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Results for “Training Analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Power train analysis for the DOE/NASA 100-kW wind turbine generator

Progress in explaining variations of power experienced in the on-line operation of a 100 kW experimental wind turbine-generator is reported. Data are presented that show the oscillations tend to be characteristic of a wind-driven synchronous generator because of low torsional damping in the power train, resonances of its large structure, and excitation by unsteady and nonuniform wind flow. The report includes dynamic analysis of the drive-train torsion, the generator, passive driveline damping, and active pitch control as well as correlation with experimental recordings. The analysis assumes one machine on an infinite bus with constant generator-field excitation.

Seidel, R. C.↗
NASA NTRS1985

Train Analysis

Running resistance, the combined resistance induced by aerodynamic drag and mechanical friction, absorbs about 16 percent of railroad budget and maintenance costs. In order to study these losses, AT&SF Railroad entered into a joint research project with the Jet Propulsion Laboratory. Various train configurations were tested when the resulting data was computer analyzed. The Coast-Down Technique was estimated at one percent accuracy. AT&SF is evaluating the data further.

Source record↗
NASA NTRS2003

Analysis and training - why different?

It is tempting to seek universal combat simulations, optimized for both analysis and training. Such a quest would be quixotic, however, because the two environments place conflicting requirements.

simulation modeling training analysis↗
NASA NTRS1978

Drive train dynamic analysis

A method for parametric variations in drive train dynamic analysis is described. The method models the individual components of a drive system, forms the appropriate system interface coordinates and, calculates the system dynamic response at particular frequencies. Application of the method for prediction of the dynamic response characteristics of a helicopter transmission, and a comparison of results with test data are also included.

Giansante, N.↗
NASA NTRS1975

Spacelab cost reduction alternatives study. Volume 3: Crew training task analysis

The on-orbit phase of flight was analyzed to identify the functions that had to be performed to meet flight objectives. The functions identified were combined into a top-level functional flow diagram (3.0 On-Orbit Operations). Each function, in turn, was analyzed to determine the major activities necessary to perform the function. The activities and activity sequences were presented in second level functional flow diagrams. Each major activity was further analyzed to define the operator tasks that are performed to accomplish that activity. Training analysis worksheets were used to document the analysis of each function down to the task level and provide training requirements data for each task. For Vol. 1, see N76-24315.

Source record↗
NASA NTRS2016

Teamwork Training Needs Analysis for Long-Duration Exploration Missions

The success of future long-duration exploration missions (LDEMs) will be determined largely by the extent to which mission-critical personnel possess and effectively exercise essential teamwork competencies throughout the entire mission lifecycle (e.g., Galarza & Holland, 1999; Hysong, Galarza, & Holland, 2007; Noe, Dachner, Saxton, & Keeton, 2011). To ensure that such personnel develop and exercise these necessary teamwork competencies prior to and over the full course of future LDEMs, it is essential that a teamwork training curriculum be developed and put into place at NASA that is both 1) comprehensive, in that it targets all teamwork competencies critical for mission success and 2) structured around empirically-based best practices for enhancing teamwork training effectiveness. In response to this demand, the current teamwork-oriented training needs analysis (TNA) was initiated to 1) identify the teamwork training needs (i.e., essential teamwork-related competencies) of future LDEM crews, 2) identify critical gaps within NASA’s current and future teamwork training curriculum (i.e., gaps in the competencies targeted and in the training practices utilized) that threaten to impact the success of future LDEMs, and to 3) identify a broad set of practical nonprescriptive recommendations for enhancing the effectiveness of NASA’s teamwork training curriculum in order to increase the probability of future LDEM success.

Smith-Jentsch, Kimberly A.↗
DOE OSTI2024

Performance Evaluation of a High Salinity Produced Water Treatment Train: Chemical Analysis and Aryl Hydrocarbon Activation

Water scarcity and increased energy demands have put a strong focus on improving industries at the heart of the water–energy nexus. Treatment of oil and gas produced water (PW) can help reduce freshwater consumption during hydraulic fracturing, especially in arid regions, while also removing harmful contaminants from entering the environment. However, it is also difficult to treat because PW contains high concentrations of many environmentally toxic contaminants, which require complex and expensive treatment processes to achieve their removal. To demonstrate the possibility of PW treatment and reuse in the O&G industry, a comprehensive environmental toxicity and water quality analysis throughout a five-process treatment train was performed on high salinity (>120 g/L) Permian basin raw PW. Here, the concentrations of naturally occurring radioactive materials were reduced by over 99%, total organic carbon was reduced by 93%, and inorganic constituents, including total dissolved solids, were reduced by over 99%. Compounds that induced the aryl hydrocarbon receptor and caused cytotoxicity in MCF-7 cells were also removed. Overall, the results of this study show that a short treatment train (five distinct unit processes) can be effective in treating PW to a level suitable for use outside of the oil industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1972

NERVA 400E thrust train dynamic analysis

The natural frequencies and dynamic responses of the NERVA 400E engine thrust train were determined for nuclear space operations (NSO), and earth-orbital shuttle (EOS) during launch and boost conditions. For NSO, a mini-tank configuration was analyzed with the forward end of the upper truss assumed fixed at the stage/mini-tank interface. For EOS, both a mini-tank and an engine only configuration were analyzed for a specific engine assembly support (EAS) stiffness. For all cases the effect of the shield on dynamic response characteristics was determined by performing parallel analyses with and without the shield. Gimbaling loads were not generated as that effort was scheduled after the termination date. The analysis, while demonstrating the adequacy of the engine design, revealed serious deficiencies in the EAS. Responses at the unsupported ends of the engine are excessive. Responses at the nuclear subsystem interface appear acceptable. It is recommended that additional analysis and design effort be expended upon the EAS to ensure that all engine responses stay within reasonable bounds.

Vronay, D. F.↗
DOE OSTI2021

Cybersecurity Training Curriculum Analysis through the application of Machine Learning Text Classification and Natural Language Processing

In the domain of cybersecurity, there are a wide variety of education and training courses offered by a variety of training providers (commercial vendors, governmental, and academic institutions). Unfortunately, a comprehensive cross-provider mapping of the body of course offerings does not currently exist. Furthermore, although individual providers have made efforts, an accepted cross-provider mapping of education and training courses (or categories) to an accepted framework of work-roles by cybersecurity work-role and competency level has not yet been developed. The inability to compare training courses by topic or cybersecurity work-role and the level of difficulty (competency level) affects all organizations in identifying and selecting potential training opportunities for their personnel performing cybersecurity duties. In this work, we report on our application of Machine Learning (ML) Text Classification and Natural Language Processing (NLP) methodologies to begin to create a process to organize an accurate and thorough catalog of course offerings by work-role and competency level. Through the analysis of text-based course attributes (description, learning objectives, prerequisites, etc.) along with the text-based attributes for identified cybersecurity work-roles, our ML efforts examined methods to determine the work-role "best-fit" for each course as well as the competency level through action-verb text comparison.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1974

Track-train dynamic analysis and test program, truck static test

A series of tests were conducted to define the characteristics of an ASF 11 Ride Truck Assembly including joint slop, friction and stiffness. Loading to the truck assembly included vertical load to simulate the car/pool loading combined with lateral or moment loading that resulted in desired truck deflections for the various phases of testing. All seven test conditions were successfully completed with load and deflection data being collected. No attempt is made to reduce the applicable data other than to provide computer plots.

Nemes, A. G.↗
NASA NTRS1976

Track-train dynamic analysis and test program. Barber S-2 static test

The results of a static test of a Barber S-2 freight truck conducted to measure the stiffness and friction parameters of the modes of deformation which are being used in various mathematical models in the railroad industry were presented. Some difficulty was first experienced with the truck hardware since it was in an essentially new condition with many high spots causing interference. No difficulty was experienced once the interference was removed. The characteristics of the Barber S-2 are very similar to the ASF ride control truck. The major difference between the two trucks is the amount of friction between the bolster and side frames in both the vertical and lateral directions. The Barber S-2 has approximately twice the friction the ASF ride control truck has in the fully loaded condition. This does not necessarily imply that all trucks will have this same ratio of friction.

Abbott, P. W.↗
NASA NTRS1982

Track train dynamics analysis and test program: Methodology development for the derailment safety analysis of six-axle locomotives

The operational safety of six axle locomotives is analyzed. A locomotive model with corresponding data on suspension characteristics, a method of track defect characterization, and a method of characterizing operational safety are used. A user oriented software package was developed as part of the methodology and was used to study the effect (on operational safety) of various locomotive parameters and operational conditions such as speed, tractive effort, and track curvature. The operational safety of three different locomotive designs was investigated.

Marcotte, P. P.↗
NASA NTRS1976

Development and analysis of a modular approach to payload specialist training

A modular training approach for Spacelab payload crews is described. Representative missions are defined for training requirements analysis, training hardware, and simulations. Training times are projected for each experiment of each representative flight. A parametric analysis of the various flights defines resource requirements for a modular training facility at different flight frequencies. The modular approach is believed to be more flexible, time saving, and economical than previous single high fidelity trainer concepts. Block diagrams of training programs are shown.

Watters, H.↗
DOE OSTI2024

A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-scale Structure Using Sequential Methods

Strong gravitational lenses are a singular probe of the Universe’s small-scale structure—they are sensitive to the gravitational effects of low-mass (<10 10 M ⊙ ) halos even without a luminous counterpart. Recent strong-lensing analyses of dark matter structure rely on simulation-based inference (SBI). Modern SBI methods, which leverage neural networks as density estimators, have shown promise in extracting the halo-population signal. However, it is unclear whether the constraints from these models are limited by the methodology or the data. In this study, we introduce an accelerator-optimized simulation pipeline that can generate lens images with realistic subhalo populations in milliseconds. Leveraging this simulator, we identify the main limitation of our fiducial SBI analysis: training set size. We then adopt a sequential neural posterior estimation (SNPE) approach, allowing us to refine the training distribution to align with the observed data. Using only one-fifth as many mock Hubble Space Telescope images, SNPE matches the constraints on the low-mass halo population produced by our best nonsequential model. Our experiments suggest that an over 3 order-of-magnitude increase in training set size and GPU hours would be required to achieve an equivalent result without sequential methods. While the full potential of the existing lens sample remains to be explored, the notable improvement in constraining power enabled by our sequential approach highlights that current constraints are limited primarily by methodology and not the data itself. Moreover, our results emphasize the need to treat training set generation and model optimization as interconnected stages of any cosmological analysis using SBI.

79 ASTRONOMY AND ASTROPHYSICS↗