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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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NASA NTRS2004

The PMDP Roadmap

NASA's complex and highly technical missions rely on effective project teams and managers. Since 1993, through its Project Management Development Process (PMDP), the Academy of Program and Project Leadership (APPL) has offered direction to the Agency's project practitioners as they advance in their careers. PMDP helps identify and sequence professional experiences, courses, and other project-based learning experiences that support individual career goals and center activities by outlining competencies at four levels of development. The result is that PMDP provides NASA project practitioners with a road map to the knowledge and competencies appropriate for their job and the jobs to which they aspire. Plus, new this year, APPL has rolled out its electronic Project Management Development Process (ePMDP) tool, a learning management system that includes a dynamic presentation of the PMDP levels, competency areas, competency organizational structures, Individual Development Plans (IDP), and online PMDP enrollment. APPL's website, www.appl.nasa.gov, provides access to ePMDP as well as other online resources for NASA practitioners enrolled in the Project Management Development Process.

Source record↗
DOE OSTI2022

Geochemistry and Multiomics Data Differentiate Streams in Pennsylvania Based on Unconventional Oil and Gas Activity

Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales. Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed (n = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry (n = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development.

16S rRNA↗
DOE OSTI2020

The Spatiotemporal Evolution of Granular Microslip Precursors to Laboratory Earthquakes

Laboratory earthquake experiments provide important observational constraints for our understanding of earthquake physics. Here we leverage continuous waveform data from a network of piezoceramic sensors to study the spatial and temporal evolution of microslip activity during a shear experiment with synthetic fault gouge. We combine machine learning techniques with ray theoretical seismology to detect, associate, and locate tens of thousands of microslip events within the gouge layer. Microslip activity is concentrated near the center of the system but is highly variable in space and time. While microslip activity rate increases as failure approaches, the spatiotemporal evolution can differ substantially between stick-slip cycles. These results illustrate that even within a single, well-constrained laboratory experiment, the dynamics of earthquake nucleation can be highly complex.

58 GEOSCIENCES↗
DOE OSTI2017

Every apple has a voice: using stable isotopes to teach about food sourcing and the water cycle

Abstract. Agricultural crops such as fruits take up irrigation and meteoric water and incorporate it into their tissue ( fruit water ) during growth, and the geographic origin of a fruit may be traced by comparing the H and O stable isotope composition ( δ 2 H and δ 18 O values) of fruit water to the global geospatial distribution of H and O stable isotopes in precipitation. This connection between common fruits and the global water cycle provides an access point to connect with a variety of demographic groups to educate about isotope hydrology and the water cycle. Within the context of a 1-day outreach activity designed for a wide spectrum of participants (high school students, undergraduate students, high school science teachers) we developed introductory lecture materials, in-class participatory demonstrations of fruit water isotopic measurement in real time, and a computer lab exercise to couple actual fruit water isotope data with open-source online geospatial analysis software. We assessed learning outcomes with pre- and post-tests tied to learning objectives, as well as participant feedback surveys. Results indicate that this outreach activity provided effective lessons on the basics of stable isotope hydrology and the water cycle. However, the computer lab exercise needs to be more specifically tailored to the abilities of each participant group. This pilot study provides a foundation for further development of outreach materials that can effectively engage a range of participant groups in learning about the water cycle and the ways in which humans modify the water cycle through agricultural activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The LSST AGN Data Challenge: Selection Methods

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗
DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Machine learning prediction of enzyme optimum pH

The relationship between pH and enzyme catalytic activity, especially the optimal pH (pH opt ) at which enzymes function, is critical for biotechnological applications. Hence, computational methods to predict pH opt will enhance enzyme discovery and design by facilitating accurate identification of enzymes that function optimally at specific pH levels, and by elucidating sequence-function relationships. Here, in this study, we proposed and evaluated various machine learning methods for predicting pH opt , conducting extensive hyperparameter optimization and training over 11,000 model instances. Our results demonstrate that models utilizing language model embeddings markedly outperform other methods in predicting pHopt. We present EpHod, the best-performing model, to predict pHopt, making it publicly available to researchers. From sequence data, EpHod directly learns structural and biophysical features that relate to pH opt , including proximity of residues to the catalytic centre and the accessibility of solvent molecules. Overall, EpHod presents a promising advancement in pH opt prediction and will potentially speed up the development of enzyme technologies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

The Application of Lean Thinking Principles and Kaizen Practices for the Successful Development and Implementation of the Ares I-X Flight Test Rocket and Mission

On October 28, 2009 the Ares I-X flight test rocket launched from Kennedy Space Center and flew its suborbital trajectory as designed. The mission was successfully completed as data from the test, and associated development activities were analyzed, transferred to stakeholders, and well documented. A positive lesson learned from Ares I-X was that the application of lean thinking principles and kaizen practices was very effective in streamlining development activities. Ares I-X, like other historical rocket development projects, was hampered by technical, cost, and schedule challenges and if not addressed boldly could have resulted in cancellation of the test. The mission management team conducted nine major meetings, referred to as lean events, across its elements to assess plans, procedures, processes, requirements, controls, culture, organization, use of resources, and anything that could be changed to optimize schedule or reduce risk. The preeminent aspect of the lean events was the focus on value added activities and the removal or at least reduction in non-value added activities. Trained Lean Six Sigma facilitators assisted the Ares I-X developers in conducting the lean events. They indirectly helped formulate the mission s own unique methodology for assessing schedule. A core team was selected to lead the events and report to the mission manager. Each activity leveraged specialized participants to analyze the subject matter and its related processes and then recommended alternatives and solutions. Stakeholders were the event champions. They empowered and encouraged the team to succeed. The keys to success were thorough preparation, honest dialog, small groups, adherence to the Ares I-X ground rules, and accountability through disciplined reporting and tracking of actions. This lean event formula was game-changing as demonstrated by Ares I-X. It is highly recommended as a management tool to help develop other complex systems efficiently. The key benefits for Ares I-X were obtaining unambiguous schedule margin, defining enabling options for risk reduction, and most importantly a stronger more unified team.

Askins, B. R.↗
DOE OSTI2021

EXPRES. II. Searching for Planets around Active Stars: A Case Study of HD 101501

By controlling instrumental errors to below 10 cm s{sup −1}, the EXtreme PREcision Spectrograph (EXPRES) allows for a more insightful study of photospheric velocities that can mask weak Keplerian signals. Gaussian processes (GP) have become a standard tool for modeling correlated noise in radial velocity data sets. While GPs are constrained and motivated by physical properties of the star, in some cases they are still flexible enough to absorb unresolved Keplerian signals. We apply GP regression to EXPRES radial velocity measurements of the 3.5 Gyr old chromospherically active Sun-like star, HD 101501. We obtain tight constraints on the stellar rotation period and the evolution of spot distributions using 28 seasons of ground-based photometry, as well as recent Transiting Exoplanet Survey Satellite data. Light-curve inversion was carried out on both photometry data sets to reveal the spot distribution and spot evolution timescales on the star. We find that the >5 m s{sup −1} rms radial velocity variations in HD 101501 are well modeled with a GP stellar activity model without planets, yielding a residual rms scatter of 45 cm s{sup −1}. We carry out simulations, injecting and recovering signals with the GP framework, to demonstrate that high-cadence observations are required to use GPs most efficiently to detect low-mass planets around active stars like HD 101501. Sparse sampling prevents GPs from learning the correlated noise structure and can allow it to absorb prospective Keplerian signals. We quantify the moderate to high-cadence monitoring that provides the necessary information to disentangle photospheric features using GPs and to detect planets around active stars.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Insights from machine learning of carbon electrodes for electric double layer capacitors

Recent years have witnessed the broad use of carbon electrodes for electric double layer capacitors (EDLCs) because of large surface area, high porosity and low cost. Whereas experimental investigations are mostly focused on the device performance, computational studies have been rarely concerned with electrochemical properties at conditions remote from equilibrium, limiting their direct applications to materials design. Through a comprehensive analysis of extensive experimental data with various machine-learning methods, we report herein quantitative correlations between the structural features of carbon electrodes and the in-operando behavior of EDLCs including energy and power density. Machine learning allows us to identify important characteristics of activated carbons useful to optimize their efficiency in energy storage.

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

A data analytical approach for assessing the efficacy of Operational Technology active defenses against insider threats

In recent years, the need for Operational Technology (OT) defenses has been recognized, serving as an additional line of defense when Information Technology (IT) defenses are bypassed. This is no longer considered an uncommon possibility when dealing with advanced persistent threat (APT) actors expected to be state-sponsored and receiving insider assistance. In these extreme adversarial situations, OT defenses aim to provide another layer of defense for the system, introduced directly at the physical process level, as described by the sensors data, the system model, and control actions. Just like IT defenses, two schools of thought, i.e., passive and active defenses, have emerged to address this challenge. In active defenses, representing the focus of this paper, known signatures, synthesized based on the system's unique characteristics, are inserted into the system. In contradistinction, passive methods rely solely on observing system behavior in search of patterns of normal behavior with deviations thereof representing abnormal behavior. In their most sophisticated implementations, both passive and active defenses rely on the use of data analytics to identify the patterns and synthesize the observed and/or inserted signatures. Past research has shown that passive defenses may be bypassed by APT actors relying on data analytics and their intimate knowledge of the system to evade detection by respecting the patterns identified by the defenders. Here this manuscript explores the use of active defenses under the assumption that the attacker has privileged access to the system, including access to the system's model and sensors data. Specifically, this manuscript assesses the ability of active defenses to remain invisible to the attackers, and discusses the associated challenges that must be addressed to ensure their resiliency against APT actors.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Biomimetic Control over Bimetallic Nanoparticle Structure and Activity via Peptide Capping Ligand Sequence

Here, the controlled design of bimetallic nanoparticles (BNPs) is a key goal in tailoring their catalytic properties. Recently, biomimetic pathways demonstrated potent control over the distribution of different metals within BNPs, but a direct understanding of the peptide effect on the compositional distribution at the interparticle and intraparticle levels remains lacking. We synthesized two sets of PtAu systems with two peptides and correlated their structure, composition, and distributions with the catalytic activity. Structural and compositional analyses were performed by a combined machine learning-assisted refinement of X-ray absorption spectra and Z-contrast measurements by scanning transmission electron microscopy. The difference in the catalytic activities between nanoparticles synthesized with different peptides was attributed to the details of interparticle distribution of Pt and Au across these markedly heterogeneous systems, comprising Pt-rich, Au-rich, and Au core/Pt shell nanoparticles. The total amount of Pt in the shells of the BNPs was proposed to be the key catalytic activity descriptor. This approach can be extended to other systems of metals and peptides to facilitate the targeted design of catalysts with the desired activity.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Full atomistic reaction mechanism with kinetics for CO reduction on Cu(100) from ab initio molecular dynamics free-energy calculations at 298 K

Significance Converting CO 2 to fuels is of great interest nowadays. Copper (Cu) is the only metal that produces hydrocarbon products, making it of interest for learning the reaction mechanisms underlying the selectivity and activity of Cu catalysts. In this report, we examine the reaction mechanism of CO reduction on Cu(100) at pH 7 including explicitly the aqueous solvent with ab initio molecular metadynamics simulations (AIMμD) free-energy calculations, which we find to explain all experimental observations. We expect that this understanding will suggest how to modify the Cu catalyst or replace it with a new material to enhance production of just a single such product while reducing the energy requirements (overpotential).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
DOE OSTI2022

Quantification of neural networks uncertainties with applications to SAFARI-1 axial neutron flux profiles

Deep Neural Networks (DNNs) have been widely used as a data-driven modelling tool in nuclear engineering. However, as a Machine Learning model, Artificial Neural Network (ANN) predictions are subjected to uncertainties originating from the noise in training data, incomplete coverage of the domain, and imperfect neural network architectures. In this work, we target at quantifying the prediction/approximation uncertainties of ANNs using Monte Carlo Dropout (MCD), as well as Bayesian Neural Networks (BNNs) which are solved by variational inference. With a demonstration problem in which neural networks are used to predict the assembly axial neutron flux profiles, the results have shown that the three different neural network models (regular DNNs, DNNs solved with MCD and BNNs) can produce results that agree very well among each other and with the measurement data, on cycles that are not used in the training process. Besides the excellent generalization capability, the uncertainty bands produced by MCD and BNN agree very well, and in general, they can fully envelop the noisy measurement data points. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Drive Electric Vermont Case Study

Currently in the United States, the heavy majority of plug-in electric vehicle (PEV) sales have been in highly conducive, selected, metropolitan areas; opposed to more broad distribution across the country. The U.S. Department of Energy’s EV Everywhere Grand Challenge is looking carefully at the barriers and opportunities that exist to enable small and midsize communities to partake in the PEV market and benefit from the economic and environmental advantages of PEVs. In order to gain insight into these challenges and barriers, DOE selected a success story (i.e., Drive Electric Vermont) as the subject of this case study, as the state of Vermont is tied with Detroit, Michigan in having the highest percentage of 2014 (most recent complete data) PEV registrations for cold weather U.S. cities and has seen more than a sixfold increase in charging stations over the last three years. The overall objective of this case study was to use the lessons learned from Drive Electric Vermont to determine what activities are most effective at encouraging acquisitions of PEVs and deployment of charging infrastructure in small to midsize communities, prioritizing and sequencing their implementation, identifying robust means for extrapolation, and applying this understanding to other small to midsize communities across the nation. The Drive Electric Vermont Program was formed in 2012 with a goal of increasing the use of electrified transportation in Vermont through policy development, education and outreach, and infrastructure development. The Drive Electric Vermont Program can be broadly broken into four components: (1) strategic planning/leadership, (2) stakeholder/partnership development, (3) education and outreach, and (4) incentives. The early phases of the program focused heavily on strategic planning, and stakeholder and partnership development, followed by a transition to education and outreach activities, charging infrastructure development, and grant and incentive programs that support charging infrastructure deployment and PEV purchases. Future plans include an additional round of consumer/dealer PEV incentives, continuation of the marketing campaign, and utilization of consumer surveys to develop new campaign themes and to optimize information distribution channels. In Vermont, the number of PEVs grew from 88 in July 2012 to 1,113 in January 2016, with a high percentage of the PEVs being plug-in hybrids (i.e., 865) versus all electric vehicles (i.e., 248). The most popular PEVs are Toyota’s Prius Plug-in and Ford’s C-Max Energi, which make up a combined 48% of the total PEV ownership in the state. The PEVs are predominantly clustered in four counties: Lamoille, Caledonia, Washington, and Chittenden. There is no evident correlation between community size and PEV penetration; however, over 70% of PEV owners are in communities of 1,000 to 10,000 people. On the other hand, there is a close correlation between where PEVs are registered and where they publically charge. The number of PEV charging stations in Vermont grew from 17 in January 2013 to 111 in January 2016, at a variety of charging venues, including retail, parking (short term and long term), workplace, dealerships, hotels, education, leisure, and hospitals. The vast majority of the charging venues contain Level 2 electric vehicle supply equipment (EVSE) solely or combined with Level 1 EVSE or direct current fast charging. The average power transferred per charging event for communities across Vermont ranged from 4.8 to 13.8 kWh for direct current fast charging and 1.0 to 11.9 kWh for and Level 2 EVSE. Over half the charging locations offer free charging. For those that do require payment, different pricing models are employed, including monthly memberships, hourly payment (where there is often a higher fee for the first hour followed by a lower hourly fee for the remaining hours), and energy use based on kWh drawn from the EVSE. There is no correlation between pricing models and community size and region, the strongest correlation is by network provider. Charging events across Vermont generally correlate with east-west bands of chargers (Figure ES-1) running from Burlington to Montpelier and in the area of Rutland.

33 ADVANCED PROPULSION SYSTEMS↗