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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-T-153: Experimental Validation and Calculation Benchmark for a Commercial Monte Carlo Pencil Beam Scanning Proton Therapy Treatment Planning System in Water

Purpose: Eclipse proton Monte Carlo AcurosPT 13.7 was commissioned and experimentally validated for an IBA dedicated PBS nozzle in water. Topas 1.3 was used to isolate the cause of differences in output and penumbra between simulation and experiment. Methods: The spot profiles were measured in air at five locations using Lynx. PTW-34070 Bragg peak chamber (Freiburg, Germany) was used to collect the relative integral Bragg peak for 15 proton energies from 100 MeV to 225 MeV. The phase space parameters (σx, σθ, ρxθ) number of protons per MU, energy spread and calculated mean energy provided by AcurosPT were identically implemented into Topas. The absolute dose, profiles and field size factors measured using ionization chamber arrays were compared with both AcurosPT and Topas. Results: The beam spot size, σx, and the angular spread, σθ, in air were both energy-dependent: in particular, the spot size in air at isocentre ranged from 2.8 to 5.3 mm, and the angular spread ranged from 2.7 mrad to 6 mrad. The number of protons per MU increased from ∼9E7 at 100 MeV to ∼1.5E8 at 225 MeV. Both AcurosPT and TOPAS agree with experiment within 2 mm penumbra difference or 3% dose difference for scenarios including central axis depth dose and profiles at two depths in multi-spot square fields, from 40 to 200 mm, for all the investigated single-energy and multi-energy beams, indicating clinically acceptable source model and radiation transport algorithm in water. Conclusion: By comparing measured data and TOPAS simulation using the same source model, the AcurosPT 13.7 was validated in water within 2 mm penumbra difference or 3% dose difference. Benchmarks versus an independent Monte Carlo code are recommended to study the agreement in output, filed size factors and penumbra differences. This project is partially supported by the Varian grant under the master agreement between University of Pennsylvania and Varian.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Integrated Modeling and Decision-Support System for Water Management in the Puget Sound Basin: Snow Caps to White Caps

Final Report for the EPA-sponsored project Snow Caps to White Caps that provides data products and insight for water resource managers to support their predictions and management actions to address future changes in water resources (fresh and marine) in the Puget Sound basin. This report details the efforts of a team of scientists and engineers from Pacific Northwest National Laboratory (PNNL) and the University of Washington (UW) to examine the movement of water in the Snohomish Basin, within the watershed and the estuary, under present and future conditions, using a set of linked numerical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1962

Measured Steady-State Performance of Water Vapor Jets for Use in Space Vehicle Attitude Control Systems

Measurements have been made in a vacuum environment to determine the steady-state performance of several nozzles having thrusts up to 1000 dynes for use in space vehicle attitude control systems. Water vapor was used as a propellant. The results indicate that the trend of the variation of specific impulse and thrust coefficient with expansion ratio is predicted by calculations based on one-dimensional isentropic flow. The level of these quantities, however, is dependent upon the nozzle diameter. The specific impulse, for example, varies from about 30 percent to 80 percent of the theoretical value as the nozzle thrust is increased from about 10 to 1000 dynes.

JET↗
NASA NTRS1969

Water-glycol system volume calculation

Two methods calculate the volume of a thermodynamic system. Integral method uses an iterative solution to determine volume based on constants of liquid mass and gas mass. Differential method approximates volume by its initial values plus first-order differential changes in volume as functions of temperature and pressure.

Liley, B.↗
NASA NTRS1969

Water separating system Patent

Development of liquid separating system using capillary device connected to flexible bladder storage chamber

Beauregard, W. W.↗
NASA NTRS1973

Application of biological filters in water treatment systems

Silver chloride placed on or close to barrier kills bacteria as they arrive. Dead bacteria accumulate linearly, whereas previously, live bacteria accumulated exponentially. During continuous 30-day tests, no bacteriological contamination was found downstream of filters with silver chloride added.

Hurley, T. L.↗
NASA NTRS1975

Raman backscatter measurement research on water vapor systems

Raman backscatter techniques proved to be a useful remote sensing tool, whose full potential has not been realized. The types of information available from laser probes in atmospheric studies are reviewed. Detection levels for known Raman cross sections are calculated using the laser radar equation. Laboratory experiments performed for H2O, N2, SO2, O2 and HCL indicate that accurate wavelength cross sections need to be obtained, as well as more emphasis on obtaining accurate Raman cross sections of molecular species at wavelengths in the ultraviolet spectra.

Workman, G. L.↗