SEARCH · Search NASA
Results for “PROBABILITY”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Introducing “Identification Probability” for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies
Not Available
Direct Measurement of Charge Transfer Probability during Photodissociation of Few-keV OD + Beam
Not Available
Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing
Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.
Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
Not provided.
Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)
The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.
An Overview of the Hydrogen Extremely Low Probability of Rupture (HELPR) Toolkit for Probabilistic Structural Integrity Assessments When Transporting Hydrogen in Natural Gas Infrastructure
Presentation for Expert Workshop on Challenges and Solutions to implementation and reliable operation of Large-Scale Gaseous Hydrogen Infrastructure
Developing a fracture probability curve based on observable microstructure in additively manufactured ceramics
Explore the source record for details and available documents.
Tests of the probability table method for unresolved resonances [Slides]
Abstract not provided.
Guidance Regarding Probable Approaches and Instrumentation to Liquid Fuel Molten Salt Reactor Material Control and Accounting (MC&A) Rev. 1
With the rapid development of advanced reactors and numerous companies requesting pre-application engagements with the Nuclear Regulatory Commission (NRC), there is need for the NRC to best prepare themselves for new challenges presented by advanced technologies. One specific reactor type of interest is the molten salt reactor (MSR) that uses liquid salt as the fuel for the reactor. Liquid salt fuel reactors have specific challenges with regards to performing material control and accounting (MC&A). The challenges result from the fuel form being a continuous fissile material form rather than discrete units like is found with fuel assemblies in traditional light water reactors (LWRs). The goal of this document is to provide the NRC with guidance on the different approaches and technologies that may be proposed by reactor designers to address these problems to prepare staff for regulatory reviews.
Hydrogen Ignition Probabilities: Current and Proposed Framework
Explore the source record for details and available documents.
A computationally derived framework for predicting probability of PV module glass breakage by hail impact
Poster for presentation at 2025 Photovoltaics Reliability Workshop, hosted by NREL. Poster serves as a review requirement for project.
Direct prediction of quantum circuit outcome probabilities using physics-aware graph neural networks
Explore the source record for details and available documents.
Direct prediction of quantum circuit outcome probabilities using physics-aware graph neural networks
Explore the source record for details and available documents.
Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay
Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.