Search NASA⌕ Search

SEARCH · Search NASA

Results for “Systems 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Optimization Simulations for a Gamma-Ray Calibration Standard for a Cyclic Neutron Activation Analysis Pneumatic System at the Penn State Breazeale Reactor

For new experimental setups, the initial testing and calibrations can be expensive and time consuming without prior optimization. However, these issues can be mitigated using realistic modeling and simulation studies of the proposed system a priori. Specifically, an experiment can be performed virtually using realistic simulations and the expected results evaluated to inform adjustments of the experimental setup to achieve optimum results. At the Penn State Breazeale Reactor, a new pneumatic transfer system has been developed for the detection and characterization of short-lived fission fragments to enhance and complement existing nuclear data. The system accomplishes this task by transporting samples cyclically between an assortment of gamma-ray/neutron detectors and the reactor-core vicinity with sub-second transit times. The measured neutron flux paired with simulations of detector response using Geant4 and a custom module for estimating cascade summing effects and corrections were used to select sample-material masses for system characterization. With the optimized sample masses, an experimental counting plan for characterizing the repeatability of the newly developed pneumatic system was developed. Finally, the predictions made with these simulations allowed for optimization of the irradiation sample characteristics and gamma-ray detection system.

cyclic neutron activation, gamma-ray spectroscopy↗
DOE OSTI2019

Systems-Level Analysis of Oxygen Exposure in Zymomonas mobilis : Implications for Isoprenoid Production

ABSTRACT Zymomonas mobilis is an aerotolerant anaerobe and prolific ethanologen with attractive characteristics for industrial bioproduct generation. However, there is currently insufficient knowledge of the impact that environmental factors have on flux through industrially relevant biosynthetic pathways. Here, we examined the effect of oxygen exposure on metabolism and gene expression in Z. mobilis by combining targeted metabolomics, mRNA sequencing, and shotgun proteomics. We found that exposure to oxygen profoundly influenced metabolism, inducing both transient metabolic bottlenecks and long-term metabolic remodeling. In particular, oxygen induced a severe but temporary metabolic bottleneck in the methyl erythritol 4-phosphate pathway for isoprenoid biosynthesis caused by oxidative damage to the iron-sulfur cofactors of the final two enzymes in the pathway. This bottleneck was resolved with minimal changes in expression of isoprenoid biosynthetic enzymes. Instead, it was associated with pronounced upregulation of enzymes related to iron-sulfur cluster maintenance and biogenesis (i.e., flavodoxin reductase and the suf operon). We also detected major changes in glucose utilization in the presence of oxygen. Specifically, we observed increased gluconate production following exposure to oxygen, accounting for 18% of glucose uptake. Our results suggest that under aerobic conditions, electrons derived from the oxidation of glucose to gluconate are diverted to the electron transport chain, where they can minimize oxidative damage by reducing reactive oxygen species such as H 2 O 2 . This model is supported by the simultaneous upregulation of three membrane-bound dehydrogenases, cytochrome c peroxidase, and a cytochrome bd oxidase following exposure to oxygen. IMPORTANCE Microbially generated biofuels and bioproducts have the potential to provide a more environmentally sustainable alternative to fossil-fuel-derived products. In particular, isoprenoids, a diverse class of natural products, are chemically suitable for use as high-grade transport fuels and other commodity molecules. However, metabolic engineering for increased production of isoprenoids and other bioproducts is limited by an incomplete understanding of factors that control flux through biosynthetic pathways. Here, we examined the native regulation of the isoprenoid biosynthetic pathway in the biofuel producer Zymomonas mobilis . We leveraged oxygen exposure as a means to perturb carbon flux, allowing us to observe the formation and resolution of a metabolic bottleneck in the pathway. Our multi-omics analysis of this perturbation enabled us to identify key auxiliary enzymes whose expression correlates with increased production of isoprenoid precursors, which we propose as potential targets for future metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

System Reliability Analysis Capability and Surrogate Model Application in RAVEN

This report collect the effort performed to improve the reliability analysis capabilities of the RAVEN code and explore new opportunity in the usage of surrogate model by extending the current RAVEN capabilities to multi physics surrogate models and construction of surrogate models for high dimensionality fields.

31274↗
DOE OSTI2020

The vertical structure of convective mass-flux derived from modern radar systems: Data analysis in support of cumulus parametrization (Final Report)

The project delivered against all its major aims. We first developed a new calibration technique that enabled the construction of a long-term (17 years) calibrated radar dataset for the Darwin region. We documented both the technique and the data set in publications. We then developed a corresponding long-term dataset that characterises the large-scale state of the atmosphere. Equipped with both, we examined the relationship of key convective ensemble characteristics, such as cloud number and size, with the convective environment. In addition to the stated goals, we also applied the data set to i) estimate rainfall efficiency; ii) derive a new metric for convective organisation and iii) derive a longterm data set of convective mass-flux in the Darwin region and apply it to derive estimates of entrainment and detrainment rates in a convective cloud ensemble. Most of the research has been published or submitted for publication and will therefore only briefly summarised here, with links to or copies of the respective papers supplied.

58 GEOSCIENCES↗
DOE OSTI2021

SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning.

This report details the results of a three-fold investigation of sensitivity analysis (SA) for machine learning (ML) explainability (MLE): (1) the mathematical assessment of the fidelity of an explanation with respect to a learned ML model, (2) quantifying the trustworthiness of a prediction, and (3) the impact of MLE on the efficiency of end-users through multiple users studies. We focused on the cybersecurity domain as the data is inherently non-intuitive. As ML is being using in an increasing number of domains, including domains where being wrong can elicit high consequences, MLE has been proposed as a means of generating trust in a learned ML models by end users. However, little analysis has been performed to determine if the explanations accurately represent the target model and they themselves should be trusted beyond subjective inspection. Current state-of-the-art MLE techniques only provide a list of important features based on heuristic measures and/or make certain assumptions about the data and the model which are not representative of the real-world data and models. Further, most are designed without considering the usefulness by an end-user in a broader context. To address these issues, we present a notion of explanation fidelity based on Shapley values from cooperative game theory. We find that all of the investigated MLE explainability methods produce explanations that are incongruent with the ML model that is being explained. This is because they make critical assumptions about feature independence and linear feature interactions for computational reasons. We also find that in deployed, explanations are rarely used due to a variety of reason including that there are several other tools which are trusted more than the explanations and there is little incentive to use the explanations. In the cases when the explanations are used, we found that there is the danger that explanations persuade the end users to wrongly accept false positives and false negatives. However, ML model developers and maintainers find the explanations more useful to help ensure that the ML model does not have obvious biases. In light of these findings, we suggest a number of future directions including developing MLE methods that directly model non-linear model interactions and including design principles that take into account the usefulness of explanations to the end user. We also augment explanations with a set of trustworthiness measures that measure geometric aspects of the data to determine if the model output should be trusted.

97 MATHEMATICS AND COMPUTING↗