A method of predicting heat transfer coefficients in the cooling passages of NERVA and Phoebus-2 rocket nozzles
Predicting heat transfer coefficients in cooling passages of Nerva and Phoebus-2 rocket nozzles
Taylor, M. F.↗
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A method of predicting heat transfer coefficients in the cooling passages of NERVA and Phoebus-2 rocket nozzles.
Heat transfer coefficients in rocket nozzle cooling passages predicted by equation developed from hydrogen flow experiments
Taylor, M. F.↗
Numerical Prediction of Heat Transfer Coefficients during Xenon Tank Fill in Microgravity
NASA’s Gateway will serve as an orbital outpost to enable sustained human lunar exploration and facilitate access to destinations beyond the Moon. To maintain sustainable deep space missions, on-orbit propellant transfer and refueling must be realized. One of Gateway’s critical components, the Power and Propulsion Element (PPE), will use solar electric propulsion to perform attitude control maneuvers and orbit transfers. Xenon will be used as the propellant for the PPE and stored in a composite overwrapped pressure vessel (COPV). During the propellant refueling process, xenon will compress causing the fluid and tank walls to warm. There is concern that the bond between the tank liner and composite may degrade at elevated temperatures, along with the possibility of the tank exceeding design pressure. While on-orbit testing is being proposed, numerical models are being used to predict the thermal response and reduce risk during a refueling operation in microgravity where natural convection is diminished. Accurate numerical models may be used to inform decisions for follow-on testing, or design and operation of refueling architectures. Challenges of modeling xenon tank fill in microgravity include non-ideal gas behavior, operating near the fluid’s critical point, and lack of experimental flight data. This study presents a computational fluid dynamics (CFD) model with conjugate heat transfer that is used to predict averaged heat transfer coefficients during tank filling. At the anticipated injection flow rates, the CFD results showed that forced convection dominates in microgravity. Initial results from a multi-node Thermal Desktop model that assumed a purely conducting fluid (Nu=1) over-predicted the CFD fluid temperature and pressure, while a similar model allowing for natural convection under-predicted. The heat transfer coefficients predicted by CFD were implemented into the multi-node model, and the COPV thermal response from the two computational approaches were compared with good agreement.
Heat Transfer↗
DRA/NASA/ONERA Collaboration on Icing Research: Prediction of Airfoil Ice Accretion - Part 2
This report presents results from a joint study by DRA, NASA, and ONERA for the purpose of comparing, improving, and validating the aircraft icing computer codes developed by each agency. These codes are of three kinds: (1) water droplet trajectory prediction, (2) ice accretion modeling, and (3) transient electrothermal deicer analysis. In this joint study, the agencies compared their code predictions with each other and with experimental results. These comparison exercises were published in three technical reports, each with joint authorship. DRA published and had first authorship of Part 1 - Droplet Trajectory Calculations, NASA of Part 2 - Ice Accretion Prediction, and ONERA of Part 3 - Electrothermal Deicer Analysis. The results cover work done during the period from August 1986 to late 1991. As a result, all of the information in this report is dated. Where necessary, current information is provided to show the direction of current research. In this present report on ice accretion, each agency predicted ice shapes on two dimensional airfoils under icing conditions for which experimental ice shapes were available. In general, all three codes did a reasonable job of predicting the measured ice shapes. For any given experimental condition, one of the three codes predicted the general ice features (i.e., shape, impingement limits, mass of ice) somewhat better than did the other two. However, no single code consistently did better than the other two over the full range of conditions examined, which included rime, mixed, and glaze ice conditions. In several of the cases, DRA showed that the user's knowledge of icing can significantly improve the accuracy of the code prediction. Rime ice predictions were reasonably accurate and consistent among the codes, because droplets freeze on impact and the freezing model is simple. Glaze ice predictions were less accurate and less consistent among the codes, because the freezing model is more complex and is critically dependent upon unsubstantiated heat transfer and surface roughness models. Thus, heat transfer prediction methods used in the codes became the subject for a separate study in this report to compare predicted heat transfer coefficients with a limited experimental database of heat transfer coefficients for cylinders with simulated glaze and rime ice shapes. The codes did a good job of predicting heat transfer coefficients near the stagnation region of the ice shapes. But in the region of the ice horns, all three codes predicted heat transfer coefficients considerably higher than the measured values. An important conclusion of this study is that further research is needed to understand the finer detail of of the glaze ice accretion process and to develop improved glaze ice accretion models.
Wright, William B.↗
The influence of a wall function on turbine blade heat transfer prediction
The second phase of a continuing investigation to improve the prediction of turbine blade heat transfer coefficients was completed. The present study specifically investigated how a numeric wall function in the turbulence model of a two-dimensional boundary layer code, STAN5, affected heat transfer prediction capabilities. Several sources of inaccuracy in the wall function were identified and then corrected or improved. Heat transfer coefficient predictions were then obtained using each one of the modifications to determine its effect. Results indicated that the modifications made to the wall function can significantly affect the prediction of heat transfer coefficients on turbine blades. The improvement in accuracy due the modifications is still inconclusive and is still being investigated.
Whitaker, Kevin W.↗
Evaluation of Heat Transfer Coefficient from Velocity Distribution in Boundary Layer
This paper describes a method of evaluating heat transfer coefficient from a velocity distribution in the boundary layer. The power law velocity profile and universal velocity distribution in a smooth pipe have been used to evaluate the wall shear stress, and the heat transfer coefficient was evaluated from the wall shear stress using the Chilton-Colburn analogy. The predicted pressure drop was compared with the pressure drop computed by the Colebrook equation, and the predicted heat transfer coefficient was compared with the heat transfer coefficient calculated by the Dittus-Boelter equation. The accuracy of pressure drop prediction was within 1%, and the accuracy of heat transfer coefficient prediction was within 8%. This method has the potential to use a Navier-Stokes based CFD solution to evaluate the heat transfer coefficient in nodal or network flow codes.
Heat Transfer Coefficient↗
Evaluation of Heat Transfer Coefficient from Velocity Distribution in Boundary Layer
This paper describes a method of evaluating heat transfer coefficient from a velocity distribution in the boundary layer. The power law velocity profile and universal velocity distribution in a smooth pipe have been used to evaluate the wall shear stress, and the heat transfer coefficient was evaluated from the wall shear stress using the Chilton-Colburn analogy. The predicted pressure drop was compared with the pressure drop computed by the Colebrook equation, and the predicted heat transfer coefficient was compared with the heat transfer coefficient calculated by the Dittus-Boelter equation. The accuracy of pressure drop prediction was within 1%, and the accuracy of heat transfer coefficient prediction was within 8%. This method has the potential to use a Navier-Stokes based CFD solution to evaluate the heat transfer coefficient in nodal or network flow codes.
Heat Transfer Coefficient↗
Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission
To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.
Cameron J. Hines↗
Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission
To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.
zero boil-off↗
Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission
To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.
zero boil-off↗
Modeling and Simulation of Tank Pressure Control using Zero-Boiloff Active Thermal Control for LOXSAT Technology Demonstration Mission
To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.
zero boil-off↗
Prediction of friction and heat-transfer coefficients with large variations in fluid properties
Prediction of friction and heat transfer coefficients with large variations in fluid properties
Taylor, M. F.↗
Uncertainty Analysis on Heat Transfer Correlations for RP-1 Fuel in Copper Tubing
NASA is studying kerosene (RP-1) for application in Next Generation Launch Technology (NGLT). Accurate heat transfer correlations in narrow passages at high temperatures and pressures are needed. Hydrocarbon fuels, such as RP-1, produce carbon deposition (coke) along the inside of tube walls when heated to high temperatures. A series of tests to measure the heat transfer using RP-1 fuel and examine the coking were performed in NASA Glenn Research Center's Heated Tube Facility. The facility models regenerative cooling by flowing room temperature RP-1 through resistively heated copper tubing. A Regression analysis is performed on the data to determine the heat transfer correlation for Nusselt number as a function of Reynolds and Prandtl numbers. Each measurement and calculation is analyzed to identify sources of uncertainty, including RP-1 property variations. Monte Carlo simulation is used to determine how each uncertainty source propagates through the regression and an overall uncertainty in predicted heat transfer coefficient. The implications of these uncertainties on engine design and ways to minimize existing uncertainties are discussed.
Driscoll, E. A.↗
Determination of optimum fin profile for a zero-G capillary drained condenser
This paper presents the analytical formulation and numerical results for heat transfer in a high heat flux condenser that relies on capillary flow along shaped fins (Gregorig surfaces) and a drainage network embedded in the condenser walls. Results are shown for a variety of fin profile shapes in order to show the geometric trade-offs involved in seeking a maximum effective heat transfer coefficient for the fin. Predictions of the model show excellent agreement with previously reported measurements for steam. Based on this work, a profile has been selected for a 2 kW ammonia condenser currently under development for use in space. In that design the fin half width is 0.5 mm and the model predicts a heat transfer coefficient referred to the base of the fin of 9 W/sq cm deg C for a heat flux of 10/W sq cm at the base.
Mccormick, John A.↗
A comparison of experimental heat transfer coefficients in a nozzle with analytical predictions from Bartz's methods for various combustion chamber pressures in a solid propellant rocket motor
Comparison of solid propellant nozzle heat transfer coefficients with predicted data
Smith, D. M.↗
A comparison of experimental heat-transfer coefficients in a nozzle with analytical predictions from Bartz's methods for various combustion chamber pressures in a solid propellant rocket motor
Comparison of experimental heat transfer coefficients in a nozzle with analytical predictions for various combustion chamber pressures in solid rocket propellant engine
Smith, D. M.↗
Heat-Transfer and Pressure Measurements from a Flight Test of the Third 1/18-Scale Model of the Titan Intercontinental Ballistic Missile up to a Mach Number of 3.86 and Reynolds Number per Foot of 23.5 x 10(exp 6) and a Comparison with Heat Transfer
Heat-transfer and pressure measurements were obtained from a flight test of a 1/18-scale model of the Titan intercontinental ballistic missile up to a Mach number of 3.86 and Reynolds number per foot of 23.5 x 10(exp 6) and are compared with the data of two previously tested 1/18-scale models. Boundary-layer transition was observed on the nose of the model. Van Driest's theory predicted heat-transfer coefficients reasonably well for the fully laminar flow but predictions made by Van Driest's theory for turbulent flow were considerably higher than the measurements when the skin was being heated. Comparison with the flight test of two similar models shows fair repeatability of the measurements for fully laminar or turbulent flow.
Graham, John B., Jr.↗
Temperature distribution in internally heated walls of heat exchangers composed of nonnuclear flow passages
In the walls of heat exchangers composed of noncircular passages, the temperature varies in the circumferential direction because of local variations of the heat-transfer coefficients. A prediction of the magnitude of this variation is necessary in order to determine the region of highest temperature and in order to determine the admissible operating temperatures. A method for the determination of these temperature distributions and of the heat-transfer characteristics of a special type of heat exchanger is developed. The heat exchanger is composed of polygonal flow passages and the passage walls are uniformly heated by internal heat sources. The coolant flow within the passages is assumed to be turbulent. The circumferential variation of the local heat-transfer coefficients is estimated from flow measurements made by Nikuradse, postulating similarity between velocity and temperature fields. Calculations of temperature distributions based on these heat-transfer coefficients are carried out and results for heat exchangers with triangular and rectangular passages are presented.
Eckert, E R G↗