Hazards analysis in the Apollo applications program.
Hazard analysis technique in Apollo applications program emphasizing flight crew safety hazards
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Hazard analysis technique in Apollo applications program emphasizing flight crew safety hazards
This guide describes the input data required for using MSAP2D (Multi Stage Aeroelastic analysis Program - Two Dimensional) computer code. MSAP2D can be used for steady, unsteady aerodynamic, and aeroelastic (flutter and forced response) analysis of bladed disks arranged in multiple blade rows such as those found in compressors, turbines, counter rotating propellers or propfans. The code can also be run for single blade row. MSAP2D code is an extension of the original NPHASE code for multiblade row aerodynamic and aeroelastic analysis. Euler equations are used to obtain aerodynamic forces. The structural dynamic equations are written for a rigid typical section undergoing pitching (torsion) and plunging (bending) motion. The aeroelastic equations are solved in time domain. For single blade row analysis, frequency domain analysis is also provided to obtain unsteady aerodynamic coefficients required in an eigen analysis for flutter. In this manual, sample input and output are provided for a single blade row example, two blade row example with equal and unequal number of blades in the blade rows.
A substructure vibration analysis which was developed as a design tool for predicting helicopter vibrations is described. The substructure assembly method and the composition of the transformation matrix are analyzed. The procedure for obtaining solutions to the equations of motion is illustrated for the steady-state forced response solution mode, and rotor hub load excitation and impedance are analyzed. Calculation of the mass, damping, and stiffness matrices, as well as the forcing function vectors of physical components resident in the base program code, are discussed in detail. Refinement of the model is achieved by exercising modules which interface with the external program to represent rotor induced variable inflow and fuselage induced variable inflow at the rotor. The calculation of various flow fields is discussed, and base program applications are detailed.
Fusion reactors depend on the blanket material to breed and release tritium at the same rate or faster than it is consumed by the fusion reaction. Cellular ceramic breeders (CCBs) are dense materials that can maintain a high tritium breeding ratio while promoting tritium release because of highly connected pores. Assessing the tritium breeding capabilities of these materials requires a combination of extensive experimental and modeling efforts. In this work, we develop and calibrate a multiphysics model of tritium transport. This novel model accounts for ceramic and pore diffusion, trapping and detrapping, and several surface reactions at the pore surface. We perform a sensitivity analysis and calibrate the model by comparing its predictions against experimental measurements of deuterium absorption. The calibrated model is then used to model tritium absorption in samples with different pore microstructures to investigate the effect of pore interconnectivity on tritium absorption. The model is part of the development of the multiscale, multiphysics framework for tritium transport [i.e., the Tritium Migration Analysis Program (TMAP8)], which is itself built on top of the finite-element multiphysics framework multiphysics object-oriented simulation environment (MOOSE). This study demonstrates some of TMAP8’s capabilities and is the first step toward assessing the tritium breeding capabilities of ceramic breeder material designs.
Surface solar radiation reaching the Earth's surface is the primary forcing function of the land surface energy and water cycle. Therefore, there is a need for information on this parameter, preferably, at global scale. Satellite based estimates are now available at accuracies that meet the demands of many scientific objectives. Selection of an approach to estimate such fluxes requires consideration of trade-offs between the use of multi-spectral observations of cloud optical properties that are more difficult to implement at large scales, and methods that are simplified but easier to implement. In this study, an evaluation of such trade-offs will be performed. The University of Maryland Surface Radiation Model (UMD/SRB) has been used to reprocess five years of GOES-8 satellite observations over the United States to ensure updated calibration and improved cloud detection over snow. The UMD/SRB model was subsequently modified to allow input of information on aerosol and cloud optical depth with information from independent satellite sources. Specifically, the cloud properties from the Atmospheric Radiation Measurement (ARM) Satellite Data Analysis Program (Minnis et al., 1995) are used to drive the modified version of the model to estimate surface short-wave fluxes over the Southern Great Plain ARM sites for a twelve month period. The auxiliary data needed as model inputs such as aerosol optical depth, spectral surface albedo, water vapor and total column ozone amount were kept the same for both versions of the model. The estimated shortwave fluxes are evaluated against ground observations at the ARM Central Facility and four satellite ARM sites. During summer, the estimated fluxes based on cloud properties derived from the multi-spectral approach were in better agreement with ground measurements than those derived from the UMD/SRB model. However, in winter, the fluxes derived with the UMD/SRB model were in better agreement with ground observations than those estimated from cloud properties provided by the ARM Satellite Data Analysis Program. During the transition periods, the results were comparable.
Perhaps the single greatest roadblock to fundamental advances in our understanding of climate variability and climate change is the lack of robust and unbiased long-term global observations of the climate system. Such observations are critical for the identification and diagnosis of climate variations, and provide the constraints necessary for developing and validating climate models. The first generation of reanalysis efforts, by using fixed analysis systems, eliminated the artificial climate signals that occurred in analyses generated at the operational numerical weather prediction centers. These datasets are now widely used by the scientific community in a variety of applications including atmosphere-ocean interactions, seasonal prediction, climate monitoring, the hydrological cycle, and a host of regional and other diagnostic studies. These reanalyses, however, had problems that made them sub-optimal or even unusable for some applications. Perhaps the most serious problem for climate applications was that, while the assimilation system remained fixed, changes in the observing systems did produce spurious changes in the perceived climate. The first generation reanalysis products also exposed problems with physical consistency of the products and the accurate representation of physical processes in the climate system. Examples are bias in the estimates of ocean surface fluxes, and inadequate representation of polar hydrology. In this talk, I will describe some initial plans for a national program on reananlysis. The program is envisioned to be part of an on-going activity to maintain, improve, and reprocess our record of climate observations. I will discuss various issues affecting the quality of reanalyses, with a special focus on those relevant to the ocean.
Ce/Mn-oxide reduces the quantity and stability of NH 4 NO 3 deposits in Cu-SSZ-13.
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ABSTRACT Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole masses. The Australian Dark Energy Survey (OzDES) conducted one of the first multi-object reverberation mapping surveys, monitoring 735 AGN up to z ∼ 4, over 6 years. The limited temporal coverage of the OzDES data has hindered recovery of individual measurements for some classes of sources, particularly those with shorter reverberation lags or lags that fall within campaign season gaps. To alleviate this limitation, we perform a stacking analysis of the cross-correlation functions of sources with similar intrinsic properties to recover average composite reverberation lags. This analysis leads to the recovery of average lags in each redshift-luminosity bin across our sample. We present the average lags recovered for the Hβ, Mg ii, and C iv samples, as well as multiline measurements for redshift bins where two lines are accessible. The stacking analysis is consistent with the Radius–Luminosity relations for each line. Our results for the Hβ sample demonstrate that stacking has the potential to improve upon constraints on the R–L relation, which have been derived only from individual source measurements until now.
Users generates a fission product inventory from either reactor operating history or a nuclear criticality event. RSAC-7 models the effects of high-efficiency particulate air filters or other cleanup systems and calculates the decay and ingrowth during transport through processes, facilities, and the environment. Doses are calculated for inhalation, air immersion, ground surface, ingestion, and cloud gamma pathways. RSAC-7 is used as a tool to evaluate accident conditions in emergency response scenarios, radiological sabotage events, and safety basis accident consequences.
We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.
Isle Inc., in collaboration with Engineering for Change (project team), performed a Sensitivity Analysis of market alternatives to evaluate commercialization barriers for the Waves to Water (W2W) competitors. This analysis included interviews with organizations and end-users in three market categories (1) Disaster Relief, (2) Commercial/Industrial, and (1) Municipal, Residential, and Government. For each category, the project team selected organizations that provided a well rounded experience and set of drivers for adopting W2W technologies. In addition to the project team's efforts, fellows from NREL/DOE completed interviews with government organizations to understand their potential to engage with W2W technologies, and these findings are summarized in a separate summary document.
Fortran program using Monte Carlo method to establish probability distribution for rocket stage residual and total payload
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Mission requirements for manned flyby, orbiter and lander missions to Mars and Venus
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For abstract, see N72-17902.
For abstract, see N72-17902.