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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

The Scale Method as an Approach for Prompt Fission Neutron Spectra Analysis and Evaluation

The Scale Method was applied for an analysis of experimental and evaluated data of Prompt Fission Neutron Spectra for different isotopes and incident neutron energies below 6 MeV. It was demonstrated that the method describes all experimental data sets in the outgoing neutron energy range 0–7 MeV within uncertainties. Theoretical arguments were provided to support the application of this method. Some important peculiarities connected with the neutron emission mechanism were identified, and proposals for future theoretical and experimental investigations were discussed. It was concluded that the new evaluations completed recently for {sup 235}U systematically overestimate the average energy of experimental prompt fission neutron spectra.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

A method for analyzing the non-stationary nucleation and overall transition kinetics: A case of water

We present the statistical method as a direct extension of the mean first-passage time concept to the analysis of molecular dynamics simulation data of a phase transformation. According to the method, the mean first-passage time trajectories for the first (i = 1) as well as for the subsequent (i = 2, 3, 4,…) nucleation events should be extracted that allows one to calculate the time-dependent nucleation rate, the critical value of the order parameter (the critical size), the waiting times for the nucleation events, and the growth law of the nuclei – i.e., all the terms, which are usually necessary to characterize the overall transition kinetics. There are no restrictions in the application of the method by the specific thermodynamic regions; and the nucleation rate parameters are extracted according to their basic definitions. The method differs from the Wedekind-Bartell scheme and its modification [A. V. Mokshin and B. N. Galimzyanov, J. Phys. Chem. B 116, 11959 (2012)], where the passage-times for the first (largest) nucleus are evaluated only and where the average waiting time for the first nucleation event is accessible instead of the true steady-state nucleation time scale. We demonstrate an efficiency of the method by its application to the analysis of the vapor-to-liquid transition kinetics in water at the different temperatures. The nucleation rate/time characteristics and the droplet growth parameters are computed on the basis of the coarse-grained molecular dynamics simulation data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data from: Comparing traditional and Bayesian approaches to ecological meta-analysis

Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

DOE-Managed HLW and SNF Research: FY15 EBS and Thermal Analysis Work Package Status.

This report examines the technical elements necessary to evaluate EBS concepts and perform thermal analysis of DOE-Managed SNF and HLW in the disposal settings of primary interest – argillite, crystalline, salt, and deep borehole. As the disposal design concept is composed of waste inventory, geologic setting, and engineered concept of operation, the engineered barrier system (EBS) falls into the last component of engineered concept of operation. The waste inventory for DOE-Managed HLW and SNF is closely examined, with specific attention to the number of waste packages, the size of waste packages, and the thermal output per package. As expected, the DOE-Managed HLW and SNF inventory has a much smaller volume, and hence smaller number of canisters, as well a lower thermal output, relative to a waste inventory that would include commercial spent nuclear fuel (CSNF). A survey of available data and methods from previous studies of thermal analysis indicates that, in some cases, thermo-hydrologic modeling will be necessary to appropriately address the problem. This report also outlines scope for FY16 work -- a key challenge identified is developing a methodology to effectively and efficiently evaluate EBS performance in each disposal setting on the basis of thermal analyses results.

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

A novel method to predict internal relative humidity in cementitious materials by 1H NMR

In sealed cementitious materials, the internal relative humidity (RH) decrease is mainly due to the water activity depression caused by menisci formation in partially-saturated pores. This process can be conveniently described with the evolution of the Kelvin radius. To obtain the Kelvin radius, a novel method based on the evolution of pore volumes quantified by {sup 1}H nuclear magnetic resonance (NMR) and mercury intrusion porosimetry (MIP) was developed. This approach was validated against experimental results for cementitious materials with a range of water to cement ratios from 0.30 to 0.46. A comparison between the Kelvin radius calculated with this approach with a previously published method using data obtained from MIP and chemical shrinkage was presented. A sensitivity analysis for the new prediction method was performed using a bootstrapping technique.

36 MATERIALS SCIENCE↗
DOE OSTI2023

classLog: Logistic regression for the classification of genetic sequences

Introduction Sequencing and phylogenetic classification have become a common task in human and animal diagnostic laboratories. It is routine to sequence pathogens to identify genetic variations of diagnostic significance and to use these data in realtime genomic contact tracing and surveillance. Under this paradigm, unprecedented volumes of data are generated that require rapid analysis to provide meaningful inference. Methods We present a machine learning logistic regression pipeline that can assign classifications to genetic sequence data. The pipeline implements an intuitive and customizable approach to developing a trained prediction model that runs in linear time complexity, generating accurate output rapidly, even with incomplete data. Our approach was benchmarked against porcine respiratory and reproductive syndrome virus (PRRSv) and swine H1 influenza A virus (IAV) datasets. Trained classifiers were tested against sequences and simulated datasets that artificially degraded sequence quality at 0, 10, 20, 30, and 40%. Results When applied to a poor-quality sequence data, the classifier achieved between >85% to 95% accuracy for the PRRSv and the swine H1 IAV HA dataset and this increased to near perfect accuracy when using the full dataset. The model also identifies amino acid positions used to determine genetic clade identity through a feature selection ranking within the model. These positions can be mapped onto a maximum-likelihood phylogenetic tree, allowing for the inference of clade defining mutations. Discussion Our approach is implemented as a python package with code available at https://github.com/flu-crew/classLog .

Zeller, Michael A.↗
DOE OSTI2015

Forensic Applicability of Femur Subtrochanteric Shape to Ancestry Assessment in Thai and White American Males

Abstract Ancestry assessment from the postcranial skeleton presents a significant challenge to forensic anthropologists. However, metric dimensions of the femur subtrochanteric region are believed to distinguish between individuals of Asian and non‐Asian descent. This study tests the discriminatory power of subtrochanteric shape using modern samples of 128 Thai and 77 White American males. Results indicate that the samples' platymeric index distributions are significantly different ( p ≤ 0.001), with the Thai platymeric index range generally lower and the White American range generally higher. While the application of ancestry assessment methods developed from Native American subtrochanteric data results in low correct classification rates for the Thai sample (50.8–57.8%), adapting these methods to the current samples leads to better classification. The Thai data may be more useful in forensic analysis than previously published subtrochanteric data derived from Native American samples. Adapting methods to include appropriate geographic and contemporaneous populations increases the accuracy of femur subtrochanteric ancestry methods.

Tallman, Sean D.↗
DOE OSTI2018

Uncertainty Analysis of Consequence Management (CM) Data Products.

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine learning in analytical spectroscopy for nuclear diagnostics [Invited]

Analytical spectroscopy methods have shown many possible uses for nuclear material diagnostics and measurements in recent studies. In particular, the application potential for various atomic spectroscopy techniques is uniquely diverse and generates interest across a wide range of nuclear science areas. Over the last decade, techniques such as laser-induced breakdown spectroscopy, Raman spectroscopy, and x-ray fluorescence spectroscopy have yielded considerable improvements in the diagnostic analysis of nuclear materials, especially with machine learning implementations. These techniques have been applied for analytical solutions to problems concerning nuclear forensics, nuclear fuel manufacturing, nuclear fuel quality control, and general diagnostic analysis of nuclear materials. The data yielded from atomic spectroscopy methods provide innovative solutions to problems surrounding the characterization of nuclear materials, particularly for compounds with complex chemistry. Implementing these optical spectroscopy techniques can provide comprehensive new insights into the chemical analysis of nuclear materials. In particular, recent advances coupling machine learning methods to the processing of atomic emission spectra have yielded novel, robust solutions for nuclear material characterization. This review paper will provide a summation of several of these recent advances and will discuss key experimental studies that have advanced the use of analytical atomic spectroscopy techniques as active tools for nuclear diagnostic measurements.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2017

Utilizing the SACADA system to collect simulator training data to inform human reliability analyses - 169

Lack of appropriate human performance data for the development of human reliability analysis (HRA) methods has been identified as a key issue contributing to the method-to-method variability among the HRA methods. The United States Nuclear Regulatory Commission (NRC) worked with domain experts to develop the Scenario Authoring, Characterization, and Debriefing Application (SACADA) system to collect nuclear power plants' licensed operator simulator training and examination data to inform HRA. In the past two decades, significant improvements in understanding and modelling human performance in HRA have affected the perspectives of the types of data to be collected for HRA. A significant change is that human performance is strongly dependent on the context of the tasks to be performed. To incorporate this understanding, the SACADA data taxonomy collects the context and actual performance information of the training objective elements (TOEs) in nuclear plant operator simulator training. This provides a framework consistent with HRA to use context to estimate human error probabilities. It is recognized that measuring crew performance is equally important as improving operator training. SACADA is designed to support the nuclear trainers in capturing this important information that is used to identify training weaknesses and drive continuous improvement. This paper discusses the SACADA taxonomy, the SACADA system's functions, and the supporting software design to enable the SACADA system to be practically implemented in the nuclear power plants' operator simulator training program to collect data for tracking operator performance and HRA analysis. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

First- and Second-Order Methods for Online Convolutional Dictionary Learning

Convolutional sparse representations are a form of sparse representation with a structured, translation-invariant dictionary. Most convolutional dictionary learning algorithms to date operate in batch mode, requiring simultaneous access to all training images during the learning process, which results in very high memory usage and severely limits the training data size that can be used. Very recently, however, a number of authors have considered the design of online convolutional dictionary learning algorithms that offer far better scaling of memory and computational cost with training set size than batch methods. This study extends our prior work, improving a number of aspects of our previous algorithm; proposing an entirely new one, with better performance, that supports the inclusion of a spatial mask for learning from incomplete data; and providing a rigorous theoretical analysis of these methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An innovative method to build a comprehensive kinetic model for air injection using TGA/DSC experiments

In order to study the AIP (air injection process), a practical method was developed to obtain the kinetic data for an AIP by matching the simulation model results with the TGA (thermogravimetry analysis) experiments in our previous work. However, one shortcoming for that method is the non-uniqueness of the kinetic data obtained. The other shortcoming is that TGA cannot measure the exothermic characteristics of a crude oil. The enthalpy values of the reactions are crucial for developing a comprehensive kinetic model as the enthalpy represents the heat generation of the chemical reaction and dominants the heat effect during the AIP. Here in this study, an innovative method is proposed to build a comprehensive kinetic model by combining both TGA/DSC (differential scanning calorimetry) experiments and simulation. In the low temperature interval, the distillation dominant stage is differentiated from the LTO (low temperature oxidation) stage by comparing the nitrogen purging TGA experiments and the air purging TGA experiments. Then, the LTO stage is further divided into several sub-divided reaction stages with corresponding temperature intervals by analyzing the TGA/DSC data. The kinetic data are estimated by applying the Arrhenius method to sub-divided reaction stages of the TGA experiments. In order to obtain the enthalpy values for oxidation reactions, the negative heat flow of the distillation process is eliminated by subtracting the nitrogen purging DSC data from the air purging DSC data, and the enthalpy values of corresponding reaction stages are obtained from the subtracted heat flow curve whose area of the exothermic peak represents the enthalpy value of a stage. Moreover, the pseudo components are defined based on the nitrogen purging TGA experiments which are associated with the sub-divided reaction stages defined previously to complete the kinetic model. Finally, the kinetic model is validated with the air purging TGA experiments. This study shows a detailed workflow to build a comprehensive kinetic model for AIP by TGA/DSC experiments. An application to a crude oil is also presented to demonstrate its practicability.

42 ENGINEERING↗
DOE OSTI2019

An Independent Analysis of the Spitzer/IRAC Phase Curves of WASP43 b

We present here a reanalysis of the Spitzer Space Telescope phase curves of the hot Jupiter WASP43 b, using the wavelet pixel-independent component analysis, a blind signal-source separation method. The data analyzed were recorded with the Infrared Array Camera and consisted of two visits at 3.6 μm, and one visit at 4.5 μm, each visit covering one transit and two eclipse events. To test the robustness of our technique we repeated the analysis on smaller portions of the phase curves, and by employing different instrument ramp models. Our reanalysis presents significant updates of the planetary parameters compared to those reported in the original phase curve study of WASP43 b. In particular, we found (1) higher nightside temperatures, (2) smaller hotspot offsets, (3) a greater consistency (∼1σ) between the two 3.6 μm visits, and (4) a greater similarity with the predictions of the atmospheric circulation models. Our parameter results are consistent within 1σ with those reported by a recent reanalysis of the same data sets. For each visit we studied the variation of the retrieved transit parameters as a function of various sets of stellar limb-darkening coefficients, finding significant degeneracy between the limb-darkening models and the analysis output. Furthermore, we performed the analysis of the single transit and eclipse events, and we examined the differences between these results with the ones obtained with the whole phase curve. Finally we provide a formula useful to optimize the trade-off between precision and duration of observations of transiting exoplanets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Digital holography for real-time non-invasive monitoring of larval fish at power plant intakes

Effective evaluation of technological and operational approaches to reduce entrainment of marine organisms at cooling water intake structures (CWIS) requires accurate organism-sensing systems. Current detection methods lead to large temporal data gaps, require tedious manual analysis, and are fatal to organisms. In this report we describe integrating deep learning with a non-lethal, non-intrusive imaging method—digital holography—to rapidly detect fish larvae. Laboratory experiments demonstrated that the instrument could successfully image fish larvae at flow rates exceeding ranges seen in CWIS. Holograms of two fish larvae species, in the presence of bubbles and detritus, were collected to build a large database for training a lightweight convolutional neural network. The model achieves 97% extraction accuracy in quantifying larvae, and distinguishing them from other particles, including detritus and bubbles, when applied to a dataset of manually classified images, exceeding previous metrics for non-lethal, accurate, and real-time detection. These results demonstrate the potential of in situ holographic imaging for monitoring endangered larval fish species at power plant intake structures, and for high-fidelity, real-time applications in monitoring aquatic ichthyoplankton.

endangered species↗
DOE OSTI2016

Salvage Radiation Therapy Dose Response for Biochemical Failure of Prostate Cancer After Prostatectomy—A Multi-Institutional Observational Study

Purpose: To determine whether a dose-response relationship exists for salvage radiation therapy (RT) of biochemical failure after prostatectomy for prostate cancer. Methods and Materials: Individual data from 1108 patients who underwent salvage RT at 10 academic centers were pooled. The cohort was enriched for selection criteria more likely associated with tumor recurrence in the prostate bed (margin positive and pre-RT prostate-specific antigen [PSA] level of ≤2.0 ng/mL) and without the confounding of planned androgen suppression. The cumulative incidence of biochemical failure and distant metastasis over time was computed, and competing risks hazard regression models were used to investigate the association between potential predictors and these outcomes. The association of radiation dose with outcomes was the primary focus. Results: With a 65.2-month follow-up duration, the 5- and 10-year estimates of freedom from post-RT biochemical failure (PSA level >0.2 ng/mL and rising) was 63.5% and 49.8%, respectively, and the cumulative incidence of distant metastasis was 12.4% by 10 years. A Gleason score of ≥7, higher pre-RT PSA level, extraprostatic tumor extension, and seminal vesicle invasion were associated with worse biochemical failure and distant metastasis outcomes. A salvage radiation dose of ≥66.0 Gy was associated with a reduced cumulative incidence of biochemical failure, but not of distant metastasis. Conclusions: The use of salvage radiation doses of ≥66.0 Gy are supported by evidence presented in the present multicenter pooled analysis of individual patient data. The observational reporting method, limited sample size, few distant metastasis events, modest follow-up duration, and elective use of salvage therapy might have diminished the opportunity to identify an association between the radiation dose and this endpoint.

62 RADIOLOGY AND NUCLEAR MEDICINE↗