A system-level thermal management aircraft model has been developed in a multidisciplinary modeling and simulation environment. Individual subsystem ...
AIAA 2011-5971
47th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 31 July - 03 August 2011, San Diego, California
Generic Aircraft Thermal Tip-to-Tail Modeling and Simulation Rory A. Roberts* and Scott M. Eastbourn† Wright State University, Dayton OH 45435 and Adam C. Maser‡ Georgia Institute of Technology, Atlanta GA 30332
A system-level thermal management aircraft model has been developed in a multidisciplinary modeling and simulation environment. Individual subsystem models developed exclusively in MATLAB/Simulink, representing the vehicle dynamics, the propulsion, electrical power, and thermal systems, and their associated controllers, are combined to investigate the thermal management issues of a typical long range strike platform. A thermal tip-to-tail model allows conceptual design trade studies of various subsystems and can quantify performance gains across the aircraft. The final result is an aircraft that is thermally optimized at the system-level, rather than at the subsystemlevel. In addition, the model has been built without the aid of proprietary data, thereby allowing the distribution of the tool to a variety of conceptual design groups and researchers. Special attention has been paid to the development of transient component models within the thermal management systems, including the Integrated Power Package, heat exchangers, fuel and oil pumps, and the engine oil heat rejection. As a result, the thermal and power challenges that face modern aircraft can be addressed, potentially increasing the performance capabilities of future aircraft. Preliminary simulation results are discussed with a specific focus on the thermal challenges encountered during reduced engine power mission segments.
Nomenclature A
dt h
N
P
= = = = = = = = = = = = = = = =
surface area specific heat capacity at constant pressure diameter time step energy transfer rate convective heat transfer coefficient thermal conductivity mass flow rate mass rotational speed efficiency Nusselt number pressure density Prandtl number heat transfer rate
*
Assistant Professor, Department of Mechanical and Materials Engineering, AIAA Member. Graduate Research Assistant, Department of Mechanical and Materials Engineering, Non-AIAA Member. ‡ Graduate Research Assistant, School of Aerospace Engineering 0150, AIAA Student Member. †
1 American Institute of Aeronautics and Astronautics Copyright © 2011 by Rory Roberts. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
R Re T V
= = = =
universal gas constant Reynolds number temperature volume
I. Introduction EXT-GENERATION aircraft will face an escalating number of thermal challenges. Aircraft are utilizing moreelectric components and require increased power generation as a result. In fact, the power system demands have grown by nearly an order of magnitude to support these new high-power loads, increasing the internal heat generated by the aircraft that must be removed by the thermal management system (TMS). 1 Simultaneously, these thermal systems have been restricted significantly. Modern aircraft must maintain low radar observability. As such, the ram air inlet areas have been greatly condensed, reducing the effectiveness of a vital heat sink. In addition, new composite aircraft skins have reduced the amount of heat that can be convected to the environment. The combination of these characteristics has increased the challenges faced by modern TMSs. In order to assist in the mitigation of these thermal challenges, new modeling and simulation tools need to be developed. 1 Conceptual design groups have traditionally designed aircraft at the subsystem-level. These subsystems, such as propulsion, electrical, and thermal management, are often optimized without consideration of vehicle-level interactions among the other subsystems. As a result, the final aircraft design may not truly be optimized. Vehiclelevel analysis of subsystem interactions could result in significant performance gains across the aircraft, potentially improving the overall effectiveness of future platforms. The development of a modeling and simulation tool allows these performance gains to be quantified. In addition, developing a generic modeling tool without proprietary subsystem data that is aircraft specific will allow collaboration among design groups, improving the utility of the tip-to-tail model. Similar tip-to-tail efforts have already been conducted.2-4 Many of these models, however, were primarily steady-state and lacked important dynamic effects. Also, some of the previous efforts contained proprietary subsystem models. The current effort has developed a new modeling tool without proprietary data and exclusively in MATLAB/Simulink. In addition, special attention is paid to capture transient behaviors, including the Integrated Power Package (IPP), heat exchangers, fuel and oil pumps, and engine oil heat rejection. Although the emphasis on transient models is primarily based on increasing overall model fidelity through the prediction of important dynamic effects, these models also provide significant computational improvement by eliminating the need for to solve for complex algebraic constraints
N
II. Outline of Non-Proprietary System-Level Model A system-level thermal management aircraft model has been developed in a multidisciplinary modeling and simulation environment. Individual subsystem models developed in MATLAB/Simulink are combined to investigate the thermal management issues of a notional long range strike platform. Figure 1 shows a Simulink screenshot of the vehicle-level model. The first subsystem of interest in Figure 1 is the Aircraft Vehicle System (AVS) model, represented by the large blue block at the bottom center of the screenshot. The AVS model contains the mission profile data as well as the forces acting on the aircraft, such as weight, drag, and lift. The mission profile consists of predefined waypoints for Mach number and altitude at various mission times. The AVS model calculates a required thrust to maintain the desired mission profile and relays this thrust to the engine model. The engine model is represented by the green block in the upper left corner of Figure 1. The aircraft in this effort utilizes four engines, each producing a maximum sea-level standard thrust of 20,000 lb., to meet the thrust demands of the mission. The engine controllers alter the fuel flow to the engine in order to produce the thrust demanded by the AVS model. The engine model also interacts with the vehicle’s TMS, which is divided into two parts: the Adaptive Power and Thermal Management System (APTMS) and the Fuel Thermal Management System (FTMS). Both the APTMS and FTMS models are represented by red blocks on the right side of Figure 1. The APTMS model contains the Integrated Power Package (IPP), an air cycle machine that cools the cockpit, air-cooled avionics, and liquid-cooled avionics. A majority of the thermal loads within the APTMS ultimately reject heat to the engine fan bypass air stream. The remaining APTMS heat loads are transferred to the FTMS and are ultimately rejected to the fuel. Additionally, the FTMS model removes heat from the engine shaft bearings, oil pumps, and fuel pumps. In search of increased model fidelity, new models have been developed for the oil and fuel pumps, capturing the time-variant heat rejection from each pump.
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Figure 1. System-Level Simulink Model. The FTMS also contains dynamic models for the engine oil heat rejection. The two orange blocks in Figure 1 represent the electrical systems. The Robust Electrical Power System (REPS) and High Power Electric Actuation System (HPEAS) are solely modeled from a thermal standpoint. The only contributions from these systems are predefined heat loads, which are a function of mission time. Components of the HPEAS and REPS models include the actuators, generator, and avionics heat loads. The magenta block of Figure 1 contains all of the necessary controllers. The model includes controllers for several control valves within the APTMS and FTMS, as well as performance monitoring for TMS temperatures and set points. The final two light blue blocks in the upper left hand corner of Figure 1 represent the Environment and Analysis components of the system. The Environment block defines the atmosphere and the Analysis block enables the user to quickly plot the simulation results. Each subsystem model is designed to interact with a generic spreadsheet that contains all of the pertinent subsystem variables. The end user is able to update physical parameters quickly and can include their own proprietary data if desired. Parameters of interest, such as temperatures, control valve positions, flow rates, and pressures, are stored as variables inside of the system controller block in Figure 1. They are then sent to the MATLAB workspace and are plotted upon the completion of each simulation. III. Transient Model Developments As previously mentioned, special attention has been paid to the development of transient models. In addition to increasing model fidelity, dynamic modeling can be used to simplify the simulation from a computational standpoint as well as reduce simulation runtime. Improved models have been developed for the IPP, heat exchangers, fuel and oil pumps, and engine oil heat rejection. A brief overview of each of these four models is presented. A. Integrated Power Package The IPP model, shown below in Figure 2, is one of the major contributions to the new tip-to-tail model. The IPP is located within the APTMS architecture and is responsible for cooling the air cooled avionics, cockpit, and liquid cooled avionics. Consisting of a power turbine, closed loop compressor, and closed loop turbine, the IPP uses high pressure bleed air from the main engine compressor to power a closed loop air cycle. The IPP speed control valve, located between the IPP power turbine and the main engine compressor, regulates the mass flow of high pressure bleed air from the main engine compressor to the IPP. When the control valve is fully open, all available bleed air is sent to the IPP’s power turbine and the cooling capacity of the closed loop air cycle is maximized. As the control valve closes, overall mass flow of bleed air to the IPP’s power turbine is reduced and the cooling capacity falls. The IPP speed control valve is operated to maintain a POA oil temperature of 60°F in the liquid cooled avionics loop. A 3 American Institute of Aeronautics and Astronautics
PI controller measures the actual temperature of oil entering the liquid cooled avionics, compares this value to the set point value of 60°F, and then operates the IPP speed control valve accordingly until the difference between the actual and set point temperatures is zero. As the results will later show, the relationship between this control valve and the engine plays a crucial role in the overall performance of the thermal management systems. In order to capture dynamics within the IPP model, two different approaches are employed. First, an intercomponent volume method is used by modeling a plenum volume after each of the different turbomachinery models. In the case of the IPP, plenum volumes are placed after the power turbine, closed loop compressor, and closed loop turbine. The turbomachinery models contain generic performance maps that can easily be based on experimental data. These maps are a function of shaft speed, pressure ratio, and inlet conditions, such as temperature and molar composition of the incoming air, and output a corrected mass flow. With the incoming and outgoing mass flows of the plenum volume known, the dynamic pressure of the plenum volume can be calculated via integration of the ideal gas law, as shown by Eq. (1). (1) Secondly, the IPP model considers shaft inertia. Any changes in torque to the IPP shaft will vary the shaft speed. By considering the shaft inertia, however, this variation does not occur instantaneously. This time delay is captured in the model, once again demonstrating dynamic capabilities.
Figure 2. Transient IPP Simulink Model. B. Heat Exchangers New dynamic heat exchanger models have also been developed for use in the thermal tip-to-tail model. While easy to use and comprehensive, the heat exchanger models used in previous efforts were predominately steady-state. In steady-state heat exchangers, all energy is immediately transferred from one fluid to the other. In reality, the heat exchanger medium must be considered, as the heat exchanger mass stores and supplies heat depending on the perturbation. In order to create transient models, the material characteristics of the heat exchanger itself are now considered. For this initial effort, the heat exchanger is divided into three separate nodes. These nodes allow the user to obtain more detailed temperature distributions through the heat exchanger medium as well as create a counterflow heat exchanger. Each of the three nodes contains blocks that are used to model energy flow within the heat exchanger. A block is present for each of the two fluid streams and a third block represents the heat exchanger medium. The energy balance calculations completed in each of the fluid streams are shown in Eqs. (2) to (5). These outgoing fluid temperatures are sent to the heat exchanger medium block. Similarly, the heat exchanger medium block computes an energy balance and relays the heat exchanger temperature to the fluid stream blocks, shown by Eq. (6). These temperatures are made transient through the integration at the end of each energy balance calculation. (2) (3) 4 American Institute of Aeronautics and Astronautics
(4) (5)
(6) Specifications for the actual heat exchangers used in the TMSs, including physical dimensions and heat exchanger types, have not been made available to the authors. As a result, several assumptions have been made, specifically the types of heat exchangers used. For the sake of simplicity, all heat exchangers are modeled as tube and shell type heat exchangers. Each heat exchanger model is masked and calls for the user to input key physical parameters, such as tube diameter, heat exchanger material capacity, heat exchanger mass, heat exchanger volume, and the heat exchanger cell area. Each of these parameters can be easily modified to match proprietary data should it become available. In search of additional fidelity, the heat transfer coefficients shown in Eqs. (2) to (5) have been made fluid dependent. Using physical parameters specified by the user, the Reynolds number is calculated for each stream. The Nusselt number is calculated using the Dittus-Boelter equation, Eqs. (7) and (8). The convective heat transfer coefficient is then derived using the thermal conductivity and diameter of the heat exchanger tube, as shown by Eq. (9). (7) (8) (9) The heat exchanger mask prompts the user to select a fluid type for both the hot and the cold streams. Another feature of these new heat exchanger models is that the user no longer needs to specify which of the two fluids is cold and which is hot. While this may seem like a trivial improvement, it does simplify the application of these new models. Instead of specifying the temperature classification of each fluid, the fluid selection is linked to polynomial function blocks that determine fluid properties as a function of temperature throughout the models. C. Fuel and Oil Pumps A new quasi-steady-state pump model has also been developed, meaning the pump inertia is not considered. This model is used for both the engine fuel and oil pumps in the current study. The model is designed so that pump speed is directly related to engine speed through an assumed constant gear ratio. In addition, the change in pump speed also leads to the generation of heat that is then rejected to the fluid stream. The TMS is designed to dissipate these time-variant heat loads. In fact, the pumps are a significant heat source for the FTMS loops. The overall model fidelity has been increased by including these transient heat loads. The performance characteristics of these pumps are represented by generic maps stored in a spreadsheet. This simplification is sufficient for determining the transient heat loads of the pumps throughout the mission and is the standard industry approach. This data can be populated with experimental pump data by the user. These maps contain mass flow rate data as a function of pressure ratio and rotation speed as shown in Eq. (10). (10) This mass flow is used to calculate the pump work, which then leads to the heat generation through Eq. (11). The fluid temperature rise is the computed using the energy balance of Eq. (12).
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(11) (12) D. Engine Oil Heat Rejection Similarly to the fuel and oil pumps, the heat rejected directly from the engine to the oil can be significant. Previous efforts have treated this through a lookup table as has been done with the REPS and HPEAS. However, the engine oil heat load is physically modeled in this effort. This is done by creating an oil loop that interacts with four heat transfer nodes representing the engine fan, compressor, low-pressure turbine, and high-pressure turbine shaft bearings. This oil loop absorbs heat from each of these bearings in parallel and then transfers this heat to the fuel stream through an additional heat exchanger. The heat transfer at each of these four nodes is determined with Eq. (12) using the temperature difference between the oil and the bearing node. The temperature data at each engine station comes directly from the engine model. (12) This temperature data, along with known heat transfer values for various mission segments, enabled the initial selection of the four heat transfer coefficients. IV. Simulation Results The newly developed non-proprietary aircraft tip-to-tail model is run in Simulink using the mission profile illustrated in Figure 3. For this initial effort, a mission profile of just over two hours is used. Physical parameters for all subsystem models are estimated based on engineering judgment and non-proprietary data. The purpose of the current research is not to replicate an actual aircraft, but rather to demonstrate the potential of the new modeling and simulation tool. The simulation is integrated in Simulink using the ode45 Runge-Kutta solver with variable time stepping.
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Figure 3. Aircraft Mission Profile used for Simulation. The mission profile selected consists of a generic high-low-high mission. As previously discussed, the AVS model calculates a required thrust based on the desired Mach number and altitude. This demanded thrust is sent to the engine controller which alters the fuel flow to produce the needed thrust. The AVS model also operates the appropriate control surfaces to ensure the aircraft follows the desired mission profile altitudes. Notice the large and rapid descents of the aircraft at mission times of approximately 30 min. and 70 min. As the results will show later in the section, these large descents are detrimental to the performance of the TMS. 6 American Institute of Aeronautics and Astronautics
Figure 4 illustrates the demanded thrust compared to the actual thrust. It is worth noting that the actual thrust saturates to zero thrust at a mission time of approximately 65 min. This mission time corresponds to a rapid descent in the aircraft altitude. In order to prevent the aircraft from exceeding the specified Mach number, the demanded engine thrust becomes negative. To prevent the engine controller from attempting to produce a negative thrust, a saturation limit of 0 lb. is placed on the engine thrust. Other than the two large descents, the engine controller maintains the actual thrust sufficiently close to the demand. 60 Actual Demand 50
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Figure 4. Aircraft Actual and Demanded Thrust Profiles. As previously mentioned, key parameters are tracked throughout the mission in MATLAB’s workspace. These temperatures, control valve positions, flow rates, and pressures are plotted in order to determine the effectiveness of the FTMS and APTMS subsystems. Temperatures in particular are compared to previously determined limits in order to show compliance as the mission progresses. These results are summarized here with special attention paid to the TMS challenges during large aircraft altitude descents. Figure 5 and Figure 6 display two different plots related to the IPP. The first plot compares the actual and demanded IPP rotational speeds, while the second plot illustrates the actual and demanded mass flows to the IPP power turbine along with the corresponding control value position.
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Figure 5. IPP Rotational Speed Profile.
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Figure 6. IPP Speed Control Valve Profile. Figure 7 shows the liquid cooled avionics temperature profile along with its controller set point. The desired temperature for the oil entering the liquid cooled avionics heat exchanger is 60 °F, represented by the blue line. This plot shows that the liquid cooled avionics temperature limit is exceeded near mission times of 30 min. and 80 min. At those same mission times, Figure 5 shows that the actual IPP speed fails to maintain the demanded speed. This can be explained by the control valve mass flows shown in Figure 6. Even with the control valve position fully open, the actual mass flow to the IPP power turbine fails to meet the demanded mass flow.
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Figure 7. Liquid Cooled Avionics Profile. Comparing these results to the mission profile from Figure 3, it is seen that the IPP speed fails to match the demand at locations where the aircraft altitude is dropping rapidly. In addition, as the altitude is dropping, the Mach number is also dropping. In order to reduce altitude and Mach number simultaneously, engine power must be reduced. By reducing engine power, the mass flow of high pressure bleed air from the engine compressor to the IPP power turbine is reduced as well because there is insufficient pressure in the engine compressor.
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The FTMS heat exchanger bypass control valve also begins to close when the IPP speed control valve is fully opened, but additional cooling is needed for the liquid cooled avionics. As Figure 8 illustrates, the heat exchanger bypass valve is fully saturated (closed) near the critical mission times of 30 min. and 70 min. Even with the APTMS rejecting as much heat as possible to the FTMS, the liquid cooled avionics temperature still exceeds the limit.
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Figure 8. FTMS Heat Exchanger Bypass Control Valve Profile.
Finally, the temperature and mass of fuel in the fuel tanks is shown in Figure 9. The fuel mass has a relatively constant reduction while the temperature increases. This is to be expected to some extent; as fuel is burned and the mass in the tank is reduced, less fuel is available to absorb heat. Since fuel is one of the largest heat sinks for the TMS, the temperature increases for the duration of the mission. It is important to note that this fuel temperature must be controlled in order to prevent coking and to ensure sufficient component cooling. 4
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Figure 9. Fuel Variation throughout Mission.
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V. Conclusion A system-level thermal management aircraft model has been developed in a multidisciplinary modeling and simulation environment using MATLAB/Simulink. The generic model has been built without the aid of proprietary data, thereby allowing the distribution of the tool to a variety of conceptual design groups and researchers. Such a tool enables a system-level optimization for the aircraft’s TMS. Special attention was also paid to the development of transient component models in an effort to increase model fidelity. As these preliminary results have shown, the notional TMS designs failed to maintain the temperature requirements of the aircraft. Specifically, the TMS failed under mission segments of reduced engine power, such as large descents that simultaneously occur with a reduction in vehicle speed. Without the transient interactions captured between the engine and the TMS system, the shortcomings in the TMS system would have not been captured. The non-equilibrium interactions between the subsystems are a critical aspect to capture in order to ensure that the vehicle design and controls can maintain operation within the design constraints. The operation of the subsystem outside the design constraints would not have been captured with steady-state/equilibrium models. Future studies will focus on TMS design parameters tradeoffs in order to minimize these thermal challenges. Acknowledgments The authors wish to acknowledge Mitch Wolff, Greg Russell, and Mark Bodie from the Air Force Research Laboratory for their assistance with this research. References 1
Walters, E.A., Iden, S., McCarthy, K., et al., “INVENT Modeling, Simulation, Analysis, and Optimization,” AIAA 2010287, 2008. 2 Bodie, M., Russell, G., McCarthy, K., Lucas, E., Zumberge, J., and Wolff, M., “Thermal Analysis of an Integrated Aircraft Model”, AIAA 2010-288, 2010. 3 McCarthy, K., Walters, E., Hetzel, A., et al., “Dynamic Thermal Management System Modeling of a More Electric Aircraft,” SAE 2008-01-2886, 2008. 4 Maser, A.C., Garcia, E., and Mavris, D.N., “Facilitating the Energy Optimization of Aircraft Propulsion and Thermal Management Systems through Integrated Modeling and Simulation,” SAE 2010-01-1787, 2010.
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