Chemically Reacting One-Dimensional Gas-Particle Flows

The governing equations for the one-dimensional flow of a gas-particle system are discussed. Gas-particle effects are coupled via the system momentum and energy equations with the gas assumed to be chemically frozen or in chemical equilibrium. A computer code for calculating the one-dimensional flow of a gas-particle system is discussed and a user's input guide presented. The computer code provides for the expansion of the gas-particle system from a specified starting velocity and nozzle inlet geometry. Though general in nature, the final output of the code is a startline for initiating the solution of a supersonic gas-particle system in rocket nozzles. The startline includes gasdynamic data defining gaseous startline points from the nozzle centerline to the nozzle wall and particle properties at points along the gaseous startline

Space shuttle SRM plume expansion sensitivity analysis

The exhaust plumes of the space shuttle solid rocket motors can have a significant effect on the base pressure and base drag of the shuttle vehicle. A parametric analysis was conducted to assess the sensitivity of the initial plume expansion angle of analytical solid rocket motor flow fields to various analytical input parameters and operating conditions. The results of the analysis are presented and conclusions reached regarding the sensitivity of the initial plume expansion angle to each parameter investigated. Operating conditions parametrically varied were chamber pressure, nozzle inlet angle, nozzle throat radius of curvature ratio and propellant particle loading. Empirical particle parameters investigated were mean size, local drag coefficient and local heat transfer coefficient. Sensitivity of the initial plume expansion angle to gas thermochemistry model and local drag coefficient model assumptions were determined

Assessment of analytical techniques for predicting solid propellant exhaust plumes and plume impingement environments

An analysis of experimental nozzle, exhaust plume, and exhaust plume impingement data is presented. The data were obtained for subscale solid propellant motors with propellant Al loadings of 2, 10 and 15% exhausting to simulated altitudes of 50,000, 100,000 and 112,000 ft. Analytical predictions were made using a fully coupled two-phase method of characteristics numerical solution and a technique for defining thermal and pressure environments experienced by bodies immersed in two-phase exhaust plumes

Influence of design parameters on occurence of oil whirl

Oil whirl instability is a serious problem in oil lubricated journal bearings. The phenomenon is characterized by a subsynchronous vibration of the journal within the bush and is particularly apparent in turbogenerators, aeroengines and electric motors. A review is presented of previous papers on the subject of oil whirl, and a simple theory is described which was used to aid the design of an oil whirl test rig. Predictions of the onset of oil whirl made by the theory presented were found to agree with those of previous researchers. They showed that increasing the shaft flexibility, or the lubricant viscosity, and decreasing the bearing radial clearance tended to reduce the oil whirl onset speed thus making the system more unstable

Input guide for computer programs to generate thermodynamic data for air and Freon CF4

FORTRAN computer programs were developed to calculate the thermodynamic properties of Freon 14 and air for isentropic expansion from given plenum conditions. Thermodynamic properties for air are calculated with equations derived from the Beattie-Bridgeman nonstandard equation of state and, for Freon 14, with equations derived from the Redlich-Quang nonstandard equation of state. These two gases are used in scale model testing of model rocket nozzle flow fields which requires simulation of the prototype plume shape with a cold flow test approach. Utility of the computer programs for use in analytical prediction of flow fields is enhanced by arranging card or tape output of the data in a format compatible with a method-of-characteristics computer program

Analysis of SRM model nozzle calibration test data in support of IA12B, IA12C and IA36 space shuttle launch vehicle aerodynamics tests

Variations of nozzle performance characteristics of the model nozzles used in the Space Shuttle IA12B, IA12C, IA36 power-on launch vehicle test series are shown by comparison between experimental and analytical data. The experimental data are nozzle wall pressure distributions and schlieren photographs of the exhaust plume shapes. The exhaust plume shapes were simulated experimentally with cold flow while the analytical data were generated using a method-of-characteristics solution. Exhaust plume boundaries, boundary shockwave locations and nozzle wall pressure measurements calculated analytically agree favorably with the experimental data from the IA12C and IA36 test series. For the IA12B test series condensation was suspected in the exhaust plumes at the higher pressure ratios required to simulate the prototype plume shapes. Nozzle calibration tests for the series were conducted at pressure ratios where condensation either did not occur or if present did not produce a noticeable effect on the plume shapes. However, at the pressure ratios required in the power-on launch vehicle tests condensation probably occurs and could significantly affect the exhaust plume shapes

Study of high altitude plume impingement

Computer program has been developed as analytical tool to predict severity of effects of exhaust of rocket engines on adjacent spacecraft surfaces. Program computes forces, moments, pressures, and heating rates on surfaces immersed in or subjected to exhaust plume environments. Predictions will be useful in design of systems where such problems are anticipated

Assessment of analytical and experimental techniques utilized in conducting plume technology tests 575 and 593

Since exhaust plumes affect vehicle base environment (pressure and heat loads) and the orbiter vehicle aerodynamic control surface effectiveness, an intensive program involving detailed analytical and experimental investigations of the exhaust plume/vehicle interaction was undertaken as a pertinent part of the overall space shuttle development program. The program, called the Plume Technology program, has as its objective the determination of the criteria for simulating rocket engine (in particular, space shuttle propulsion system) plume-induced aerodynamic effects in a wind tunnel environment. The comprehensive experimental program was conducted using test facilities at NASA's Marshall Space Flight Center and Ames Research Center. A post-test examination of some of the experimental results obtained from NASA-MSFC's 14 x 14-inch trisonic wind tunnel is presented. A description is given of the test facility, simulant gas supply system, nozzle hardware, test procedure and test matrix. Analysis of exhaust plume flow fields and comparison of analytical and experimental exhaust plume data are presented

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data