Modelling and

Control Strategy Development for

Spark Ignited Engine

Model Based Control Strategy Development for

SI Engine

reglerstrategi för spark ignited engine

2018-09-28

Dejiu Chen

SCANIA

Pramod Swaminathan

Mikael Hellgren

Mårten Wallengren

Sammanfattning

Development for Spark Ignited Engine

2018-09-28

Abstract

Dejiu Chen

SCANIA

Pramod Swaminathan

Mikael Hellgren

Mårten Wallengren

I would like to thank my supervisor Mårten Wallengren (NESI) from SCANIA for many

interesting discussions, valuable feedback and for creating a positive environment to work. From

first, he had a strong belief in me and has been constantly supporting throughout my thesis work.

He has been a great mentor to me and I have learned a lot from him. I am very grateful to work

under his supervision.

I would like to thank my second supervisor Mikael Hellgren from Kth University for providing

valuable inputs for my research work. He has been a great mental support throughout my thesis

work. The regular weekly updates and feedbacks helped me a lot in the research. I would also like

to thank Ola Stenlåås for giving me an opportunity to work in SCANIA, providing valid guidance

and motivation at the right time. I was really motivated with his intelligence and work. A great

thanks for showing me the path that I have always wanted. I would also like to thank my

department manager Erik Rundqvist for all kinds of supports and providing access to resources

without which it would have been really hard for me to finish the work on time.

I would also like to thank my colleagues Joakim Rodebäck for the GT-SUITE introduction and

explanation about the models, Christer Forslund for helping me to establish the communication

link which was really appreciative to commence a good start in my thesis and all the co-workers in

NESI department and Raymond Reinmann for constant motivation and support.

Finally I would like to express my gratitude to my parents, my sister, my brother and friends for

always being there, for understanding my situations and giving me all kinds of support and

encouragement that was much needed throughout my work.

Pramod Swaminathan

Contents

1 Introduction 1

1.1 Background Study . . . 1

1.2 Purpose and Goal . . . 3

1.3 Research Questions . . . 3

1.4 Problem Statement . . . 4

1.5 Overall Approach . . . 4

1.6 Ethical Consideration . . . 8

2 State of the Art 9 3 Modelling 12 3.1 Throttle Model . . . 12 3.2 EGR Model . . . 14 3.2.1 EGR Cooler . . . 14 3.3 Turbo Charger . . . 15 3.3.1 Compressor Efficiency . . . 15 3.3.2 Turbine Efficiency . . . 16 3.3.3 Total Efficiency . . . 16 3.3.4 Wastegate . . . 16 3.4 GT SUITE . . . 18 3.4.1 Engine Model . . . 18 4 Control Strategy 19 4.1 Model-Based Controller . . . 19 4.1.1 Throttle . . . 21 4.1.2 EGR . . . 22 4.1.3 Wastegate . . . 24

4.2 Closed Loop Control Using PI/PID . . . 24

4.2.1 Air Correction . . . 25

4.2.2 EGR Correction . . . 26

4.2.3 Pressure Drop Over Throttle Correction . . . 27

4.3 Overall Controller Architecture . . . 27

6.1 Conclusion . . . 35 6.2 Future Work . . . 36

Bibliography 37

Appendices 38

List of Figures

1.1 Plant Model [2] . . . 2

1.2 Schematic Illustration of SI Engine Model . . . 5

1.3 Overall Approach . . . 6

3.1 Psi Function . . . 13

4.1 Filter Response . . . 20

4.2 Delay Model With Low Pass Filter . . . 20

4.3 Model Based Air Controller Without PID . . . 22

4.4 Model Based EGR Controller Without PID . . . 23

4.5 Closed Loop Control Architecture . . . 25

4.6 Model Based Air Controller With PID . . . 25

4.7 Model Based EGR Controller With PID . . . 26

4.8 Cascade Controller Architecture . . . 28

5.1 LowLoad Positive AIR Request . . . 29

5.2 LowLoad Positive EGR Request . . . 30

5.3 LowLoad Negative AIR Request . . . 31

5.4 LowLoad Negative EGR Request . . . 31

5.5 MediumLoad Positive AIR Request . . . 32

5.6 MediumLoad Positive EGR Request . . . 32

5.7 MediumLoad Pegative AIR Request . . . 33

Chapter 1

Introduction

In the current scenario, the automotive industry has imposed a lot of regulations that should be followed strictly in order to reduce the emission from the heavy-duty vehicles. At the same time, from the customer’s perspective, there is a huge demand for torque performance which also includes minimal fuel consumption, high safety and comfort. With the advancement in control strategies, hardware configurations and software architecture, it is possible to add extra-functional specific features which satisfies the requested demands and legislation. Many heavy-duty commercial vehicles are running on diesel as it gets more mileage, better fuel consumption than gasoline vehicles. A more environmentally friendly option is to have CNG vehicles which can be run on biogas from food wastes that reduces more emission and provides sustainable solutions [1]. In today’s world, CNG vehicles use Exhaust Gas Recirculation (EGR) to reduce nitrogen oxide emission and also it reduces the exhaust temperature which ultimately prevents damaging the components like the turbine, manifolds, wastegate valve, three-way catalyst etc. Hence from the control perspective, to achieve good efficiency, it is important to control the air flow into the intake manifold to have the desired torque. Hence in this thesis work, a control architecture of coordinated actuation of throttle, waste-gate and EGR valves are developed and investigated in both theoretical and experimental aspects.

1.1

Background Study

Figure 1.1. Plant model [2]

1.2. PURPOSE AND GOAL

On the other hand, there are few constraints that have to be imposed in order to have a better efficiency by taking consideration of factors that influence high fuel consumption, higher emissions and pumping losses. Constraints are imposed on air-fuel ratio to be at stoichiometry at all operating points as it is very important for the 3-way catalyst to work efficiently which gives lower emissions. Constraints are also imposed on pressure drop over throttle to be in an acceptable range as it can influence high pumping loss thereby more work has to be done by the piston to get the air from the intake manifold to the cylinder which results in high fuel consumption. In the control perspective, constraints are imposed on the overshoots and steady-state error in order to have better settling time. The ultimate aim to develop a control algorithm which satisfies all the constraints and cross-sensitivities that provides an optimal results in terms of efficiency and high torque by reaching the target air and EGR requests as fast as possible.

1.2

Purpose and Goal

The main purpose of this project is to build a control system which controls the flow of air and EGR rate into the cylinder by simultaneously controlling throttle, EGR and wastegate actuator to have an optimum response time in the transient domain in all operating points (fixed and varying RPM). Integrating EGR control functionalities together with air mass control adds more complexity as all the three actuators are interdependent on each other. Each actuator influences the other ac-tuator’s responses and it’s a high risk if there is a huge delay between the actuator responses which ultimately loses its performance and makes the system unstable. The air-fuel mixture is always maintained to stay at stoichiometric in all operating regions. Hence lambda control is beyond the scope of this project. The ultimate need of this project is in the current SCANIA architecture, all the actuators are controlled independently, having no knowledge of each others responses. In order to have robust and optimum control flow, the developed control algorithm through this thesis work paves the way that all the control actions are automated and syn-chronized with respect to the environment.

1.3

Research Questions

In this thesis work, the main research questions that are addressed are discussed below.

• How to integrate Model-based controller and nonlinear Gain-Scheduling PID controller in the closed loop control system architecture?

• How to ‘Synchronize’ or ’Schedule’ the controller tasks and the control flow from SIMULINK to GT-SUITE and vice versa?

1.4

Problem Statement

A gas Spark Ignited (SI) engine with stoichiometric combustion uses cooled Exhaust Gas Recirculation (EGR) from the exhaust manifold into the intake manifold to lower the exhaust gas temperature in order to protect the components such as valves, manifold, turbine and catalyst. Instead of using after treatment process to reduce the exhaust emission, it is possible to reduce the exhaust emission during combustion by introducing Exhaust Gas Recirculation (EGR) into the cylinder. To control the right amount of air and EGR into the cylinder, three actuators are involved, namely throttle, EGR and waste-gate valve with cross sensitivities between them. The project aims at controlling the above-mentioned actuators namely throttle, EGR and waste-gate simultaneously to achieve the requested torque by developing a control strategy to dynamically reach the reference values of air and EGR into the cylinder as quickly as possible. Hence, model-based controller algorithm should be implemented on the actuators to handle the constraints and make the transient response more robust. The ultimate goal of this thesis is to develop a robust control algorithm which controls the three actuators simultaneously considering the cross sensitivities, constraints and more focus is given to the EGR valve such that the other two are automatically tuned to the required amount so as to increase the response time, minimal overshoots (maybe 2%) at positive transients and completely neglecting undershoots at negative transients. In this thesis project, a cascade controller is developed which includes PID as a pre-controller, feeding the control signals to the Model-based controller which tunes to the requested performance.

1.5

Overall Approach

1.5. OVERALL APPROACH

illustration of Spark Ignited (SI) Engine and the control algorithm implementation has been explained below with a simple sketch.

Figure 1.2 explains a simple schematic illustration of Spark Ignited (SI) Engine Model flows and actuation. The throttle valve controls the fresh air intake into the inlet manifold. EGR valve controls the EGR fraction requested from the exhaust manifold into the intake manifold. From the exhaust manifold, the turbine and compressor are attached through the turbo shaft which controls the boost pressure and the pressure drop over the throttle. The wastegate controls the air intake into inlet manifold (boost pressure), the EGR flow rate (Back Pressure) and the torque by controlling the exhaust gas to the turbine.

1.5. OVERALL APPROACH

1.6

Ethical Consideration

Chapter 2

State of the Art

There are a lot of research works that have been done in the area of developing control strategies for internal combustion engines and gave strong foundation on the dynamics and algorithms.

Henrik Andersson [1], different control strategies have been performed and the transient responses are well analyzed for different operating ranges. The trade-off and methodology are well explained in order to provide a good base for the future researcher to carry on with respect to the control development in the transient do-main. The paper gives a clarity on how to proceed with the algorithm with respect to EGR ratio control, simultaneously adapting with respect to throttle and waste-gate valve using the Model-based controller.

Tomohiko Jimbo et al [2], The dynamical model properties and the approach are almost similar in our case. The only difference is the paper focused only on controlling EGR rate into the cylinder. This paper provides sufficient understand-ing of the plant dynamics and the controller design. In the proposed controller, the nonlinear problem is completely transformed into a time-invariant quadratic one for the EGR flow through the intake valve into the cylinder. It has proven successful results by implementing only one controller for the entire engine operating range. The EGR ratio is automatically maximized without misfiring by imposing suitable constraints to the MPC controller.

of system variability to achieve offset-free reference tracking.

Qilun Zhu et al [4], a nonlinear model predictive control based algorithm has been designed in order to track the manifold pressure and EGR concentration by controlling throttle, EGR and continuous surge and waste-gate valve. The control actions have been obtained optimally using Sequential Quadratic Programming. This paper provides a good foundation in designing the controller to meet the re-quired objective as it provides satisfactory results in tracking the manifold pressure and EGR rate with minimal response time.

Argolini et al [5], tries different control algorithms such as integral actions with anti-windup to follow the reference with full state feedback systems, LQ controller, MPC controller and good analysis has been provided at the end. The relation between torque required and the waste-gate boost pressure control was well demon-strated at low load as well as at high load. It also explains clearly as for how to minimize the pumping loss using optimal control technique.

Thomas et al [6], the model-based controller is well explained as for how to dynamically change the model parameters on look-up tables i.e., on the engine model itself which ultimately saves a lot of time during calibration. The Model-based controller is a less complex algorithm when compared to other optimal control algorithm and it is easy to calibrate yet provides satisfactory behavior at transients. Friedrich et al [7], it is well noted that the EGR actuator is controlled with the help of auxiliary throttle controller. With the help of pressure drop over the throttle valve, the EGR flow is maintained in the intake manifold. If necessary EGR flow is not achieved, closing the throttle increases the pressure difference which is used as a parameter to control the EGR actuator. Using waste-gate actuator, it increases the pressure in the exhaust manifold which causes the EGR rate to flow more as well as increases the pumping loss thereby leading to high fuel consumption. Hence, throttle actuator is used to control EGR valve which is well noted.

Signal Description Unit

Pic Pressure Before Throttle Bar

Pim Pressure After Throttle Bar

PimReq Pressure request after throttle Bar

Tic Temperature Before Throttle Kelvin

Pem Pressure in the exhaust manifold Bar

Tem Temperature in the exhaust manifold Kelvin

Tbef Comp Temperature before Compressor Kelvin

Pbef Comp Pressure before Compressor Bar

Tbef T urb Temperature before Turbine Kelvin

Pamb Ambient Pressure Bar

˙m Mass Flow Rate Kg/s

AT h,EGR,wg Effective Area throttle,EGR,wastegate mm2

ΠT h,EGR.cmp,turb Pressure Ratio throttle,EGR,compressor,turbine No Unit

γ Specific Heat capacity J oules Ra Specific Gas Constant = 287.1 No Unit

Regr Specific Gas Constant = 286.9 No Unit

PratioComp Pressure ratio over the Compressor No Unit

PratioT urb Pressure ratio over the Turbine No Unit

VairF low Volume of the air flow through the Compressor mm3

Cpair Specific Heat capacity of air = 1.005 KJ/KgK

Cpexh Specific Heat capacity of exhaust air = 1.32 KJ/KgK

Modelling

In this chapter, modelling of the actuators are based on the previous thesis work by Henrik Andersson [1] who developed a Mean-Value Model for the Gas Exchange flow. His work provides a better understanding of theoretical aspects of the model and how it works when the actuators are controlled simultaneously. There are two types of modelling in this thesis. Modelling the dynamics of the engine is done in GT-SUITE software which was readily available at the beginning of the thesis work. This model consists of 5 cylinder engine, the valves, pipes, manifolds and components parameters are designed and calibrated in GT-SUITE modelling soft-ware. The other sub-model is modelling the flow and pressure drop over the com-ponents/actuators in the engine to find optimum control signals which are modelled using physical relations. The main purpose of this modelling is to develop a dy-namic behaviour and to establish the cross-sensitivities between throttle, EGR and wastegate which ultimately makes it realistic to develop and analyze the control algorithms.

3.1

Throttle Model

3.1. THROTTLE MODEL

The flow model is described as [1]

˙m =_√ Pic RairTic AT hΨ(ΠT H) (3.1) ΠT h = max ( Pim Pic ,  2 γ+ 1 ) (3.2) Ψ(ΠT h) = s 2γ γ −1  Π2γ T h−Π γ+1 γ T h  (3.3)

From the equations described in 3.1, ˙m is the mass flow through the valve, ψ is the psi function derived from pressure ratio. The significant of psi function which can be seen from Figuere 3.1 is to maintain it around 0.7 and the pressure ratio to be within 0.68, otherwise the psi function goes low and the fuel consumption increases tremendously because more flow of air mass (that is the relation between Psi function and fuel consumption in this context). The effective area AT h is

cal-culated from the requested air mass flow rate model (calibrated from the requested torque model).

Figure 3.1. Psi Function

Using thermodynamics principle, the effective area is calculated for the requested air flow. The Ψ(Π) function which depends on the pressure ratio over the throttle (ΠT h) varies between 0 and 0.6847 at sonic (shocked) flow which is shown in Figure

3.2

EGR Model

The EGR flow actuator is modelled similarly to that of throttle and the factor of EGR cooler has been taken into account because when controlling all the three actuators simultaneously, the emptying and filling of EGR cooler plays a very vital role during the transients as we have huge EGR cooler which should be able to bring the exhaust gases to the engine temperature. During the transients, a prediction model has been designed to compensate the EGR cooler filling/emptying that has provided a greater contribution to increase the EGR rate response to reach the re-quests as soon as possible. The EGR flow over the EGR actuator is modelled using throttle equation [1] which is shown below

˙m = Pem p RegrTem AEGRΨ(ΠEGR) (3.4) ΠEGR = max ( Pim Pem ,  2 γ+ 1 ) (3.5) Ψ(ΠEGR) = s 2γ γ −1  Πγ2 EGR−Π γ+1 γ EGR  (3.6) Similarly, from the throttle model description, the EGR effective area is calculated from the EGR rate flow equation.

3.2.1 EGR Cooler

Since the exhaust gases are completely burnt and it has very high temperature and pressure, the EGR cooler has to be made big enough to make the EGR gas tem-perature equivalent to Engine temtem-perature. When there is a transient (new load request change), a prediction model has been designed to compensate the empty-ing and fillempty-ing of EGR cooler in advance to make the EGR rate enterempty-ing the inlet manifold as quickly as possible.

In order to capture the dynamic of the EGR cooler, it is necessary to capture the mass exchange and temperature exchange over the cooler. To capture the temper-ature change over the cooler, a weighted mean average of the exhaust tempertemper-ature and the engine temperature is considered with the relation as shown in equation 3.7

Tmean= 0.8Tengine+ 0.2Tem (3.7)

Since the volume of EGR cooler is very high, more weights are given to the engine temperature as it has the capability to cool the exhaust gases through the EGR valve to the engine coolant temperature.

The first step is to discretize the signal with that of the sampling time 10ms.

mcooler(n) = mcooler(n − 1) + minM eas− moutM eas (3.8)

mcooler(n + 1) = mcooler(n) + minReq− moutM eas (3.9)

3.3. TURBO CHARGER

where mcooler(n − 1), minM eas and moutM eas are calculated as

mcooler(n − 1) = PimVEGRcooler REGRTmean(n − 1) (3.11) minM eas= Aef f EGR_√ ΨEGRPem REGRTem (3.12)

moutM eas = EGRmeasRate(%) ˙mairM eas (3.13)

Combining the equations from (4.8) to (4.13), we predict the EGR mass flow model as shown in equation 3.14

minReq = mcooler(n + 1) − mcooler(n − 1) − minM eas+ 2moutM eas (3.14)

From 3.14, the total predicted mass flow rate is estimated as

˙minReq = minReq∗ Sampling_T ime (3.15)

where the Sampling_T ime is fixed for the simulation process to be 10ms.

3.3

Turbo Charger

The model of Turbo Charger consists of modelling the compressor, turbine and wastegate actuator which will be described in the below subsections.

3.3.1 Compressor Efficiency

The compressor contains frictionless flow which means all energy produced from the compressor is not been used to force the boost into the intercooler volume. To calculate the consumed power, η efficiency of the compressor has to be modelled [1]. In this thesis, a calibrated 2-D Map has been provided with Πcmp and VairF low

as inputs. The model to calculate Πcmp and VairF low are shown in equations 3.16

and 3.17

Πcmp=

Pic

Pbef Comp (3.16)

VairF low =

mairM easF low

ρin (3.17)

where mairM easf low is the measure air mass flow through the intercooler and ρin is

the air density. To calculate the air density ρin we use a small model as shown in

equation 3.18

ρin=

Pbef Comp

TairRair (3.18)

With the physical models that is designed from equations 3.16 to 3.18, we cal-culate the compressor efficiency ηComp in the form of a calibration map with flow

3.3.2 Turbine Efficiency

In turbo charged gas engines, the turbine is connected to the turbo shaft and which is connected to the compressor on the other side. The main purpose is to drive the compressor with specific turbine speed. Work is done by the flow to turn the turbine and the shaft. In order to calculate the turbine efficiency ηturb, the

compressor speed is estimated and calibrated from the volume of air flow VairF low

and the pressure ratio over the compressor Πcmp as inputs from equations 3.16 and

3.17. The compressor speed was in the form of a calibration map with flow and pressure quote on the axises. The calibration was provided by Scania. In order to calculate the turbine efficiency, a small approximation model has been designed by dividing the reduced turbine speed by a constant factor and the square root of exhaust temperature. The approximated model output is then calibrated with respect to the pressure quote over the turbine in the form of a 1D calibration map. The calibration has been performed and it is provided by SCANIA.

3.3.3 Total Efficiency

In order to calculate the total work done by the compressor and turbine, it is very much important to calculate the total efficiency of the turbocharger which consists of efficiency of turbine and compressor. The efficiency η is given by

η= P ower_Shaft

W0 (3.19)

where W0 _{is the total Work done.}

Thus the total efficiency is calculated as shown in equation 3.20

ηtot= ηturbηcomp (3.20)

3.3.4 Wastegate

3.3. TURBO CHARGER ˙m = Pem p RegrTem AwgΨ(Πwg) (3.21) Πwg = max ( Pamb Pem ,  2 γ+ 1 ) (3.22) Ψ(Πwg) = s 2γ γ −1  Π2γ wg−Π γ+1 γ wg  (3.23) To calculate the turbine mass flow, the compression work and expansion work has to be modelled using thermodynamics principle as shown in (3.25) to (3.30)

Wcomp = Wturb∗ ηcomp∗ ηturb∗ ηmech= Wturb∗ ηtot (3.24)

where Wcomp and Wcomp are the compression work and turbine work respectively.

Here, the nmech is neglected and the total efficiency ηtot is calculated from the

equa-tion 3.20.

Isentropic compressor workis given by the relation 3.26.

Wcomp = mair∗ Cpair∗ Tbef Comp∗(Π(acmpair)−1) (3.25)

where mair is the mass of air in mg, Cpair is the specific heat capacity of air which

can be considered as a constant given by 1.005 KJ/KgK. The constant aairis given

by the relation 3.26.

aair =

γair−1

γair (3.26)

where γair is the ratio of specific heats (Cp_Cv) which is equal to 1.4.

Isentropic expansion work is modelled using the relation 3.28

Wturb = mturb∗ Cpexh∗ Tbef T urb∗(1 − Π

( 1

aexh)

exh ) (3.27)

where mturb is the turbine air mass in mg, Cpexh is the specific heat capacity of

exhaust air which is constant given by 1.32 KJ/KgK. The constant aexh is given by

the relation 3.28

aexh =

γexh−1

γexh (3.28)

where γexh is the ratio of specific heats (_CvCp) which has been chosen to be equal to

1.33.

From the modelled turbine mass flow and with the specific operating points (RPM), the mass flow over the turbine is computed which drives the turbo shaft and the compressor to give the necessary boost to increase the air response. From the computed turbine flow mass, a PWM signal is generated that gives the control signal to the wastegate position. However the control signal here is the orifice diameter. Hence a small 2-D MAP has been generated for PWM signal and RPM as inputs. By varying the wastegate diameter from 0 % to 100 %, its corresponding response over the air mass response is calibrated for all operating points.

3.4

GT SUITE

3.4.1 Engine Model

The model that is explained in section 3 is implemented in the SIMULINK envi-ronment in order to analyze the flow and characteristics of the actuators. With the control algorithm techniques, the actual characteristics and test analysis can be done with a real time engine model that has been modelled in the GT-SUITE software. A 5-cylinder engine model with actuators, manifolds, turbine, compressor and other components that also includes valves, cooler, pipes, crankshaft etc, with appropriate dimensions and characteristic flows is modelled which is provided at the beginning of the thesis work. Appropriate sensors are integrated and a communication link has been established between the GT-SUITE software and the physical models that is designed in SIMULINK environments through Functional Mock-up Unit (FMU). It’s a two way communication link where the sensor values are communicated to the physical models in SIMULINK, the control algorithm is performed with the physical models and the actuating control signals are communicated back to the actuators that is modelled in GT-SUITE through FMU channels. The transient domain responses has been analyzed in the SIMULINK environment.

Chapter 4

Control Strategy

In this chapter, the plant model [2] as shown in Figure 1.1 and the control strategy is explained. The mathematical model definition discussed in Chapter 3 will be very useful to design the physical model-based control algorithm to control air and EGR into the inlet manifold. The plant model that is described in Figure 1.1 is similar to that of the model that needs to be controlled in this thesis work. The only component that is missing is the EGR cooler but it has been modelled which is explained in the subsection 3.2.1

4.1

Model-Based Controller

A model-based controller is a proven technique to visualize the physical and mathe-matical models associated with designing complex control systems. From the plant model (which is modelled in GT-SUITE), all the necessary temperatures, pressures, mass flows, the control signal and feedback signals are integrated with the control environment (SIMULINK) using sensors which are filtered with respect to the sam-pling time. A simple low pass filter has been designed for the sensor signals which makes it compatible with the algorithm to perform with respect to the time stamp (sampling time). Filter design is one of the important process to eliminate the spikes in the signal and make it compatible to process the variables. A low pass filter is designed for all the sensor signals, controller error signals and PWM signals (processed variables). Figure 4.1 shows the elimination of spikes with respect to the actual signal used for designing the controller.

In Figure 4.1, the red signal is the unfiltered signal with unwanted spikes. The black signal is filtered using low pass filter which reduces the unwanted spikes. Similarly, as mentioned in section 3.4.1, the delay has to be modelled in order to compensate different response time of the three actuators. Here, the control signal is the orifice diameter which is calculated using the expression as mentioned in 4.1

D= 2

s

Aef f

Figure 4.1. Filter Response

where Aef f is the effective area which is obtained from the mass flow relation as

mentioned in equation (3.1), (3.4), (3.22). The control signal must be modelled as a time varying delay to synchronize the scheduling process and it is filtered using low pass filter as shown in Figure 4.2. It is seen that the control signal has continuous spikes as it is not filtered. Hence a small time varying delay model has been designed based on its response time and low pass filter has been implemented on the control signal to avoid spikes and sync with its sampling time.

Figure 4.2. Delay Model with Low Pass Filter

4.1. MODEL-BASED CONTROLLER

blue signal is the filtered control signal with its sampling time and it is delayed with suitable time varying response time of the actuator.

4.1.1 Throttle

The throttle model is explained in the subsection under Modelling 3.1. From the plot 3.1 and from the equation 3.1 we can see that lower the pressure ratio, the more effective area of the throttle is opened which increases the flow of air through the valve. This idea can be used to increase the air response by having a lower pressure ratio for the effective area calculation. To have lower pressure ratio, the pressure in the inlet manifold has to be optimal and referenced. Using thermodynamic principle, the pressure after the throttle request is calculated as

PimReq=

mtotRTim

ηvolV olcyl (4.2)

where mtot is the total mass entering into the inlet manifold which is calculated as

shown in equation 4.3, R is the specific gas constant, ηvolis the volumetric efficiency

which is calculated and calibrated using operating points (RPM), Pem and Pim as

inputs.

mtot= mair+ mf uel+ mEGR (4.3)

To have a faster response in the flow of fresh air through the throttle valve, in the calculation of pressure ratio ΠT h in equation 3.2, the pressure in the intake

manifold Pim is modified in the form as explained in equation 4.4 and this has

proved to increase the physical response time during the transients.

Pim=

(

PimReq, if PimReq>= Pim.

Pim, if PimReq< Pim.

(4.4)

Higher the Pim gives higher pressure ratio ΠT h which increases the flow ˙mT h

0 1 2 3 4 5 6 7 8 9 10 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 t mg

Figure 4.3Model Based Air Controller Without PID.

The y-axis is the air requests in milligram (mg). The blue signal is the requested air mass that needs to be followed by controlling the actuators. The red signal is the response after implementing the model-based controller where the requested re-sponse is achieved with little overshoot which should be avoided. To compensate the error an additional PID controller is designed which is explained in the subsection 4.2.1

4.1.2 EGR

4.1. MODEL-BASED CONTROLLER

as the torque is directly proportional to the fresh air. During the transients, the EGR valve open and close has to be saturated as it should not open too much which will again affect the air intake into the inlet manifold. This is the very challenging task to control Air and EGR into the inlet manifold at the same time and to solve this complicated issue is the main objective of this thesis. The EGR response using model-based algorithm is shown in Figure 4.4.

0 1 2 3 4 5 6 7 8 9 10 0 6 12 18 24 30 36 t E GR %

From the Figure 4.4, it is seen that the red signal is the requested EGR rate (%), the orange signal is the maximum acceptable boundary condition above which misfiring may happen in the engine which will severely damage the 3-way catalyst resulting in increased emissions. The maximum acceptable boundary limit is calcu-lated with respect to the air mass intake into the inlet manifold. The blue signal is the actual response from the EGR flow model. In this result, the y-axis is the EGR rate (%) and the x-axis is the time. There are some small oscillation/undershoot due to non-minimum phase behaviors. However, the effect of EGR cooler model can be clearly seen during the transient time which has improved the EGR response. In order to reduce the error in the response, Gain scheduling PID algorithm is implemented that will be explained in subsection 4.2.2.

4.1.3 Wastegate

From the model that is designed from subsection 3.3.4, the turbine mass flow model is controlled with respect to the requested pressure ratio and air mass which is used to predict the wastegate activation. Also, the PWM map has been generated and it is calibrated for all operating points and the wastegate diameter has been calibrated and tested from the requested turbine mass that is explained in subsection 3.3.4. As mentioned in section 3.4.1, the main purpose of wastegate is not only to boost the turbo-compressor flow to increase the air and EGR response during the transients, but also to control the pressure drop over the throttle as it will increase the fuel consumption.

4.2

Closed Loop Control Using PI/PID

4.2. CLOSED LOOP CONTROL USING PI/PID

Figure 4.5Closed Loop Control Architecture

4.2.1 Air Correction

As discussed in subsection 4.1.1, to compensate the overshoot, Gain Scheduling PID controller has been implemented and the response is shown in Figure 4.6.

0 1 2 3 4 5 6 7 8 9 10 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 t mg

In Figure 4.6, the y-axis is the air mass request into the intake manifold in milligrams (mg). The orange signal is the requested air in (mg). The red signal is the combination of PID controller and the model-based controller with error free offset response.

4.2.2 EGR Correction

As mentioned in subsection 4.1.2, to minimize the error in the EGR response, Gain scheduling PID algorithm is correlated over the error and the response is as shown in Figure 4.8. The Gain Scheduling algorithm is implemented with respect to the error dynamics from the Model based controller. When we deal with different operating points, Gain scheduling algorithm for nonlinear systems has proven advantage over other nonlinear controllers. The scheduled/observable variables are scheduled and appropriate linear controller is performed over the error dynamics.

0 1 2 3 4 5 6 7 8 9 10 0 6 12 18 24 30 36 t E GR %

4.3. OVERALL CONTROLLER ARCHITECTURE

In this Figure 4.7, the y-axis is the EGR rate (%). It is seen that the error is compensated and during the transients, the EGR emptying/filling model increases the response preventing the dip/undershoot. The yellow signal is the requested EGR (%), the red signal is the maximum bound obtained from the measured air above which it might cause misfire in the engine which should be avoided. Hence the response of the EGR actuator is captured with respect to the dynamics of the measure air rate with Gain scheduling PID to compensate the error which prevents the non-minimum phase behavior providing smooth performance which is shown by the blue signal.

4.2.3 Pressure Drop Over Throttle Correction

From the section 4.1.3, wastegate controls the air, EGR and pressure drop over the throttle. Hence three PID controllers have been designed and tested that controls the air, EGR and pressure drop over the throttle during the transients and through-out all the operating points which will be explained in the subsection 4.3.1 using block diagrams.

4.3

Overall Controller Architecture

4.3.1 Cascade Controller

In this subsection, the schematic block diagrams is explained which consists of Model based controller and precontroller that compensates the error (Requests -Measured) using Gain Scheduling PID algorithm.

Figure 4.8Cascade Controller Architecture

In Figure 4.8, the overall Cascade controller architecture is depicted which con-sists of filtering the sensor signals, implementing Gain Scheduling PID control Al-gorithm as a pre-filter to the model to activate the closed loop control that is explained in the chapter 3 and in section 4.2. EGR cooler emptying/filling func-tionalities are designed for the transients. Three PID controllers are designed for the wastegate which controls the EGR during the transients, Air requests and pres-sure drop over the throttle respectively. The Orifice diameter calculated from the Model-based controller is communicated back to GT-SUITE physical Engine model through FMU.

Chapter 5

Results and Analysis

The individual control design are integrated together with a scheduling function and all the three actuators are controlled simultaneously with cross sensitivities and functionalities. Calibration of the controller is done by varying the load requests and operating points. Testing and analyses is done with a fixed RPM in both positive and negative transients. For the low load requests, the throttle controls the air requests and EGR controls the EGR rate (%) requests. Active boost controller is disabled. For high load requests, the wastegate controls the AIR and EGR controls the EGR rate as per the Activation/Scheduling function. Throttle is activated when the wastegate tries to control the pressure drop over the throttle or when it boosts the back pressure to increase the EGR during the transients.

Figure 5.2. LowLoad Positive EGR Request

Figure 5.3. LowLoad Negative AIR Request

Figure 5.4. LowLoad Negative EGR Request

Figure 5.5. MediumLoad Positive AIR Request

Figure 5.6. MediumLoad Positive EGR Request

Figure 5.7. MediumLoad Negative AIR Request

Figure 5.8. MediumLoad Negative EGR Request

Note: The computational time is calculated from the simulation which depends on CPU clock speed, number of task handling in the CPU cores. In real time, it will be much more faster.

An important constraint that is imposed on the controller is to have acceptable EGR rate into the intake manifold (i.e) it must be in the burnable range so as to prevent combustion misfire. The plots in Figures 5.2, 5.4, 5.6 and 5.8 shows that the EGR rate measured does not go beyond the maximum allowable range. The maximum allowable range is calculated with respect to the measured air in the intake manifold.

Chapter 6

Conclusions and Future Work

6.1

Conclusion

Thus the Model-based controller integrated with pre-controllers using PID Gain Scheduling algorithm is performed and the main objective of controlling all the three actuators namely throttle, EGR and wastegate simultaneously with cross-sensitivities and constraints are implemented in the algorithm and the results are analyzed in the Chapter 5. The main research questions that are analyzed through this thesis work are as follows:

• How to integrate Model-based controller and nonlinear Gain-Scheduling PID controller in the closed loop control system architecture?

The dynamic controller is integrated in the closed loop architecture such that the actuation signal from the model based controller is fed to the Gain schedul-ing PID algorithm which is calculated with respect to error dynamics and is sent through the communication channel via Functional Mock-up Unit (FMU) to GT-SUIT model and the sensor signal (measured) is fed back to SIMULINK model through the same two-way communication channel (FMU) and thus it forms a closed loop controller. The combination of model-based controller and pre-controllers such as PI, PID using gain Scheduling algorithm for the nonlinear plant model gave us satisfactory results.

• How are the stability and robustness of the controller is evaluated?

• How to ‘Synchronize’ or ’Schedule’ the controller tasks and the con-trol flow from SIMULINK to GT-SUITE and vice versa?

The Synchronization (Activation) function of individual controller tasks is performed through an activation function in order to synchronize the control flow from the controller to the actuator by a series of cascaded logic func-tions. The synchronizing of communication link between SIMULINK and GT-SUITE is also established by having the same sampling interval between modelling environment and the control environment.

6.2

Future Work

Some of the possible improvements that can be made in terms of improving the algorithm and its performance are listed below:

• Calibration or tuning the pre-controllers can be improved to make the per-formance better in terms of reaching the steady state as soon as possible and calibrate for all operating points (RPM)

• Testing and analyses can be made with a very high change in the load requests and also by varying the operating points with respect to a vehicle model and tune the controllers for the varied RPM and load requests to have more robust and dynamic system.

• Improve the Mean temperature model in the EGR cooler design. A temporary fix has been made by creating a small model with exhaust temperature and engine temperature but further improved model can be made with respect to mass flow which will be more realistic.

Bibliography

[1] H. Andersson, "Model Based Control of Throttle, EGR and Wastegate - A Sys-tem Analysis of the Gas Flows in an SI-Engine", Dissertation, 2017.

[2] T. Jimbo, S. Tanaka, K. Sata, R. Hibino, “Predictive Control for High-EGR SI Engines without Misfire via Flow-based Design”, 51st IEEE Conference on Decision and Control, Maui, Hawaii, USA, December 10-13, 2012.

[3] M. Santillo, A. Karnik, “Model Predictive Controller Design for Throttle and Wastegate Control of a Turbocharged Engine”, 2013 American Control Confer-ence (ACC), Washington DC, USA, June 17-19, 2013.

[4] Q. Zhu, R. Koli, L. Feng, Si. Onori, Senior Member, IEEE and R. Prucka, “Nonlinear Model Predictive Air Path Control for Turbocharged SI Engines with Low Pressure EGR and a Continuous Surge Valve”, 2017 American Control Conference Sheraton Seattle Hotel, Seattle, USA, May 24-26, 2017.

[5] R. Argolini and V. Bloisi, “On optimal control of the wastegate in a turbocharged SI engine,” Dissertation, 2007.

[6] S.Thomas and R. P. Sharma. Model Based Control of Engines. SAE Paper, 2007-26-025, 2007.

[7] I. Friedrich, C.S. Lui, and D. Oehlerking, “Coordinated EGR-Rate Model-Based Controls of Turbocharged Diesel Engines via an Intake Throttle and an EGR valve”, Vehicle Power and Propulsion Conference, IEEE, 2009.

[8] Johan Wahlström, "Control of EGR and VGT for Emission Control and Pump-ing Work Minimization in Diesel Engines".

[9] E. Olvera Olvera, B. Del Muro Cuellar, J. C. Sanchez garcia and G. I. Duchen Sanchez - "Stabilization of unstable first order linear systems with time de-lay using a PD controller", Postgraduate Section, ESIME-Culhuacan, National Polytechnic Institute.

[11] Yang Wang and Stephen Boyd, "Fast Model Predictive Control Using On-line Optimization", IEEE Transactions on Control Systems Technology, Volume 18,No. 2, March 2010.

[12] Tianpu Dong, Fujun Zhang, Bolan Liu and Xiaohui An, "Model-Based State Feedback Controller Design for a Turbocharged Diesel Engine with an EGR system", ISSN 1996-1073, Energies 2015.

Appendix A

Communication Link

This is the message out from building Functional Mock-up Interface (FMI) to es-tablish the communication link.

Database gdx_2017_trial1_21786 created DB Threads connected: 4

Max. connection allowed: 400

============================================================================== GT-SUITE - GAMMA TECHNOLOGIES

—————————————————————————— License : GT-SUITE

Version : 2017 Build # : 3.0000

Platform : Windows 64 bit

============================================================================== INFO Allocating Object/Part memory...

Fuel Heating Value Stoich. Ratio GTI-STYLE SPECIES C H O N S Ar (@298.15K) MJ/kg (std air) ethanol-vap 2.00 6.00 1.00 0.00 0.00 0.00 27.73 8.943

indolene-vap 7.93 14.8 0.00 0.00 0.00 0.00 43.95 14.50 methane-vap 1.00 4.00 0.00 0.00 0.00 0.00 46.55 17.12

======================================================================== Processing TurbineSAEMap Object: HP(64)T70AR059_WG_CNG

INFO Default Tref = 923K set for SAE files.

INFO Speed line DEFAULT: contains speed points within 2% range Speed lines found

speed, #points, speed var (%): 2 7 2508.25 0.0 speed, #points, speed var (%): 3 7 3009.39 0.0 speed, #points, speed var (%): 4 7 3510.64 0.0 speed, #points, speed var (%): 5 7 4019.01 0.0 speed, #points, speed var (%): 6 9 4520.79 0.0

*WARN:Requested maximum speed less than map data, some data will be dis-carded!

Map range will be reduced from= 4520.79 to= 4438.3

*WARN:Point of EFFmax is the FIRST point at speed line(s): > 4520.79

*WARN:Input mass flow not monotonically increasing with PR at speed line(s): > 2005.12

> 2508.25 > 4520.79

*WARN:Input efficiency has more than one local peak at speed line(s): > 2005.12

> 2508.25 > 3510.64 > 4019.01 > 4520.79

*WARN:Speed lines extend to PR > 3.2, BSR fit limited to speeds up to= 3510.64 Slope: fitted = 0.053

Turbine Ref. Diameter = 64.0000 (for reference only) PR Table2: 1 2005. 1.349 0.514 0.0158 PR Table2: 2 2508. 1.594 0.535 0.0187 PR Table2: 3 3009. 1.940 0.552 0.0205 PR Table2: 4 3511. 2.415 0.544 0.0218 PR Table2: 5 4019. 3.045 0.521 0.0223 PR Table2: 6 4521. 3.834 0.500 0.0223

line#, cm1,em1,ce1,de2 1 1.005 5.000 1.597 1.223 line#, cm1,em1,ce1,de2 2 1.005 2.400 1.769 1.496 line#, cm1,em1,ce1,de2 3 1.049 1.000 1.573 1.654 line#, cm1,em1,ce1,de2 4 1.200 1.000 1.529 1.528

Mass flow fit, lowBSR/highPR side: mass flow ratio at zero BSR: 1.03953 Mass flow fit, exponent of the mass flow line: 2.72744

Efficiency fit, lowBSR/highPR side: shape factor at low BSR: 1.63295 Efficiency fit, highBSR/lowPR side: zero intercept at high BSR: 1.51826 INFO Generating Turbine Map: HP(64)T70AR059_WG_CNG

INFO Efficiency fit RMS error : 0.615161% INFO Mass flow fit RMS error : 2.351654E-02% INFO Efficiency fit normalized RMS error : 8.04132% INFO Mass flow fit normalized RMS error : 3.03523%

======================================================================== Processing TurbineSAEMap Object: HTT_HP64mm_70T_059AR

INFO Default Tref = 923K set for SAE files.

INFO Speed line DEFAULT: contains speed points within 2% range Speed lines found

——————-speed, #points, speed var (%): 1 7 1970.30 0.0 speed, #points, speed var (%): 2 7 2795.54 0.0 speed, #points, speed var (%): 3 7 3453.37 0.0 speed, #points, speed var (%): 4 7 3783.97 0.0 speed, #points, speed var (%): 5 7 3948.37 0.0 speed, #points, speed var (%): 6 7 4204.67 0.0 speed, #points, speed var (%): 7 7 4434.91 0.0

*WARN:Point of EFFmax is the LAST point at speed line(s): > 3783.97

*WARN:Input mass flow not monotonically increasing with PR at speed line(s): > 3783.97

> 3948.37 > 4204.67 > 4434.91

*WARN:Input efficiency has more than one local peak at speed line(s): > 1970.3

> 3783.97 > 3948.37 > 4204.67 > 4434.91

*WARN:Speed lines extend to PR > 3.2, BSR fit limited to speeds up to= 4204.67 Slope: fitted = 0.091

Turbine Ref. Diameter = 64.0000 (for reference only) PR Table2: 1 1970. 1.338 0.506 0.0161 PR Table2: 2 2796. 1.753 0.528 0.0207 PR Table2: 3 3453. 2.238 0.590 0.0231 PR Table2: 4 3784. 2.532 0.612 0.0234 PR Table2: 5 3948. 2.690 0.619 0.0236 PR Table2: 6 4205. 2.955 0.617 0.0236 PR Table2: 7 4435. 3.209 0.615 0.0236 Fits for mass/efficiency for each line

line#, cm1,em1,ce1,de2 1 1.060 1.000 1.617 1.272 line#, cm1,em1,ce1,de2 2 1.010 1.000 1.464 2.204 line#, cm1,em1,ce1,de2 3 1.106 1.000 1.300 2.204 line#, cm1,em1,ce1,de2 4 1.030 5.000 1.405 2.204 line#, cm1,em1,ce1,de2 5 1.018 5.000 1.500 1.268 line#, cm1,em1,ce1,de2 6 1.118 1.000 1.500 1.227

Mass flow fit, lowBSR/highPR side: mass flow ratio at zero BSR: 1.04311 Mass flow fit, exponent of the mass flow line: 1.34742

Efficiency fit, lowBSR/highPR side: shape factor at low BSR: 1.50634 Efficiency fit, highBSR/lowPR side: zero intercept at high BSR: 1.97651 INFO Generating Turbine Map: HTT_HP64mm_70T_059AR

*WARN:Requested map max PR is greater than top of Max. Eff. Curve. Extrapolation will be used beyond PR= 3.20891

INFO Efficiency fit RMS error : 0.75006% INFO Mass flow fit RMS error : 9.784124E-03% INFO Efficiency fit normalized RMS error : 4.90877% INFO Mass flow fit normalized RMS error : 1.12122%

======================================================================== Processing TurbineSAEMap Object: HTT_HP64mm_70T_069AR

INFO Default Tref = 923K set for SAE files.

——————-speed, #points, speed var (%): 1 7 1973.13 0.0 speed, #points, speed var (%): 2 7 2793.18 0.0 speed, #points, speed var (%): 3 7 3450.63 0.0 speed, #points, speed var (%): 4 7 3780.04 0.0 speed, #points, speed var (%): 5 7 3942.08 0.0 speed, #points, speed var (%): 6 7 4205.63 0.0 speed, #points, speed var (%): 7 7 4438.30 0.0

*WARN:Input mass flow not monotonically increasing with PR at speed line(s): > 3780.04

> 3942.08 > 4205.63 > 4438.3

*WARN:Input efficiency has more than one local peak at speed line(s): > 1973.13 > 2793.18 > 3450.63 > 3780.04 > 3942.08 > 4205.63 Slope: fitted = 0.052

Turbine Ref. Diameter = 64.0000 (for reference only) PR Table2: 1 1973. 1.282 0.519 0.0165 PR Table2: 2 2793. 1.640 0.571 0.0225 PR Table2: 3 3451. 2.098 0.605 0.0251 PR Table2: 4 3780. 2.400 0.616 0.0257 PR Table2: 5 3942. 2.567 0.616 0.0257 PR Table2: 6 4206. 2.872 0.613 0.0257 PR Table2: 7 4438. 3.172 0.611 0.0255 Fits for mass/efficiency for each line

line#, cm1,em1,ce1,de2 1 1.005 5.000 1.557 1.302 line#, cm1,em1,ce1,de2 2 1.005 3.400 1.366 2.204 line#, cm1,em1,ce1,de2 3 1.108 1.000 1.657 1.832 line#, cm1,em1,ce1,de2 4 1.158 1.000 1.500 1.934 line#, cm1,em1,ce1,de2 5 1.200 1.000 1.358 1.320 line#, cm1,em1,ce1,de2 6 1.052 3.900 1.500 1.412 line#, cm1,em1,ce1,de2 7 0.000 0.000 0.000 1.275

Efficiency fit, lowBSR/highPR side: shape factor at low BSR: 1.49859 Efficiency fit, highBSR/lowPR side: zero intercept at high BSR: 1.81264 INFO Generating Turbine Map: HT T _HP 64mm_70T069AR

*WARN:Requested map max PR is greater than top of Max. Eff. Curve. Extrapolation will be used beyond PR= 3.17209

INFO Efficiency fit RMS error : 0.375634% INFO Mass flow fit RMS error : 1.49424E-02% INFO Efficiency fit normalized RMS error : 3.27207% INFO Mass flow fit normalized RMS error : 1.448%

INFO Loading data for CompressorMap/C117(71)T48AR060EI070_PS_CNG

======================================================================== INFO Speed line DEFAULT: contains speed points within 2% range

Speed lines found

——————-speed, #points, speed var (%): 1 7 59984.00 0.0 speed, #points, speed var (%): 2 7 74986.57 0.0 speed, #points, speed var (%): 3 7 89969.57 0.0 speed, #points, speed var (%): 4 7 105033.57 0.0 speed, #points, speed var (%): 5 7 120005.86 0.0 speed, #points, speed var (%): 6 9 135027.78 0.0 Generating Compressor Map

======================================================================== INFO Loading data for CompressorMap/HTT_C117_48T_070AR

======================================================================== INFO Speed line DEFAULT: contains speed points within 2% range

Speed lines found

======================================================================== INFO Allocating special part array memory...

INFO EM1 has a non-default forward/reverse pressure loss coefficient (there may be other offending parts). The internal model for bends will be disabled for such pipes and this value(s) will be used instead. Piston-cup depth/head-cup depth/hclear: 28.100 0.399 1.100

*WARN:EngCylinder or PistonCylinder: Piston Cup Object used in Flow Model but

None used in Conduction Model

Check the Flow Object and Wall Temperature Object in Cylinder 1 to verify

Piston-cup depth/head-cup depth/hclear: 28.100 0.399 1.100

*WARN:EngCylinder or PistonCylinder: Piston Cup Object used in Flow Model but

None used in Conduction Model

Check the Flow Object and Wall Temperature Object in Cylinder 2 to verify

Piston-cup depth/head-cup depth/hclear: 28.100 0.399 1.100

*WARN:EngCylinder or PistonCylinder: Piston Cup Object used in Flow Model but

None used in Conduction Model

Check the Flow Object and Wall Temperature Object in Cylinder 3 to verify

Piston-cup depth/head-cup depth/hclear: 28.100 0.399 1.100

*WARN:EngCylinder or PistonCylinder: Piston Cup Object used in Flow Model but

None used in Conduction Model

Check the Flow Object and Wall Temperature Object in Cylinder 4 to verify

Piston-cup depth/head-cup depth/hclear: 28.100 0.399 1.100

*WARN:EngCylinder or PistonCylinder: Piston Cup Object used in Flow Model but

None used in Conduction Model

Check the Flow Object and Wall Temperature Object in Cylinder 5 to verify

LEGEND: CNG, Engine speed 1400 rpm

INFO Part defining periodic process: CRANKSHAFT INFO Total number of pipe sub-volumes = 280 INFO Total number of flow volumes = 326

*WARN:Specified pipe section length is too short for given radius and angle Should be at least 50.4074 mm.

Pipe Part: Em1-1 Section # 2

*WARN:FLOWSPLIT expansion diameter is less than the attached pipe diame-ter

(Some connections are to bundles of multiple pipes)

Connection Exp Dia(1 pipe) Pipe Dia(1 pipe) Exp Dia(All) Pipe Dia(All) MahleBehr-NCG-20 221.9 237.9

*WARN:The following connections link a component with imposed wall temperature to one with calculated wall-temperature using "def"

conductivity. This may not be realistic because the fixed

temperature side acts as an infinite heat source/sink. Please confirm that the "Heat Conduction Flange" attribute has the desired value. ("ign"=no conduction, "def"=no resistance)

86 89

INFO Generating code for FMU Export INFO Building FMU...

INFO Model generated successfully.

INFO The outputs of the following Controls parts are not connected: diffP-1, Activate-1.

CASE COMPUTATIONS: Elapsed Time: 000:00:13.88 FINAL COMPUTATIONS: Elapsed Time: 000:00:15.15 END OF RUN