ALBERT

Description: It is useful to interface a high-speed-flow solution and SINDA to analyze the thermal behavior of systems that include both conduction and high speed flows. When interfacing a high-speed-flow solution to SINDA, it may be necessary to include the viscous effects in the energy equations. Boundary layer effects of interest include heat transfer coefficients (including convection and viscous dissipation) and friction coefficients. To meet this need, a fast, uncoupled, compressible, two-dimensional, boundary layer algorithm was developed that can model flows with and without separation. This algorithm was used as a subroutine with SINDA. Given the core flow properties and the wall heat flux from SINDA, the boundary layer algorithm returns a wall temperature to SINDA and boundary layer algorithm are iterated until they predict the same wall temperature.

Description: An uncoupled boundary layer algorithm was combined with an inviscid core flow algorithm to model flows within supersonic engine inlets. The inviscid flow algorithm that was used was the LArge Perturbation INlet Code (LAPIN). The boundary layer and inviscid core flow algorithms were formulated in different manners. The boundary layer algorithm was two dimensional and solved in nonconservation form, while the core flow algorithm was one dimensional and solved in conservation form. In order to interface the two codes, the following modifications were important. The coordinate system was set up to maintain the parabolic nature of the boundary layer algorithm while approaching the one dimensional core flow solution far from a wall. The pressure gradient used in the boundary layer equation was calculated using the core flow values and the boundary layer equations, so the boundary layer solution smoothly approached the core flow values far from the wall. Flaring was used for the advection terms perpendicular to the core flow to maintain the stability of the algorithm. With these modifications, the combined viscous/inviscid algorithm matched well experimental observations of pressure distributions with a supersonic inlet.

Description: A finite difference boundary layer algorithm was developed to model viscous effects when an inviscid core flow solution is given. This algorithm solved each boundary layer equation separately, then iterated to find a solution. Solving the boundary layer equations sequentially was 2.4 to 4.0 times faster than solving the boundary layer equations simultaneously. This algorithm used a modified Baldwin-Lomax turbulence model, a weighted average of forward and backward differencing of the pressure gradient, and a backward sweep of the pressure. With these modifications, the boundary layer algorithm was able to model flows with and without separation. The number of grid points used in the boundary layer algorithm affected the stability of the algorithm affected the stability of the algorithm as well as the accuracy of the predictions of friction coefficients and momentum thicknesses. Results of this boundary layer algorithm compared well with experimental observations of friction coefficients and momentum thicknesses. In addition, when used interactively with an inviscid flow algorithm, this boundary layer algorithm corrected for viscous effects to give a good match with experimental observations for pressures in a supersonic inlet.

Description: In this research we have developed an algorithm for the purpose of constraint processing by utilizing relational algebraic operators. Van Beek and others have investigated in the past this type of constraint processing from within a relational algebraic framework, producing some unique results. Apart from providing new theoretical angles, this approach also gives the opportunity to use the existing efficient implementations of relational database management systems as the underlying data structures for any relevant algorithm. Our algorithm here enhances that framework. The algorithm is quite general in its current form. Weak heuristics (like forward checking) developed within the Constraint-satisfaction problem (CSP) area could be also plugged easily within this algorithm for further enhancements of efficiency. The algorithm as developed here is targeted toward a component-oriented modeling problem that we are currently working on, namely, the problem of interactive modeling for batch-simulation of engineering systems (IMBSES). However, it could be adopted for many other CSP problems as well. The research addresses the algorithm and many aspects of the problem IMBSES that we are currently handling.

Description: The Ninth Thermal and Fluids Analysis Workshop (TFAWS 98) was held at the Ohio Aerospace Institute in Cleveland, Ohio from August 31 to September 4, 1998. The theme for the hands-on training workshop and conference was "Integrating Computational Fluid Dynamics and Heat Transfer into the Design Process." Highlights of the workshop (in addition to the papers published herein) included an address by the NASA Chief Engineer, Dr. Daniel Mulville; a CFD short course by Dr. John D. Anderson of the University of Maryland; and a short course by Dr. Robert Cochran of Sandia National Laboratories. In addition, lectures and hands-on training were offered in the use of several cutting-edge engineering design and analysis-oriented CFD and Heat Transfer tools. The workshop resulted in international participation of over 125 persons representing aerospace and automotive industries, academia, software providers, government agencies, and private corporations. The papers published herein address issues and solutions related to the integration of computational fluid dynamics and heat transfer into the engineering design process. Although the primary focus is aerospace, the topics and ideas presented are applicable to many other areas where these and other disciplines are interdependent.

Description: Line Chill-down heat transfer was modelled using SINDA/FLUINT. Multiple chill-down tests were modelled using the heat transfer correlations that are available in SINDA/FLUINT, as well as incorporating heat transfer empiricisms developed by the University of Florida1 based on a series of liquid nitrogen chill-down tests. The chill-down tests that were modelled were the liquid nitrogen tests conducted by the University of Florida1 as well as liquid hydrogen tests conducted by NASA Glenn Research Center2. The liquid nitrogen tests included horizontal flow, upward flow, and downward flow with the liquid Reynolds Numbers ranging 850 - 231,000. The liquid hydrogen test was vertical upward flow at a Reynolds Number range of 18,400 - 433,000. Both the University of Florida's heat transfer correlations and SINDA/FLUINT's internal correlations faired similarly to wall temperature test data. They were acceptable although improvements could be made to the University of Florida correlations as well and SINDA/FLUINT's internal correlations.

Description: Line chill-down is an important process in cryogenic tank propellant management, storage, and usage Complex flow dynamics during these processes: boiling heat transfer (film, transition, and nucleate) Understanding boiling phenomena can lead to efficient line chill-down systems that use less propellant, propellant stored, reducing cost for space missions Line Chill-down heat transfer was modelled using SINDA/FLUINT version 5.8 (SF) Multiple chill-down tests were modelled using: heat transfer correlations readily available in SF using HTN/HTC TIES heat transfer empiricisms developed by the University of Florida (UF) based on a series of liquid nitrogen chill-down tests using SF HTU TIES Chill-down tests modelled: liquid nitrogen tests conducted by the University of Florida horizontal flow, upward flow, and downward flow (Reynolds Numbers ranging 850-231,000)liquid hydrogen tests conducted by NASA Glenn Research Center vertical upward flow (Reynolds Number range of 18,400 - 433,000)The flow rate was measured far downstream of the test section, near the system exit. Where to set the flow rate? SF was highly sensitive, and sometime unstable, setting the test flow rate downstream (the outlet) of the test section model and setting the test pressure upstream (the inlet) of the test section model higher flow rate oscillations at the entrance of the model's test section SF was more stable setting the test flow rate upstream (than the downstream flow rate set case)test pressure was used as an inlet (SF plenum) to set the thermodynamic state (temperature and quality) coming into the system setting the appropriate downstream pressure was the unknown. The pressure drops predicted by SF for the downstream set flow rate boundary condition were much smaller than test section measured pressure drops. The multiphase pressure drop correlations used internally in SF may need to be adjusted. Models with an upstream flow rate set assumed a pressure drop that was small

Description: Modeling droplet condensation via CFD codes can be very tedious, time consuming, and inaccurate. CFD codes may be tedious and time consuming in terms of using Lagrangian particle tracking approaches or particle sizing bins. Also since many codes ignore conduction through the droplet and or the degradating effect of heat and mass transfer if noncondensible species are present, the solutions may be inaccurate. The modeling of a condensing spray chamber where the significant size of the water droplets and the time and distance these droplets take to fall, can make the effect of droplet conduction a physical factor that needs to be considered in the model. Furthermore the presence of even a relatively small amount of noncondensible has been shown to reduce the amount of condensation [Ref 1]. It is desirable then to create a modeling tool that addresses these issues. The path taken to create such a tool is illustrated. The application of this tool and subsequent results are based on the spray chamber in the Spacecraft Propulsion Research Facility (B2) located at NASA's Plum Brook Station that tested an RL-10 engine. The platform upon which the condensation physics is modeled is SINDAFLUINT. The use of SINDAFLUINT enables the ability to model various aspects of the entire testing facility, including the rocket exhaust duct flow and heat transfer to the exhaust duct wall. The ejector pumping system of the spray chamber is also easily implemented via SINDAFLUINT. The goal is to create a transient one dimensional flow and heat transfer model beginning at the rocket, continuing through the condensing spray chamber, and finally ending with the ejector pumping system. However the model of the condensing spray chamber may be run independently of the rocket and ejector systems detail, with only appropriate mass flow boundary conditions placed at the entrance and exit of the condensing spray chamber model. The model of the condensing spray chamber takes into account droplet conduction as well as the degrading effect of mass and heat transfer due to the presence of noncondensibles. The one dimension model of the condensing spray chamber makes no presupposition on the pressure profile within the chamber, allowing the implemented droplet physics of heat and mass transfer coupled to the SINDAFLUINT solver to determine a transient pressure profile of the condensing spray chamber. Model results compare well to the RL-10 engine pressure test data.

Description: Modeling droplet condensation via CFD codes can be very tedious, time consuming, and inaccurate. CFD codes may be tedious and time consuming in terms of using Lagrangian particle tracking approaches or particle sizing bins. Also since many codes ignore conduction through the droplet and or the degradating effect of heat and mass transfer if noncondensible species are present, the solutions may be inaccurate. The modeling of a condensing spray chamber where the significant size of the water droplets and the time and distance these droplets take to fall, can make the effect of droplet conduction a physical factor that needs to be considered in the model. Furthermore the presence of even a relatively small amount of noncondensible has been shown to reduce the amount of condensation. It is desirable then to create a modeling tool that addresses these issues. The path taken to create such a tool is illustrated. The application of this tool and subsequent results are based on the spray chamber in the Spacecraft Propulsion Research Facility (B2) located at NASA's Plum Brook Station that tested an RL-10 engine. The platform upon which the condensation physics is modeled is SINDAFLUINT. The use of SINDAFLUINT enables the ability to model various aspects of the entire testing facility, including the rocket exhaust duct flow and heat transfer to the exhaust duct wall. The ejector pumping system of the spray chamber is also easily implemented via SINDAFLUINT. The goal is to create a transient one dimensional flow and heat transfer model beginning at the rocket, continuing through the condensing spray chamber, and finally ending with the ejector pumping system. However the model of the condensing spray chamber may be run independently of the rocket and ejector systems detail, with only appropriate mass flow boundary conditions placed at the entrance and exit of the condensing spray chamber model. The model of the condensing spray chamber takes into account droplet conduction as well as the degrading effect of mass and heat transfer due to the presence of noncondensibles. The one dimension model of the condensing spray chamber makes no presupposition on the pressure profile within the chamber, allowing the implemented droplet physics of heat and mass transfer coupled to the SINDAFLUINT solver to determine a transient pressure profile of the condensing spray chamber. Model results compare well to the RL-10 engine pressure test data.

Description: The Baldwin-Lomax model is used in many CFD codes because it is quick and easy to implement. In this paper, we discuss implementing the Baldwin-Lomax turbulence model for both steady and unsteady compressible flows. In addition, these flows may be either separated or attached. In order to apply this turbulence model to flows which may be subjected to these conditions, certain modifications should be made to the original Baldwin-Lomax model. We discuss these modifications and determine whether the Baldwin-Lomax model is a viable turbulence model that produces reasonably accurate results for high speed flows that can be found in engine inlets.