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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction

BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed. METHODS: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies of opioid use disorder and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex of people who died of opioid overdose and control individuals. RESULTS: Here we identified 211 highly interrelated genes identified by genome-wide association studies or dysregulation in the dorsolateral prefrontal cortex of people who died of opioid overdose that implicated the Akt, BDNF (brain-derived neurotrophic factor), and ERK (extracellular signal-regulated kinase) pathways, identifying 414 drugs targeting 48 of these opioid addiction–associated genes. Some of the identified drugs are approved to treat other substance use disorders or depression. CONCLUSIONS: Our synthesis of multiomics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Forest Soil Carbon Efflux Evaluation Across China: A New Estimate With Machine Learning

Forest soil respiration (Rs) plays an important role in the carbon balance of terrestrial ecosystems. China's forest occupies a large part of the world's forest. However, due to the lack of integrated observation data and appropriate upscaling methodologies, substantial uncertainties exist in the Rs evaluation, which limits our understanding of the carbon balance. Here, we re-evaluated the total soil carbon effluxes across China by combining field observations from 634 published annual Rs with a machine learning technique (i.e., Random Forest (RF)). Our results revealed that the combination of systematic measurements with the RF model allowed a definite estimate. The average annual Rs was 773.6 g C m -2 yr -1 , ranging from 404.6 to 1,772.3 g C m -2 yr -1 . Total forest soil carbon effluxes amounted to 1.17 Pg C yr -1 in China. Geographically, annual Rs showed a clear spatially increasing trend from northeast to southwest. Forest type is an important factor in determining the soil respiration rate. Bamboo and Evergreen broadleaf forests were higher than other types of forests. In conclusion, these results provide a unique insight into the magnitudes and mechanisms of soil CO 2 emissions in China's forest ecosystems.

58 GEOSCIENCES↗
DOE OSTI2021

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Cell-to-cell interaction requires optimal positioning of a pilus tip adhesin modulated by gram-positive transpeptidase enzymes

Assembly of pili on the gram-positive bacterial cell wall involves 2 conserved transpeptidase enzymes named sortases: One for polymerization of pilin subunits and another for anchoring pili to peptidoglycan. How this machine controls pilus length and whether pilus length is critical for cell-to-cell interactions remain unknown. We report here in Actinomyces oris , a key colonizer in the development of oral biofilms, that genetic disruption of its housekeeping sortase SrtA generates exceedingly long pili, catalyzed by its pilus-specific sortase SrtC2 that possesses both pilus polymerization and cell wall anchoring functions. Remarkably, the srtA-deficient mutant fails to mediate interspecies interactions, or coaggregation, even though the coaggregation factor CafA is present at the pilus tip. Furthermore, increasing ectopic expression of srtA in the mutant progressively shortens pilus length and restores coaggregation accordingly, while elevated levels of shaft pilins and SrtC2 produce long pili and block coaggregation by SrtA + bacteria. With structural studies, we uncovered 2 key structural elements in SrtA that partake in recognition of pilin substrates and regulate pilus length by inducing the capture and transfer of pilus polymers to the cell wall. Evidently, coaggregation requires proper positioning of the tip adhesin CafA via modulation of pilus length by the housekeeping sortase SrtA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Reduced-Order Parameterized Short-Circuit Current Model of Inverter-Interfaced Distributed Generators

The short-circuit response of inverter-interfaced distributed generators (IIDGs) is not adequately represented in many conventional protection studies. This paper presents an in-depth analysis of IIDG behavior during grid faults and proposes a more accurate reduced-order parameterized short-circuit current (RPSC) model of inverters. Typical inverter components are thoroughly investigated to identify those that play dominant roles during faults. The paper shows that the current limiter is the dominant factor for the steady-state fault-current of inverter; while the inverter filter along with the severity of the voltage disturbance largely determine the initial transient spike of inverter fault current. The proposed model is low-order and can be used in large scale simulations. The parameters of the proposed RPSC model can be extracted from laboratory experiments without requiring proprietary manufacturer information. The proposed fault-current model is analogous to the well-known synchronous machine model that segregates the inverter fault current into subtransient, transient, and steady-state fault currents. Finally, experimental and simulation tests are presented to validate the model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nuclear Quantum Effects on the Electronic Structure of Water and Ice

The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of the hydrogen atoms. Despite numerous previous studies, a comprehensive understanding of the nuclear quantum effects (NQEs) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Development of a Technical Basis and Guidance for Advanced SMR Function Allocation

This report presents the results from three key activities for FY13 that influence the definition of new concepts of operations for advanced Small Modular Reactors (AdvSMR: a) the development of a framework for the analysis of the functional environmental, and structural attributes, b) the effect that new technologies and operational concepts would have on the way functions are allocated to humans or machines or combinations of the two, and c) the relationship between new concepts of operations, new function allocations, and human performance requirements.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns

The comprehensive understanding of the residential electricity consumption patterns (ECPs) and how they relate to household characteristics can contribute to energy efficiency improvement and electricity consumption reduction in the residential sector. After recognizing the limitations of current studies (i.e. unreasonable typical ECP (TECP) extraction method and the problem of multicollinearity and interpretability for regression and machine learning models), this paper proposes an association rule mining based quantitative analysis approach of household characteristics impact on residential ECPs trying to address them together. First, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to create seasonal TECP of each individual customer only for weekdays. K-means is then adopted to group all the TECPs into several clusters. An enhanced Apriori algorithm is proposed to reveal the relationships between TECPs and thirty five factors covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Results of the case study using 3326 records containing smart metering data and survey information in Ireland suggest that socio-demographic and cooking related factors such as employment status, occupants and whether cook by electricity have strong significant associations with TECPs, while attitudes related factors almost have no effect on TECPs. The results also indicate that those households with more than one person are more likely to change ECP across seasons. The proposed approach and the findings of this study can help to support decisions about how to reduce electricity consumption and CO2 emissions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Estimating manufacturing electricity costs by simulating dependence between production parameters

The electricity costs associated with manufacturing generally consist of an energy charge and a demand charge, which are calculated by total kWh × kWh and peak kW × kW, respectively. While these two charges have been extensively studied with production scheduling, dependence between production parameters has not been thoroughly investigated for production energy costs when the production parameters are assumed to be independent. Such dependence, however, can significantly affect the peak kW and resulting energy costs. Therefore, industrial practitioners need a tool to simulate the production power demand and cost to evaluate energy costs for each production plan and for any dependence in the parameters. In this paper, we propose a method for simulating the power demand at the machine and facility levels, focusing on the dependence in milling machine parameters. Using probabilistic techniques with copulas, we quantify the degree of dependence between two milling parameters (processing times and processing amounts) and build a power demand simulator combining discrete event simulation and numerical simulation models. From 35 illustrative examples, we show that a production strategy to adjust dependence can reduce the peak kW and energy costs by more than 15% and 4%, respectively. In addition to dependence, less variability in processing times results in less peak kW and energy costs. Other factors such as a marginal probability distribution and copula types having the same degree of variability have been found to have limited effects on reducing energy costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Highly tritiated water processing by isotopic exchange

Highly tritiated water (HTW) is produced in fusion machines and one of the promising technologies to process it is isotopic exchange. 3 kinds of Pt-catalyzed zeolite (13X-APG, CBV-100-CY and HiSiv-1000) were tested as candidates for isotopic exchange of highly tritiated water (HTW), and CBV-100-CY (Na-Y type with a SiO{sub 2}/Al{sub 2}O{sub 3} ratio of ∼ 5.0) shows the best performance. Small-scale tritium testing indicates that this method is efficient for reaching an exchange factor (EF) of 100. Full-scale non-tritium testing implies that an EF of 300 can be achieved in 24 hours of operation if a temperature gradient is applied along the column. For the isotopic exchange, deuterium recycled from the Isotope Separation System (deuterium with 1% T and/or 200 ppm T) should be employed, and the tritiated water regenerated from the Pt-catalyzed zeolite bed after isotopic exchange should be transferred to Water Detritiation System (WDS) for further processing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Deep learning enhanced super-resolution x-ray fluorescence microscopy by a dual-branch network

X-ray fluorescence (XRF) microscopy is a powerful technique for quantifying the distribution of elements in complex materials, which makes it a crucial imaging technique across a wide range of disciplines in physical and biological sciences, including chemistry, materials science, microbiology, and geosciences. However, as a scanning microscopy technique, the spatial resolution of XRF imaging is inherently constrained by the x-ray probe profile and scanning step size. Here we propose a dual-branch machine learning (ML) model, which can extract scale-variant features and bypass abundant low-frequency information separately, to enhance the spatial resolution of the XRF images by mitigating the effects of blurring from the probe profile. The model is trained by simulated natural images, and a two-stage training strategy is used to overcome the domain gap between the natural images and experimental data. The tomography reconstruction from enhanced XRF projections shows an improvement in resolution by a scale factor of four and reveals distinct internal features invisible in low-resolution XRF within a battery sample. This study offers a promising approach for obtaining high-resolution XRF imaging from its low-resolution version, paving the way for future investigations in a broader range of disciplines and materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Development of a Technology Roadmap for Online Monitoring of Nuclear Power Plants

The nuclear power industry has recently recognized the potential cost saving that can be achieved from migrating the current manual and labor dependent surveillance and preventive maintenance activities to data-driven online monitoring methods. Since then, several efforts have been launched with various degrees of momentum to tackle specific surveillance or preventive maintenance activities. The light water reactor sustainability program (LWRS) program has anticipated this need and launched an effort that aimed to develop a technology roadmap for the nuclear power industry. Because the roadmap has to be focused towards the user or application’s value, and because nuclear power plants have different process efficiencies and deficiencies, the technology roadmap has to be plant independent. Achieving this objective required optimizing the extent of details described by a technology roadmap. The approach followed is for the roadmap to describe the sequence of activities needed to automate a process or equipment activity, but not describe the sequence of processes or equipment to target. For example, the steps required to migrate the inspection of a pump are plant independent. However, defining that the migration of feed water pumps inspections to online monitoring should be performed before inspections of feed water valves is dependent on multiple plant factors such as the plant requirements, labor work capacity, and process or equipment conditions. The technology roadmap was broken down into six elements, four of which have a sequential path of advancement, while the other two are supporting elements to the sequential elements. The sequential part of the roadmap analyzes data collection, analytics, visualization, and management. For these elements of the technology roadmap, the migration of an activity is defined in three states. The base state is defined as the most primitive, manual, and labor dependent process used by some nuclear power plants. The modern state is defined as the state that can be achieved if the activities are augmented or replaced by current technologies that are commercially available or are soon to be available. The state of the art is defined as using technologies of the future, i.e. concept and technologies that are being researched. The data collection part of the technology roadmap defines three states of every process performed in a nuclear power plant. However, the data analytics part is described based on equipment, because automating an activity performed on one equipment required considering multiple data sources. The visualization and data management elements are process or equipment independent. Visualization describes the means to use human factor science and technologies advancement in presenting the data collected and the data analytics results. The data management targets storage, communication, and computational power need, in addition to data integration and sharing. Since storage, communication, and computational power technologies and path for deployment are known to any information technology organization, this effort targeted the cost of data management. This has a direct impact on the feasibility of the automation effort. The supporting elements analyzes the value of automating the activity and the change challenges to tackle. For the support elements, the roadmap targeted describing activities that need to be performed as part of the roadmap development. The value element of the roadmap describes how to analyze the cost saving and the associated risk. The change management identifies multiple factors to consider such as regulation, cybersecurity, resource availability, supporting infrastructure availability, feasibility (power, fitness, environment tolerance, facilities), and cultural change.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2025

High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. Furthermore, we suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

Batteries↗
DOE OSTI2023

Machine learning assisted derivation of minimal low-energy models for metallic magnets

Abstract We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.

Sharma, Vikram (ORCID:0000000189105519)↗
DOE OSTI2024

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗