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This manual is intended as a quick guide for data reduction of GP-SANS data. It includes all necessary steps to do the data reduction based on absolute calibration using the open beam method and how to transfer the reduced data to the personal computer system. If any errors are coming up so that the reduction script is not functioning as intended, please contact the instrument scientist.
The Flight Research Services Directorate at the NASA Langley Research Center (LaRC) provides development and operations services associated with three general aviation (GA) aircraft used for research experiments. The GA aircraft includes a Cessna 206X Stationair, a Lancair Colombia 300X, and a Cirrus SR22X. Since 2004, the GA Data Framework software was designed and implemented to gather data from a varying set of hardware and software sources as well as enable transfer of the data to other computers or devices. The key requirements for the GA Data Framework software include platform independence, the ability to reuse the framework for different projects without changing the framework code, graphics display capabilities, and the ability to vary the interfaces and their performance. Data received from the various devices is stored in shared memory. This paper concentrates on the object oriented software design patterns within the General Aviation Data Framework, and how they enable the construction of project specific software without changing the base classes. The issues of platform independence and multi-threading which enable interfaces to run at different frame rates are also discussed in this paper.
A combined experimental and computational program was conducted to examine the heat transfer distribution in a turbine rotor passage geometrically similiar to the Space Shuttle Main Engine (SSME) High Pressure Fuel Turbopump (HPFTP). Heat transfer was measured and computed for both the full-span suction and pressure surfaces of the rotor airfoil as well as for the hub endwall surface. The primary objective of the program was to provide a benchmark-quality data base for the assessment of rotor passage heat transfer computational procedures. The experimental portion of the study was conducted in a large-scale, ambient temperature, rotating turbine model. Heat transfer data were obtained using thermocouple and liquid-crystal techniques to measure temperature distributions on the thin, electrically-heated skin of the rotor passage model. Test data were obtained for various combinations of Reynolds number, rotor incidence angle and model surface roughness. The data are reported in the form of contour maps of Stanton number. These heat distribution maps revealed numerous local effects produced by the three-dimensional flows within the rotor passage. Of particular importance were regions of local enhancement produced on the airfoil suction surface by the main-passage and tip-leakage vortices and on the hub endwall by the leading-edge horseshoe vortex system. The computational portion consisted of the application of a well-posed parabolized Navier-Stokes analysis to the calculation of the three-dimensional viscous flow through ducts simulating the a gas turbine passage. These cases include a 90 deg turning duct, a gas turbine cascade simulating a stator passage, and a gas turbine rotor passage including Coriolis forces. The calculated results were evaluated using experimental data of the three-dimensional velocity fields, wall static pressures, and wall heat transfer on the suction surface of the turbine airfoil and on the end wall. Particular attention was paid to an accurate modeling of the passage vortex and to the development of the wall boundary layers including crossflow.
The role of computational fluid dynamics (CFD) programs is usually one of analysis. Generally they are not used in the design phase of a project. There was an effort to integrate CFD codes into the design phase of combustion devices, specifically, in the design of the Space Transportation Main Engine (STME) nozzle. Before the results of such analyses can be accepted, the credibility of the CFD codes upon which they are based must be established. The effort to assess the capability of the NASA code FDNS2D to compute the heat transfer to a solid bounding surface is detailed. Specifically, high speed flow over a flat plate is considered, and the resulting wall shear stress, and heat transfer are computed. These values are compared against analytical results (for wall shear stress) and experimental data (for heat transfer). A brief description is given of the FDNS2D code, with special emphasis on how it handles solid wall boundary conditions. The flow conditions and the FDNS solution are presented, along with comparison to analytical and experimental data. Some intermediate observations are then made, followed by a recommendation for adoption of an alternate method for computing the wall heat flux.
An experimental program was conducted to investigate heat transfer and pressure loss characteristics of rotating multipass passages, for configurations and dimensions typical of modern turbine blades. The immediate objective was the generation of a data base of heat transfer and pressure loss data required to develop heat transfer correlations and to assess computational fluid dynamic techniques for rotating coolant passages. Experiments were conducted in a smooth wall large scale heat transfer model.
The Mu2e experiment at Fermilab imposes stringent requirements on the elimination of out-of-time beam in its pulsed proton beam - a requirement known as "extinction". We present a method to measure the out-of-time particle rates to calculate the level of extinction in the inter-pulse gaps. The proposed method utilizes an array of quartz Cherenkov radiators and photomultiplier tubes to detect particles scattered from a vacuum chamber in the M4 transfer beamline at Fermilab.The measurement will employ a new μTCA-based FPGA system for data acquisition and signal processing, utilizing real-time peak detection algorithms to count scattered beam particles. By integrating data over many transfers, the time profile of the out-of-time beam will be resolved to fractional levels relative to that of the in-time beam. These results are compared with G4beamline simulations to validate models of beam transport, dynamics, and extinction, providing critical input for optimizing beam delivery to Mu2e.
Bayesian inference is used to calibrate a bottom-up home PLC network model with unknown loads and wires at frequencies up to 30 MHz. A network topology with over 50 parameters is calibrated using global sensitivity analysis and transitional Markov Chain Monte Carlo (TMCMC). The sensitivity-informed Bayesian inference computes Sobol indices for each network parameter and applies TMCMC to calibrate the most sensitive parameters for a given network topology. A greedy random search with TMCMC is used to refine the discrete random variables of the network. This results in a model that can accurately compute the transfer function despite noisy training data and a high dimensional parameter space. The model is able to infer some parameters of the network used to produce the training data, and accurately computes the transfer function under extrapolative scenarios.
In the past decade, science and technology have reached levels that permit assessments of global environmental change. Scientific success in understanding global environmental change depends on integration and management of numerous data sources. The Global Change Data and Information System (GCDIS) must provide for the management of data, information dissemination, and technology transfer. The Earth Observing System Data and Information System (EOSDIS) is NASA's portion of this global change information system.
In the current political climate NASA must be able to show reliable measures demonstrating successful technology transfer. The currently available quantitative data of intellectual property technology transfer efforts portray a less than successful performance. In this paper, the use of only quantitative values for measurement of technology transfer is shown to undervalue the effort. In addition, NASA's current policy in negotiating intellectual property rights results in undervalued royalty rates. NASA has maintained that it's position of providing public good precludes it from negotiating fair market value for its technology and instead has negotiated for reasonable cost in order to recover processing fees. This measurement issue is examined and recommendations made which include a new policy regarding the intellectual property rights negotiation, and two measures to supplement the intellectual property measures.
Research in planetary seismology is fundamentally constrained by a lack of data. Seismo-logical science products of future missions can typically only be informed by theoretical signal/noise characteristics of the environment or likely Earth-analogues. Although objectives can be re-assessed after some initial data-collection upon lander arrival, transfer of high-resolution data back to Earth is costly on lander power usage. Over the last several years, development of GPU computing techniques and open-source high-level APIs have led to rapid advances in deep learning within the fields of computer vision, natural language processing, and collaborative filtering. These techniques are actively being adapted in seismology for a variety of tasks, including: earthquake detection, seismic phase discrimination, and ground-motion prediction. Until the recent detection of mars quakes during the Mars InSight mission, the only other measurements of seismicity recorded outside of Earth was on the Moon during the Apollo missions between 1969 to 1977. These unique data sets have been periodically revisited using new seismological methods, including ambient noise interferometry and Hidden Markov Models. Our objective is to develop a deep learning seismic detector and use it to catalog moonquakes from the Apollo 17 Lunar Seismic Profiling Experiment (LSPE) and compare the results with those obtained by other methods. Additionally, we will assess the accuracy tradeoff between using a training set of lunar data and one composed of Earth seismicity. In this document, we present preliminary results using a prototype classifier trained on a small set of earthquakes that was able to obtain detections for LSPE moonquakes with a greater accuracy than a recent study using Hidden Markov Models.
This study explores the transferability of machine-learning models to analyze X-ray diffraction (XRD) profiles of shock-loaded single-crystal and polycrystalline data.
The effect of shuttle configuration geometry, angle of attack, and free stream flow conditions on the heat-transfer distribution as influenced by three-dimensional effects, the wing-fuselage shock-interaction, and resultant wing-impingement phenomena are examined. In addition, the data provided information regarding the flow field in the vicinity of the nose and boundary layer transition in the plane of symmetry of the fuselage. The data included measurements of the surface pressure, the heat transfer rate distributions, (using models instrumented with thermocouples and models painted with thermographic phosphor) and schlieren and shadowgraph photographs. Posttest photographs of the painted models supplemented the heat transfer data.
The results of a semi-analytical and experimental study of adiabatic tube flow and an analytical and experimental investigation of the thermal entrance region for gas flowing through electrically heated circular tubes are presented. Emphasis is placed on the low Reynolds number turbulent flow regime--defined as fully turbulent flow at bulk Reynolds numbers from 3,000 to about 15,000. Adiabatic air velocity and friction data and localized heat transfer measurements for air and helium, at low heating rates, are presented for this range. The adiabatic data were obtained in a 1.61 inch 1D tube for flow at bulk Reynolds numbers from 3,000 to 15,000. A continuous, Reynolds number-dependent, profile is developed from the data by using a modification of Reichardts' wall and middle law eddy diffusivity expressions. The velocity profile satisfies continuity. It is valid for all Reynolds numbers in excess of 3,000 for which the flow is fully turbulent and the Blasius friction factor expression is valid. The thermal entrance problem for a fully developed velocity profile is solved analytically by the method of Sparrow, Hallman, and Siegel. The solution is based on the profile developed from the velocity study. Tabular values of the eigenvalues and normalized Nusselt numbers for gases are presented for it range of Reynolds numbers from 3,000 to 50,000. The axial variation of Nusselt number is found to be correlated by [Nu/Nu_(∞)] = 1 + 0.8 (1+ 70,000 Re^(-3/2)) ((x/D)^(-1)) to within ± 5 per cent for x/D ≥2. The fully developed value agrees with the Dittus - Boelter correlation, Nu_(∞) = 0.021 Re^(0.8) Pr^(0.4) For the eigenvalues, λ^(2)_(n), and the associated constants, A_(n), correlations of the form λ^(2)_(n) =A_(1,n) Re^(-b_(1,n) + C_(1,n) Re^(-d_(1,n)) A_(n) = -A_(2,n) Re^(-b_(2,n) + C_(2,n) Re^(-d_(2,n)) are obtained. The coefficients and powers are presented in tabular form. Heat transfer data are presented, primarily for helium, for the low Reynolds number turbulent range. A one-quarter inch, resistively heated, vertical, circular tube was used for the study. The data cover an axial range from 1.2 to 96 diameters; wall-to-bulk temperature ratios vary from 1 to 1.4. In the low Reynolds number turbulent regime, these data clearly support the present analytical solution rather than the prediction obtained by applying the eddy diffusivity distribution used by Sparrow, Hallman, and Siegel.
The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.
The functional requirements and the configuration of the data base management system are described. Techniques and technology which will enable more efficient and timely transfer of useful data from the sensor to the user, extraction of information by the user, and exchange of information among the users are demonstrated.
The development of an operational computer program, the Procedures and Performance Program (PPP), is reported which provides a procedures recording and crew/vehicle performance monitoring capability. The PPP provides real time CRT displays and postrun hardcopy of procedures, difference procedures, performance, performance evaluation, and training script/training status data. During post-run, the program is designed to support evaluation through the reconstruction of displays to any point in time. A permanent record of the simulation exercise can be obtained via hardcopy output of the display data, and via magnetic tape transfer to the Generalized Documentation Processor (GDP). Reference procedures data may be transferred from the GDP to the PPP.
Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.
The failure information propagation model (FIPM) data base was developed to store and manipulate the large amount of information anticipated for the various Space Shuttle Main Engine (SSME) FIPMs. The organization and structure of the FIPM data base is described, including a summary of the data fields and key attributes associated with each FIPM data file. The menu-driven software developed to facilitate and control the entry, modification, and listing of data base records is also discussed. The transfer of the FIPM data base and software to the NASA Marshall Space Flight Center is described. Complete listings of all of the data base definition commands and software procedures are included in the appendixes.