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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Compressive sensing optical design and simulation tool

A system or method for designing optical systems includes generating a mathematical model of an imaging system; tracing a plurality of light rays from a source through the model of the imaging system in a forward direction and a backward direction incident upon an arbitrarily defined detector location; calculating the total light rays incident upon the arbitrarily defined detector location in response to the plurality of light rays; tracing a series of calibration images first through the computational or compressive imager mathematical model; measuring an output set of parameters based on a plurality of detected rays calibration images at the defined detector location; storing the output set of parameters in software, and determining an estimated system transfer function.

42 ENGINEERING↗
DOE OSTI2013

Development of a tool dedicated to the evaluation of hydrogen term source for technological Wastes: assumptions, physical models, and validation

In radioactive waste packages hydrogen is generated, in one hand, from the radiolysis of wastes (mainly organic materials) and, in the other hand, from the radiolysis of water content in the cement matrix. In order to assess hydrogen generation 2 tools based on operational models have been developed. One is dedicated to the determination of the hydrogen source term issues from the radiolysis of the wastes: the STORAGE tool (Simulation Tool Of Emission Radiolysis Gas), the other deals with the hydrogen source term gas, produced by radiolysis of the cement matrices (the Damar tool). The approach used by the STORAGE tool for assessing the production rate of radiolysis gases is divided into five steps: 1) Specification of the data packages, in particular, inventories and radiological materials defined for a package medium; 2) Determination of radiochemical yields for the different constituents and the laws of behavior associated, this determination of radiochemical yields is made from the PRELOG database in which radiochemical yields in different irradiation conditions have been compiled; 3) Definition of hypothesis concerning the composition and the distribution of contamination inside the package to allow assessment of the power absorbed by the constituents; 4) Sum-up of all the contributions; And finally, 5) validation calculations by comparison with a reduced sampling of packages. Comparisons with measured values confirm the conservative character of the methodology and give confidence in the safety margins for safety analysis report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Simplifications for hydronic system models in modelica

Building systems and their heating, ventilation and air conditioning flow networks, are becoming increasingly complex. Some building energy simulation tools simulate these flow networks using pressure drop equations. These flow network models typically generate coupled algebraic nonlinear systems of equations, which become increasingly more difficult to solve as their sizes increase. This leads to longer computation times and can cause the solver to fail. These problems also arise when using the equation-based modelling language Modelica and Annex 60-based libraries. This may limit the applicability of the library to relatively small problems unless problems are restructured. This paper discusses two algebraic loop types and presents an approach that decouples algebraic loops into smaller parts, or removes them completely. The approach is applied to a case study model where an algebraic loop of 86 iteration variables is decoupled into smaller parts with a maximum of five iteration variables.

42 ENGINEERING↗
DOE OSTI2016

SU-F-J-208: Prompt Gamma Imaging-Based Prediction of Bragg Peak Position for Realistic Treatment Error Scenarios

Purpose: To quantify the accuracy in predicting the Bragg peak position using simulated in-room measurements of prompt gamma (PG) emissions for realistic treatment error scenarios that combine several sources of errors. Methods: Prompt gamma measurements by a knife-edge slit camera were simulated using an experimentally validated analytical simulation tool. Simulations were performed, for 143 treatment error scenarios, on an anthropomorphic phantom and a pencil beam scanning plan for nasal cavity. Three types of errors were considered: translation along each axis, rotation around each axis, and CT-calibration errors with magnitude ranging respectively, between −3 and 3 mm, −5 and 5 degrees, and between −5 and +5%. We investigated the correlation between the Bragg peak (BP) shift and the horizontal shift of PG profiles. The shifts were calculated between the planned (reference) position and the position by the error scenario. The prediction error for one spot was calculated as the absolute difference between the PG profile shift and the BP shift. Results: The PG shift was significantly and strongly correlated with the BP shift for 92% of the cases (p<0.0001, Pearson correlation coefficient R>0.8). Moderate but significant correlations were obtained for all cases that considered only CT-calibration errors and for 1 case that combined translation and CT-errors (p<0.0001, R ranged between 0.61 and 0.8). The average prediction errors for the simulated scenarios ranged between 0.08±0.07 and 1.67±1.3 mm (grand mean 0.66±0.76 mm). The prediction error was moderately correlated with the value of the BP shift (p=0, R=0.64). For the simulated scenarios the average BP shift ranged between −8±6.5 mm and 3±1.1 mm. Scenarios that considered combinations of the largest treatment errors were associated with large BP shifts. Conclusion: Simulations of in-room measurements demonstrate that prompt gamma profiles provide reliable estimation of the Bragg peak position for complex error scenarios. Yafei Xing and Luiza Bondar are funded by BEWARE grants from the Walloon Region. The work presents simulations results for a prompt gamma camera prototype developed by IBA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Risk Reduction and Training using Simulation Based Tools - 12180

Process Modeling and Simulation (M and S) has been used for many years in manufacturing and similar domains, as part of an industrial engineer's tool box. Traditionally, however, this technique has been employed in small, isolated projects where models were created from scratch, often making it time and cost prohibitive. Newport News Shipbuilding (NNS) has recognized the value of this predictive technique and what it offers in terms of risk reduction, cost avoidance and on-schedule performance of highly complex work. To facilitate implementation, NNS has been maturing a process and the software to rapidly deploy and reuse M and S based decision support tools in a variety of environments. Some examples of successful applications by NNS of this technique in the nuclear domain are a reactor refueling simulation based tool, a fuel handling facility simulation based tool and a tool for dynamic radiation exposure tracking. The next generation of M and S applications include expanding simulation based tools into immersive and interactive training. The applications discussed here take a tool box approach to creating simulation based decision support tools for maximum utility and return on investment. This approach involves creating a collection of simulation tools that can be used individually or integrated together for a larger application. The refueling simulation integrates with the fuel handling facility simulation to understand every aspect and dependency of the fuel handling evolutions. This approach translates nicely to other complex domains where real system experimentation is not feasible, such as nuclear fuel lifecycle and waste management. Similar concepts can also be applied to different types of simulation techniques. For example, a process simulation of liquid waste operations may be useful to streamline and plan operations, while a chemical model of the liquid waste composition is an important tool for making decisions with respect to waste disposition. Integrating these tools into a larger virtual system provides a tool for making larger strategic decisions. The key to integrating and creating these virtual environments is the software and the process used to build them. Although important steps in the direction of using simulation based tools for nuclear domain, the applications described here represent only a small cross section of possible benefits. The next generation of applications will, likely, focus on situational awareness and adaptive planning. Situational awareness refers to the ability to visualize in real time the state of operations. Some useful tools in this area are Geographic Information Systems (GIS), which help monitor and analyze geographically referenced information. Combined with such situational awareness capability, simulation tools can serve as the platform for adaptive planning tools. These are the tools that allow the decision maker to react to the changing environment in real time by synthesizing massive amounts of data into easily understood information. For the nuclear domains, this may mean creation of Virtual Nuclear Systems, from Virtual Waste Processing Plants to Virtual Nuclear Reactors. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗