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At least 73 records · Page 4

Self-Pressurization of a Flightweight, Liquid Hydrogen Tank: Simulation and Comparison with Experiments

This paper presents ANSYS Fluent simulation results and analysis for self-pressurization of a flightweight, cryogenic, liquid hydrogen tank in 1-g. These results are compared with experimental data, in particular, pressure evolution and temperature measurements at a set of sensors. The simulations can be analyzed to identify and quantify heat flows in the tank. Heat flows change over time and influence the self-pressurization process. The initial rate of self-pressurization is sensitive to the initial temperature profile near the interface. Uncertainty in saturation pressure data and the accuracy of experimental measurements complicate simulation of self-pressurization. Numerical issues encountered, and their resolution, are also explained.

computational fluid mechanics↗

Liquefaction Study of Gaseous Oxygen Inside Mars Ascent Vehicle Propellant Tank

The in-situ production of propellants for Mars missions will utilize carbon dioxide (CO2) in the Mars atmosphere to produce oxygen. The oxygen then needs to be cooled, liquefied, and stored to be available for Mars ascent propulsion, which could be up to 2 years after liquefaction starts. Recent investigations have demonstrated the feasibility of both achieving zero boiloff and controlling the pressure of oxygen within a tank using high-efficiency reverse turbo-Brayton-cycle cryocoolers. A tube-on-tank configuration is being studied in this work. The cooling fluid circulating in the cryocooler system is routed through a network of cooling tubes on the oxygen tank. The oxygen gas produced from the in-situ production process is introduced into the chilled tank. A series of analysis of this configuration has been performed to investigate the liquefaction rate inside the tank, the thermal gradient near the top of the tank where the oxygen gas feeding tubing is located. The analyses include 2D axisymmetric CFD analysis using ANSYS Fluent, 1D thermal analysis using Matlab, and 3D thermal analysis using MSC Patran/pthermal. These three models correlate and validate each other.

Liquefaction↗

Meander-Like Shape Optimization of the Stator-Rotor Platform Cavity

Recent progress in additive manufacturing has enabled opportunities to explore novel stator rim geometries which can be implemented to improve cooling strategies in turbomachinery. This paper presents a simplified stationary geometry optimization strategy to produce enhanced stator-rotor cavity sealing and highlights main driving mechanisms.The stator and rotor rims were designed using a design strategy based on inspiration from the meandering of rivers. A minimum thickness of 2 millimeters was maintained throughout the cavity to ensure a practical implementation. The computational domain comprised of the stator outlet, hub disk leakage cavity, and rotor platform was meshed using NUMECA Int. package, Hexpress. The numerical analysis required 3D Unsteady Reynolds Average Navier-Stokes to replicate vorticial structures using Ansys Fluent. The operating conditions were representative of engine-like conditions, exploring a wide range of mass flow ratios from 1 to 3 percent. The optimization yielded designs that provide 30 percent reduction in rear platform temperature while minimizing coolant mass flow. The applicability of the design was compared against 3D sector in both stationary and in rotation.

Shape Optimization↗

Risk Analysis Associated with Loss of Toxic Gases During Orion Landing and Recovery Operations

Mission, landing and recovery operations for the Orion crew module involve reentry into the Earth's atmosphere and the deployment of three Nomex parachutes to slow the descent before landing along the west coast of the United States. Orion may have residual fuel (hydrazine, N2H4) or coolant (ammonia, NH3) on board which are both highly toxic to crew in the event of exposure. These risks were evaluated using a first principles analysis approach through fluid dynamics modeling. Plume calculations were first performed with the ANSYS Fluent computational fluid dynamics code. Data were then extracted at locations relevant to crew safety such as the snorkel fan inlet and the egress hatch. Mixing calculations were performed to quantify exposure concentrations within the crew bay before and during egress and departure. Finally, results included herein were used to inform the Orion post-landing Concept of Operations (ConOps) so that strategies could be formulated to maintain crew safety in the event of the loss of fuel or coolant.

Orion↗

Application of Extended Messinger Models to Complex Geometries

Since, ice accretion can significantly degrade the performance and the stability of an airborne vehicle, it is imperative to be able to model it accurately. While ice accretion studies have been performed on airplane wings and helicopter blades in abundance, there are few that attempt to model the process on more complex geometries such as fuselages. This paper proposes a methodology that extends an existing in-house Extended Messinger solver to complex geometries by introducing the capability to work with unstructured grids and carry out spatial surface streamwise marching. For the work presented here commercial solvers such as STAR-CCM+ and ANSYS Fluent are used for the flow field and droplet dispersed phase computations. The ice accretion is carried out using an in-house icing solver called GT-ICE. The predictions by GT-ICE are compared to available experimental data, or to predictions by other solvers such as LEWICE and STAR-CCM+. Three different cases with varying levels of complexity are presented. The first case considered is a commercial transport airfoil, followed by a three-dimensional MS(1)-317 swept wing. Finally, ice accretion calculations performed on a Robin fuselage have been discussed. Good agreement with experimental data, where applicable, is observed. Differences between the ice accretion predictions by different solvers have been discussed.

Gupta, Avani↗

Modeling and Analysis of Stirling Power Convertors

NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors for Dynamic Radioisotope Power Systems (DRPS). American Superconductor (AMSC) and Sunpower Inc. are the two of the firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively, to meet DRPS requirements. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics.

Stirling↗

Modeling and Analysis of Stirling Power Convertors

NASA Glenn’s Thermal Energy Conversion Branch is supporting the development of the next generation free-piston Stirling power convertors for Dynamic Radioisotope Power Systems (DRPS). American Superconductor (AMSC) and Sunpower Inc. are the two of the firms under contract to develop the Flexure Isotope Stirling Convertor (FISC) and the Sunpower Robust Stirling Convertor (SRSC), respectively, to meet DRPS requirements. To comprehend and forecast convertor performance, Sage, ANSYS® Maxwell, and ANSYS® Fluent were used to model the Stirling thermodynamic cycle, alternator electromagnetics, and piston and displacer dynamics.

Stirling↗

EMU CO2 Washout Comparative Assessments for the HAB/HAP-E in support of EVA 80

After water was reported in the EMU helmet during ISS US EVA-80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) model was developed to assess the effect that the absorbent material has on the concentration of carbon dioxide in the EMU helmet. Within a small, closed volume such as a helmet, some amount of the carbon dioxide produced by the suit-wearer will be re-inhaled before being cleared from the oral-nasal region. The ability of the suit to remove carbon dioxide is referred to as CO2 washout. Pathological levels of inhaled CO2 (hypercapnia) are associated with dizziness, fatigue, and headaches. The CFD model was built in ANSYS Fluent and adapted to assess CO2 washout in a variety of scenarios grouped into three categories: purge cases, varying metabolic rates, and varying sorbent material configurations. The presence of the sorbent material did not prove detrimental to CO2 washout in the helmet.

Moses Navarro↗

Assessment of HAB Particulate Tracing in EMU Helmet in Support of Eva 80

After water was reported in the EMU helmet during ISS US EVA-80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) analysis was done tracing the paths of particulates potentially generated by the absorbent material. The objective was to characterize the risk these particulates pose for inspiration and eye irritation by tracking what percentage of particulates contact the suit-wearer’s face, eyes, or mouth. The model consisted of a human mannikin head and torso inside the EMU suit and helmet. The software used was ANSYS Fluent, with discrete phase modeling enabled to generate and track the particulates within the ventilation flow of the helmet.

Abigail Rose Baukus↗

Validation of CFD Model Prediction of Flow Boiling Regime Transitions during LN2 Line Chilldown

Propellant storage and transfer during future long-duration missions will involve fuel depot operations in which a donor depot tank is used to fill a receiver spacecraft tank with a cryogenic propellant. Prior to the on-Orbit filling operation both the receiver tank and the transfer line must be cooled. The line chilldown process involves transition between boiling regimes in microgravity that will be quite different from their 1g ground-based counterparts. Since the cryogenic propellant itself will be used to perform the chilldown process, the time constants to cool the wall and the amount of fuel that will be used become important design considerations. In this light, the focus of the present work is to understand the flow boiling characteristics of a cryogenic fluid, namely, liquid nitrogen, during the chill-down of a transfer line using CFD modelling and simulations. The cryogenic chill-down process involves different flow boiling regimes: film boiling, transition boiling and nucleate boiling. The prediction of transition between these regimes in a CFD framework is new and challenging. The present work addresses this challenge by employing a volume-of-fluid (VOF) based methodology with Lee phase change model to predict the film boiling regime of the chill-down process in ANSYS Fluent®. The transition and nucleate boiling regimes are predicted by incorporating a sub-grid model developed by Craft Tech that accounts for bubble nucleation, growth, shedding frequency, and departure diameter. The sub-grid model is implemented into Fluent via a user-defined function for wall-fluid heat flux calculations. The sub-grid model is similar in formulation to the well-known Rensselaer Polytechnic Institute (RPI) boiling model. The model constants are tested for different operating conditions and designated values are reported. The CFD model is validated against published experimental data for liquid nitrogen chill-down of a heated stainless-steel pipe in 1g. Predicted results showing good agreement of wall temperature, rewetting temperature, and transition between film and nucleate boiling with the experimental measurements are presented and discussed.

Boiling regimes↗

CFD Model Development of a Cryogenic Storage Tank Self-Pressurization in Normal Gravity and Validation against SHIIVER Experiment

Two-phase flow and heat transfer simulations with interfacial phase change of the Structural Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) self-pressurization experiment were conducted using storage tank CFD model in the framework of the ANSYS Fluent CFD code. The simulations were performed for the 70% fill level case with MLI on domes and no vapor cooling. All the phase change calculations in these simulations were generated by in-house Schrage-based evaporation-condensation model. The calculations were performed using both the Volume of Fluid (VOF) and Sharp Interface multiphase 2D axisymmetric models. A number of parametric and sensitivity studies were performed to check the various aspects of the CFD model. These studies helped to understand the effects of varying several parameters on the tank pressure and temperature during self-pressurization. Turbulence modeling; turbulence damping at the interface; and using constant vs. temperature dependent fluid properties were shown to have the most profound influence on predicted tank pressures and temperatures. Current study indicates that including phase change at the interface into the computational model is crucial for accurate prediction of the tank self-pressurization process. The effect of accommodation coefficient was also studied. Tank pressure values predicted by the VOF, and Sharp Interface models are within 4% of the experimental ones.

Computational Fluid Dynamics↗

CFD Model Development of a Cryogenic Storage Tank Self-Pressurization in Normal Gravity and Validation against SHIIVER Experiment

Two-Phase flow and heat transfer simulations with interfacial phase change of the Structural Heat Intercept, Insulation, and Vibration Evaluation Rig (SHIIVER) self-pressurization experiment were conducted using storage tank CFD model in the framework of the ANSYS Fluent CFD code. The simulations were performed for the 70% fill level case with MLI on domes and no vapor cooling. All the phase change calculations in these simulations were generated by in-house Schrage-based evaporation-condensation model. The calculations were performed using both the VOF and Sharp Interface multiphase 2Daxisymmetric models. A number of parametric and sensitivity studies were performed to check the various aspects of the CFD model. These studies helped to understand the effects of varying several parameters on the tank pressure and temperature during self-pressurization. These parameters include: the effect of turbulence modeling and turbulence damping at the interface; the effect of constant vs. temperature dependent fluid properties; the effect of accommodation coefficient; the effect of modeling phase change at the interface and the effect of the fill level.

Computational Fluid Dynamics↗

EMU CO2 Washout Comparative Assessments for the HAB/HAP-E in Support of EVA 80

After water was reported in the Extravehicular Mobility Unit (EMU) helmet during ISS US EVA 80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) model was developed to assess the effect that the absorbent material has on the concentration of carbon dioxide in the EMU helmet. Within a small, closed volume such as a helmet, some amount of the carbon dioxide produced by the suit-wearer will be re-inhaled before being cleared from the oral-nasal region. The ability of the suit to remove carbon dioxide is referred to as CO2 washout. Pathological levels of inhaled CO2 (hypercapnia) are associated with dizziness, fatigue, and headaches. The CFD model was built in ANSYS Fluent and adapted to assess CO2 washout in a variety of scenarios grouped into three categories: ventilation cases, varying metabolic rates, and varying sorbent material configurations. The presence of the sorbent material did not prove detrimental to CO2 washout in the helmet.

Moses Navarro↗

Assessment of HAB Particulate Tracing in EMU Helmet in Support of Eva 80

After water was reported in the EMU helmet during ISS US EVA-80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) analysis was done tracing the paths of particulates potentially generated by the absorbent material. The objective was to characterize the risk these particulates pose for inspiration and eye irritation by tracking what percentage of particulates contact the suit-wearer’s face, eyes, or mouth. The model consisted of a human mannikin head and torso inside the EMU suit and helmet. The software used was ANSYS Fluent, with discrete phase modeling enabled to generate and track the particulates within the ventilation flow of the helmet.

Abigail Baukus↗

Predictions of Line Chilldown Boiling Regime Transitions by a Coupled CFD-Sub-Grid Boiling Model Validated against 1G LN2 Experiments

Propellant storage and transfer during future long-duration missions will involve fuel depot operations in which a donor depot tank is used to fill a receiver spacecraft tank with a cryogenic propellant. Prior to the on-Orbit filling operation both the receiver tank and the transfer line must be cooled. The line chilldown process involves transition between boiling regimes in microgravity that will be quite different from their 1g ground-based counterparts. Since the cryogenic propellant itself will be used to perform the chilldown process, the time constants to cool the wall and the amount of fuel that will be used become important design considerations. In this light, the focus of the present work is to capture the flow boiling characteristics of a cryogenic fluid, namely, liquid nitrogen, during the chill-down of a transfer line using CFD modelling and simulations. The cryogenic chill-down process involves different flow boiling regimes: film boiling, transition boiling and nucleate boiling. The prediction of transition between these regimes in a CFD framework is new and challenging. The present work addresses this challenge by employing a volume-of-fluid (VOF) based methodology with Lee phase change model to predict the film boiling regime of the chill-down process in ANSYS Fluent®. The transition and nucleate boiling regimes are predicted by incorporating a sub-grid model that accounts for bubble nucleation, growth, shedding frequency, and departure diameter. The sub-grid model is implemented into Fluent via a user-defined function for wall-fluid heat flux calculations. The sub-grid model is similar in formulation to the well-known Rensselaer Polytechnic Institute (RPI) boiling model. The CFD model is validated against published experimental data for liquid nitrogen chill-down of a heated stainless-steel pipe in 1g. Predicted results shows good agreement of wall temperature, rewetting temperature, and transition between film and nucleate boiling with the experimental measurements for several different LN2 flowrates in the vertical orientation. The strengths and weaknesses of this modeling approach are presented and discussed.

Cryogenic Fluid Management↗

EMU CO2 Washout Comparative Assessments for the HAB/HAP-E in Support of EVA 80

After water was reported in the Extravehicular Mobility Unit (EMU) helmet during ISS US EVA 80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) model was developed to assess the effect that the absorbent material has on the concentration of carbon dioxide in the EMU helmet. Within a small, closed volume such as a helmet, some amount of the carbon dioxide produced by the suit-wearer will be re-inhaled before being cleared from the oral-nasal region. The ability of the suit to remove carbon dioxide is referred to as CO2 washout. Pathological levels of inhaled CO2 (hypercapnia) are associated with dizziness, fatigue, and headaches. The CFD model was built in ANSYS Fluent and adapted to assess CO2 washout in a variety of scenarios grouped into three categories: ventilation cases, varying metabolic rates, and varying sorbent material configurations. The presence of the sorbent material did not prove detrimental to CO2 washout in the helmet.

Moses Navarro↗

Assessment of HAB Particulate Tracing in EMU Helmet in Support of Eva 80

After water was reported in the EMU helmet during ISS US EVA-80, mitigation strategies were created to attempt to arrest the motion of any droplets that enter the helmet for future Extravehicular Activities (EVAs). This included adding absorbent materials into the interior of the helmet. But before a mitigation strategy can be implemented, it must first be proven to be safe. Towards this aim, a computational fluid dynamics (CFD) analysis was done tracing the paths of particulates potentially generated by the absorbent material. The objective was to characterize the risk these particulates pose for inspiration and eye irritation by tracking what percentage of particulates contact the suit-wearer’s face, eyes, or mouth. The model consisted of a human mannikin head and torso inside the EMU suit and helmet. The software used was ANSYS Fluent, with discrete phase modeling enabled to generate and track the particulates within the ventilation flow of the helmet.

Abigail Baukus↗

Integrated Bosch Process System Models for In-Situ Oxygen and Carbon Production

In-Situ Resource Utilization (ISRU) technology is a vital component to NASA’s mission of a sustainable presence on the Moon and Mars. Local resources can be leveraged to reduce resupply frequency and mass. Elements of the Bosch process, combined with the carbothermal reduction process, can produce oxygen on the lunar surface with minimal consumables. The Bosch process can also produce oxygen on the Martian surface by using the CO 2 -rich environment. Between both systems, adsorption pump, solar thermal energy, carbon formation reactor, and water recovery subsystems are modeled and integrated to create a functional model in MATLAB software. The model is used to simulate performance of the system and reduce mass, power, and volume requirements. This integrated system model provides a tool to scale ISRU technologies for oxygen and carbon production. The MATLAB model is created by developing a system of independent subsystem models that are solved for their quasi-steady state values which can be integrated with respect to time to determine the change in current states. A flexible time stepping method is used to ensure a high level of accuracy during periods of rapid change while still making use of a simple explicit integration method. The flexible time step is calculated for each independent subsystem and the minimum value from those is used as the overall time step. A flexible time step is calculated by dividing a resolution value, or the maximum change per time step, by the variables current rate of change. The maximum value from all points in space is used for subsystem models that contain multiple values. The process is done for every variable that is being monitored in each subsystem and the global minimum is used as that iteration’s timestep. Several assumptions used in the MATLAB model for fluid flow dynamics, such as 1-D gas flow through the sorption pump, are supported by modeling in Ansys Fluent software. The Lunar oxygen production system is outlined in Fig. 1. The carbothermal reduction subsystem uses solar energy to heat a mixture of lunar regolith and carbon powder to produce carbon monoxide. To begin, the carbon monoxide feeds to the modified Bosch subsystem along with hydrogen gas. The reactants then enter the carbon formation reactor where water and carbon powder are produced. Solar thermal energy is used to add energy to the reactor, but waste heat from the carbothermal process is another potential heat source. The water is collected and electrolyzed to produce hydrogen which reenters the Bosch subsystem, and the oxygen is stored for downstream use. The carbon powder is collected and feeds back into the carbothermal subsystem. The Martian oxygen production system uses the full Bosch process and is outlined in Fig 2. A CO 2 adsorption pump thermally cycles to scrub and pressurize CO 2 from the environment. Along with an initial supply of hydrogen, the reactants enter the Reverse Water Gas Shift Reactor (RWGSR) which produces carbon monoxide and water. Carbon monoxide and unreacted hydrogen enter the carbon formation reactor to produce water and carbon powder. The water is collected from both reactors and electrolyzed to reintroduce hydrogen and store oxygen for propellant production or life support. Carbon is removed from the carbon formation reactor and stored. The adsorption pump utilizes rapid cycle temperature swings within a stack of zeolite coated surfaces. The subsystem model solves 1-D quasi-steady conservation laws of the quasi-steady form, shown in Eq. 1, for the gas stream and heat exchange liquid to predict performance parameters such as breakthrough capacity and optimum cycle time. The source term S is used to capture interactions between the fluid flows and the sorbent. A quasi-steady-state scheme is used where no time derivatives appear in the governing equations, except for those in the source terms. This results in an autonomous system, where ∂F/∂x = ƒ(F). The fluxes F are provided at the inlet, and an explicit method is used to solve for the spatial distribution of F. The heat and mass flows to the sorbent are then extracted from the source terms. These flows are numerically integrated to produce a 1-D solution for the system’s state as a function of both time and space. The body of the adsorption pump is separated into two semi-independent models: the heat exchanger fluid flow and gas flow through the zeolite coated surfaces. Both models are solved using the above-described method to find a 1-D solution as a function of space and interact only once a timestep is taken. The interaction point is the sorbent through which all heat transfer between the two models must occur. Sorbent mass adsorption is calculated using the Lagergren model, shown in Eq. 2, where the transfer coefficient, λ D , is found by solving a system of nondimensionalized equations derived by using the heat and mass transfer analogy for transport phenomena. Using Grade 544 Type 13X zeolite as the sorbent material, the equilibrium concentration, θ eq , is calculated using the k-site Langmuir isotherm and fit parameters. Additionally, the enthalpy of adsorption used in the model is computed by interpolation of available data [1]. The subsystem model was validated using the Rapid Cycle Temperature Swing Adsorption (RC-TSA) pump. The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC). The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC).

In situ Resource Utilization↗