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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Advances in statistical methods for cancer surveillance research: an age-period-cohort perspective

Background: Analysis of Lexis diagrams (population-based cancer incidence and mortality rates indexed by age group and calendar period) requires specialized statistical methods. However, existing methods have limitations that can now be overcome using new approaches. Methods: We assembled a “toolbox” of novel methods to identify trends and patterns by age group, calendar period, and birth cohort. We evaluated operating characteristics across 152 cancer incidence Lexis diagrams compiled from United States (US) Surveillance, Epidemiology and End Results Program data for 21 leading cancers in men and women in four race and ethnicity groups (the “cancer incidence panel”). Results: Nonparametric singular values adaptive kernel filtration (SIFT) decreased the estimated root mean squared error by 90% across the cancer incidence panel. A novel method for semi-parametric age-period-cohort analysis (SAGE) provided optimally smoothed estimates of age-period-cohort (APC) estimable functions and stabilized estimates of lack-of-fit (LOF). SAGE identified statistically significant birth cohort effects across the entire cancer panel; LOF had little impact. As illustrated for colon cancer, newly developed methods for comparative age-period-cohort analysis can elucidate cancer heterogeneity that would otherwise be difficult or impossible to discern using standard methods. Conclusions: Cancer surveillance researchers can now identify fine-scale temporal signals with unprecedented accuracy and elucidate cancer heterogeneity with unprecedented specificity. Birth cohort effects are ubiquitous modulators of cancer incidence in the US. The novel methods described here can advance cancer surveillance research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Lattice $ \mathcal{N} $ = 4 super Yang-Mills at strong coupling

In this paper we present results from numerical simulations of N = 4 super Yang-Mills for two color gauge theory over a wide range of ’t Hooft coupling 0 < λ ≤ 30 using a supersymmetric lattice action. Numerical study of this lattice theory has been stymied until recently by both sign problems and the occurrence of lattice artifact phases at strong coupling. We have recently developed a new action that appears capable of solving both problems. The resulting action possesses just SU(2) rather than U(2) gauge symmetry. By explicit computations of the fermion Pfaffian we present evidence that the theory possesses no sign problem and exists in a single phase out to arbitrarily strong coupling. Furthermore, preliminary work shows that the logarithm of the supersymmetric Wilson loop varies as the square root of the ’t Hooft coupling λ for large λ in agreement with holographic predictions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Secure boot, trusted boot and remote attestation for ARM TrustZone-based IoT Nodes

With the extensive application of IoT techniques, IoT devices have become ubiquitous in daily lives. Meanwhile, attacks against IoT devices have emerged to compromise IoT devices by tampering with system pre-installed programs or injecting new malware. To mitigate these attacks, integrity enforcement of IoT systems has been proposed. The integrity of an IoT device system includes load-time integrity and runtime integrity. In this paper, we design an IoT system based on ARM TrustZone to enforce the system integrity. First, we establish the root of trust and propose a hybrid booting approach consisting of both secure boot and trusted boot to enforce the system load-time integrity. Second, we investigate a paging-based process integrity measurement method to measure the NW processes and conduct remote attestation based on the measurement results ensuring the NW runtime process integrity. We implement an IoT prototype system on a NXP i.MX6Q SABRE SD development board to assess its feasibility. Finally, real-world experiment results demonstrate that our prototype introduces negligible performance overhead to the original system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Dynamics of human protein kinase Aurora A linked to drug selectivity

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinase Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Aurora A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Variation in Flooding Tolerance in Populus deltoides ‘D-124’ and P. trichocarpa x P. deltoides Hybrid ‘52–225’

Flooding poses a substantial challenge to plant survival and productivity, particularly in riparian genus like Populus. This study examines the physiological, morphological, metabolic, and molecular responses of Populus deltoides ‘D-124’ and P. trichocarpa x P. deltoides hybrid clone ‘52–225’ under control and inundated conditions to identify differences in flooding tolerance. Under flooding conditions, physiological and cellular stress was more pronounced in P. deltoides ‘D-124’ than in the hybrid clone ‘52–225,’ as evidenced by lower transpiration (E), photosynthesis (A), and chlorophyll content. In contrast, ‘52–225’ showed reduced ROS accumulation suggesting better cellular function under stress. Morphologically, ‘52–225’ produced more shoot-born roots, which likely enhance oxygen transport and metabolic activity during flooding. Metabolite profiling revealed both overlapping and distinct patterns of sugar and amino acid accumulation between genotypes. Gene expression analysis revealed that flooding-responsive genes, including ALCOHOL DEHYDROGENASE 1 and HYPOXIA RESPONSIVE ERF 2, were activated in both genotypes, with a more pronounced response noted in ‘52–225.’ These findings extend our understanding of flooding tolerance mechanisms in Populus by connecting physiological traits, stress responses, and genetic regulation. This research contributes to the development of more flooding-resilient poplar varieties, with potential applications in breeding and restoration programs for flooding-prone environments.

Flooding stress↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2019

Halogen Bond Structure and Dynamics from Molecular Simulations

Halogen bonding has emerged as an important noncovalent interaction in a myriad of applications, including drug design, supramolecular assembly, and catalysis. Current understanding of the halogen bond is informed by electronic structure calculations on isolated molecules and/or crystal structures that are not readily trans- ferable to liquids and disordered phases. To address this issue, we present a first-principles simulation-based approach for quantifying halogen bonds in molecular systems rooted in an understanding of nuclei-nuclei and electron-nuclei spatial correlations. We then demonstrate how this approach can be used to quantify the structure and dynamics of halogen bonds in condensed phases, using solid and liquid molecular chlorine as prototypical examples with high concentrations of halogen bonds. We close with a discussion of how the knowledge generated by our first-principles approach may inform the development of classical empirical models, with a consistent representation of halogen bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Smartphone application for visualizing building air leakage

Annually, unwanted air leakage through building envelopes accounts for 4 quads of energy consumption in the United States, which translates to about 10% of total building energy consumption. Locating and sealing leakage sites is crucial fordecreasing building energy consumption. Smartphones are ubiquitous and contain sophisticated cameras and highperformance processors that could be employed to visualize air leakage using the background-oriented schlieren imaging technique, making leak detection cheaper and easier. This technique requires a textured and high-contrast background such as a brick or concrete masonry unit wall, a building air leak that has a temperature difference compared with the ambient air, and an imaging system. This work focuses on using smartphones as the imaging system to visualize air leakages. The paper discusses application development and the results of testing to determine leakage visualization performance as a function of leak temperature. The results show that leaks with a temperature difference greater than 16°C compared with the ambient air temperature were visualized using existing smartphones.

Boudreaux, Philip [ORNL] (ORCID:0000000229564665)↗
DOE OSTI2019

Belowground Biomass Response to Nutrient Enrichment Depends on Light Limitation Across Globally Distributed Grasslands

Anthropogenic activities are increasing nutrient inputs to ecosystems worldwide, with consequences for global carbon and nutrient cycles. Recent meta analyses show that above ground primary production is often co-limited by multiple nutrients; however, little is known abouthowroot production responds to changes in nutrient availability. At twenty-nine grassland sites on four continents, we quantified shallow root biomass responses to nitrogen (N), phosphorus (P) and potassium plus micronutrient enrichment and compared below- and above ground responses. We hypothesized that optimal allocation theory would predict context dependence in root biomass responses to nutrient enrichment, given variation among sites in the resources limiting to plant growth (specifically light versus nutrients). Consistent with the predictions of optimal allocation theory, the proportion of total biomass below ground declined with N or P addition, due to increased biomass above ground (for N and P) and decreased biomass below ground (N, particularly in sites with low canopy light penetration). Absolute root biomass increased with N addition where light was abundant at the soil surface, but declined in sites where the grassland canopy intercepted a large proportion of incoming light. These results demonstrate that below ground responses to changes in resource supply can differ strongly from above ground responses, which could significantly modify predictions of future rates of nutrient cycling and carbon sequestration. Our results also highlight how optimal allocation theory developed for individual plants may help predict below ground biomass responses to nutrient enrichment at the ecosystem scale across wide climatic and environmental gradients.

Cleland, Elsa↗
DOE OSTI2020

Unprecedented generation of 3D heterostructures by mechanochemical disassembly and re-ordering of incommensurate metal chalcogenides

Three-dimensional heterostructures are usually created either by assembling two-dimensional building blocks into hierarchical architectures or using stepwise chemical processes that sequentially deposit individual monolayers. Both approaches suffer from a number of issues, including lack of suitable precursors, limited reproducibility, and poor scalability of the preparation protocols. Therefore, development of alternative methods that enable preparation of heterostructured materials is desired. We create heterostructures with incommensurate arrangements of well-defined building blocks using a synthetic approach that comprises mechanical disassembly and simultaneous reordering of layered transition-metal dichalcogenides, MX 2 , and non-layered monochalcogenides, REX, where M = Ta, Nb, RE = Sm, La, and X = S, Se. We show that the discovered solid-state processes are rooted in stochastic mechanochemical transformations directed by electronic interaction between chemically and structurally dissimilar solids toward atomic-scale ordering, and offer an alternative to conventional heterostructuring. Details of composition–structure–properties relationships in the studied materials are also highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

g-factor engineering with InAsSb alloys toward zero band gap limit

Band gap is known as an effective parameter for tuning the Landé g factor in semiconductors and can be manipulated in a wide range through the bowing effect in ternary alloys. In this work, using the recently developed virtual substrate technique, high-quality InAsSb alloys throughout the whole Sb composition range are fabricated and a large g factor of g≈-90 at the minimum band gap of ~0.1 eV, which is almost twice that in bulk InSb, is found. Further analysis to the zero gap limit reveals a possible gigantic g factor of g≈-200 with a peculiar relativistic Zeeman effect that disperses as the square root of magnetic field. Such a g-factor enhancement toward the narrow gap limit cannot be quantitatively described by the conventional Roth formula, as the orbital interaction effect between the nearly triply degenerated bands becomes the dominant source for the Zeeman splitting. These results may provide insights into realizing large g factors and spin-polarized states in semiconductors and topological materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Voltron Compatible Whole Building Root-Fault Detection and Diagnosis

Buildings consume more than 40% of primary energy in the U.S. Malfunctioning sensors, components, and control systems, as well as degrading components in Heating, Ventilating and Air-conditioning (HVAC) and lighting systems are main reasons for energy waste and unsatisfactory indoor environment. Field studies have shown that an energy saving of 5-30% of total building energy consumption and improved indoor air quality can be achieved by simply applying automated fault detection and diagnosis (AFDD), followed by corrections even if these are not done in real time. Extensive research has been made on the development of component level AFDD tools in the past two decades. However, in recent years, the development of method for a whole building level AFDD has received much attention. Here, a whole building fault refers to a fault happening in one subsystem but impact on more than one subsystems, or has a significant impact on building performances, such as building’s energy consumption. Existing component level AFDD tools could fail to detect or give false alarm for faults that trigger abnormalities in multiple subsystems. Isolating such whole building faults, which have impacts on multiple subsystems, is also challenging by simply using component based AFDD solutions. On the other hand, it might be more cost-effective for medium- or small-sized commercial buildings that have subsystems (e.g. primary cooling subsystem, air loop subsystem, heating subsystem, lighting subsystem, etc.) to install one whole building level AFDD tool, rather than many component level AFDD tools, to focus on significant faults. The focus of this project is firstly to provide training opportunities in building AFDD research area for undergraduate students and to develop a workforce pipeline in building sciences. The objective of the proposed research activities is to develop cost-effective and scalable AFDD solutions for whole building level faults. The developed AFDD solution is VOLTTRONTM compatible to further increase its plug-n-play capacity and the market penetration. VOLTTRONTM is an open-source innovative distributed control and sensing software platform developed by the Pacific Northwest National Laboratory. A VOLTTRONTM compatible AFDD tool brings the benefits such as low-cost deployment, and better building to grid integration, etc. The performance of the developed whole building AFDD tools is evaluated using data collected from a mixed-use medium-sized university campus building. Another significant goal of this project is to engage undergraduate students in research activities and expose them to building control and AFDD fields. A demonstration building – Nesbitt Hall at Drexel University is identified for this study. This demonstration building has a typical HVAC system configuration, which includes a water-cooled chiller system, three variable air volume (VAV) air handling unit (AHU) systems, and a hydronic heating system. Faults, that are expected to have a whole building level impact, are artificially implemented in this demonstration building for three different seasons. Building data have been collected in this project, which include fault free building automation system (BAS) data and BAS data that contain system behaviors caused by artificially implemented and naturally occurred faults. These collected data are used to evaluate the developed whole building AFDD tools. A data-driven method, which includes a weather and schedule-based pattern matching (WPM) method and Feature based Principal Component Analysis (FPCA) method, is developed for whole building level fault detection. Parameter sensitivity tests, including snapshot window size, data sample searching pool size, etc., are implemented to evaluate the impacts of various WPM-FPCA parameters. A data-driven and expert knowledge/rule-based method using Bayesian Network (BN) and WPM is developed for whole building level fault diagnosis. The developed WPM-BN method, which includes a two-layer BN structure model and BN parameters, are either learned from baseline data or developed from expert knowledge. The developed whole building AFDD tools are evaluated using BAS data collected from the demonstration building. Using the evaluation data set, the WPM-FPCA fault detection method is evaluated under two different Principle Component (PC) retention rates, i.e., 0.65 retention rate and 0.95 retention rate. It is observed that when using 0.65 PC retention rate, the WPM-FPCA fault detection method achieves a 79% fault detection rate (this rate reaches to 85% when including the six make-up fault test cases discussed in the next bullet), while when using 0.95 PC retention rate, the WPM-FPCA fault detection method achieves 100% fault detection rate (this rate remains to be 100% when including the six make-up fault test cases). Under both PC retention rates, the WPM-FPCA fault detection method demonstrates 0% false alarm rates. Using the evaluation data set, the developed WPM-BN fault diagnosis method has successfully diagnosed thirteen (13) faults out of the fourteen (14) fault test cases. The one mis-diagnosed case exhibits fewer fault symptoms in different subsystems, when compared with other fault test cases. Overall speaking, the developed whole building AFDD solutions demonstrate satisfactory fault detection/diagnosis accuracy with very low false alarm rate. Lastly, a survey and market study has been performed to understand the existing AFDD market, including its gaps and needs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

EcoFAB 3.0: a sterile system for studying sorghum that replicates previous field and greenhouse observations

Introduction Studying plant-microbe interactions is one of the key elements in understanding the path to sustainable agricultural practices. These interactions play a crucial role in ensuring survival of healthy plants, soil and microbial communities. Many platforms have been developed over the years to isolate these highly complex interactions however, these are designed for small model plants. This creates a need for complementary devices for larger plants, such as sorghum. Methods This work introduces a novel platform, EcoFAB 3.0, which is designed to enable studying bioenergy plants such as sorghum for up to 4 weeks in a controlled sterile environment. Several other advantages of this platform such as dark root chambers and user-friendly assembly are also discussed in this work. Results and discussion EcoFAB 3.0 was found to replicate previous greenhouse and field observations when comparing an engineered sorghum line overproducing 4-hydroxybenzoic acid (4-HBA) and wildtype (variety BTx430). Consistent with greenhouse and field observations, it was found that the engineered line of sorghum grown in EcoFAB 3.0 had a higher 4-HBA content and a lower dry biomass.

Gupta, Kshitiz↗
DOE OSTI2016

STEADY STATE MODELING OF THE MINIMUM CRITICAL CORE OF THE TRANSIENT REACTOR TEST FACILITY

With the advent of next generation reactor systems and new fuel designs, the U.S. Department of Energy (DOE) has identified the need for the resumption of transient testing of nuclear fuels. The DOE has decided that the Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) is best suited for future testing. TREAT is a thermal neutron spectrum, air-cooled, nuclear test facility that is designed to test nuclear fuels in transient scenarios. These specific scenarios range from simple temperature transients to full fuel melt accidents. DOE has expressed a desire to develop a simulation capability that will accurately model the experiments before they are irradiated at the facility. It is the aim for this capability to have an emphasis on effective and safe operation while minimizing experimental time and cost. The multi physics platform MOOSE has been selected as the framework for this project. The goals for this work are to identify the fundamental neutronics properties of TREAT and to develop an accurate steady state model for future multiphysics transient simulations. In order to minimize computational cost, the effect of spatial homogenization and angular discretization are investigated. It was found that significant anisotropy is present in TREAT assemblies and to capture this effect, explicit modeling of cooling channels and inter-element gaps is necessary. For this modeling scheme, single element calculations at 293 K gave power distributions with a root mean square difference of 0.076% from those of reference SERPENT calculations. The minimum critical core configuration with identical gap and channel treatment at 293 K resulted in a root mean square, total core, radial power distribution 2.423% different than those of reference SERPENT solutions.

Critical↗
DOE OSTI2016

Assessment of the Analysis Capability for Core-wide PWR Pellet-Clad Interaction (PCI) Screening

The Level 1 Milestone, L1:CASL.P13.03, “Assess the analysis capability for core-wide PWR Pellet-Clad Interaction (PCI) screening and demonstrate detailed 3-D analysis on selected sub-region.”, has been completed by the Fuels, Materials and Chemistry and Physics Integration focus areas of CASL. This report illustrates the application of VERA simulation to PWR core-wide PCI screening, with emphasis on the multi-dimensional analysis capabilities of BISON fuel performance code. The VERA codes utilized in this effort are: the fuel performance code BISON and the VERA-CS sub-element, which combines the neutron transport code MPACT and the thermal-hydraulics code COBRA-TF (CTF). The target reactor for this work is Watts Bar Unit 1, Cycles 6 and 7, selected on the basis that PCI failures had occurred during power start-up in both cycles, despite the moderate power levels, which offered an interesting test of the code’s veracity and a particularly challenging condition for this Milestone’s assessment. The analysis began by performing BISON-IN-VERA, i.e. VERA-CS + BISON, quarter-core depletion calculations, from which limiting assemblies and limiting fuel rods were selected using a four-parameter selection criterion: high power, high temperature, small gap thickness and high cladding hoop stress. The cladding material being Westinghouse Zirlo, the analysis utilized a new material model developed for this cladding by CASL’s FMC team using open literature sources. The VERA-CS + BISON analysis resulted in identifying 51 limiting rods as candidates for more detailed PCI analysis using stand-alone BISON, employing local 2D and 3D geometric models. This local-effects BISON analysis provided detailed PCI failure assessment, considering the impact of power histories on the two potential sources of PCI failure: the classical SCC-PCI and the missing pellet surface geometry MPS-PCI. The 51-rod analyses of Watts Bar Unit 1 cycle 6 and 7 revealed that the classical SCC-PCI stress levels were well below the failure threshold determined in a previous CASL study, which implicated the other mode of failure, namely MPS-PCI. Considering MPS defects to be the root cause for the failure, the local-effects calculations was repeated using MPS defect sizes equal to and larger than Westinghouse quality assurance standards. Initial results of this new analysis showed a significant increase in the cladding stress, indicating that an MPS may have been present in cycle 6 and 7 for the failure to occur. In order to determine the size of MPS needed for cycle 6 and 7 rods to fail, a sensitivity study using 2-D and 3-D models was performed. The results showed cladding hoop stresses, while remaining below the failure level for an MPS of equal size to the acceptance standard, continued to increase as the size of the MPS increased. This report has demonstrated the use of BISON, in consort with VERA’s simulation methodology, for core-wide PCI failure screening capability. Utilizing this demonstration, and emulating the PCI margin analysis methodology developed in the EPRI PCI guidelines, BISON-IN-VERA simulation can be used very effectively to develop core power histories to minimize PCI failure potential.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment

Cataloging and classifying trees in the urban environment is a crucial step in urban and environmental planning; however, manual collection and maintenance of this data is expensive and time-consuming. Although algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, they generally struggle in the more varied urban environment. This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach. Our results highlight the importance of fusing spectral and structural information for remote sensing tasks in complex urban environments.

54 ENVIRONMENTAL SCIENCES↗