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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Development of Laboratory Testbeds Simulating Real-World Greenhouse Gas Emissions

Climate change leads to drastic variations in the environment that can threaten food security. To protect the security and prosperity of the environment, new agricultural practices that mitigate greenhouse gas emissions need to be developed. Agriculture plays a large role in the emissions of greenhouse gases, thus a need for new practices. At present, testing landscape management practices for carbon sequestration and reduction of greenhouse gas emissions are expensive because such tests often require intricate field experiments. Laboratory tests on landscape management practices often fail to simulate and accurately represent the natural systems researchers intend to study. There are methods that can be used to make these laboratory tests more closely replicate natural systems for the improvement of agricultural lands used in biofuel and food production. The goal of this project is to develop a laboratory testbed replicating field conditions that will mimic field greenhouse gas emissions. To achieve this goal, we used soil cores taken from agricultural and marginal lands, then attached fiberglass wicks to improve the drainage system in the soil core to simulate a more natural hydraulic system. The soil cores with the wicks were used to compared to greenhouse gas emissions observed in the field. Our preliminary results suggest that the soil carbon dioxide and nitrous oxide flux from the agricultural land site were comparable to the soil cores equipped with the drainage wicks. The data suggests that laboratory testbeds can be used to mimic field greenhouse gas emissions if parameters, such as soil water content were in a set range. The laboratory testbed produces similar greenhouse gas emissions to the agricultural site when the soil water content was between 15-25%.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using Eye Tracking to Elucidate the Mechanisms Underlying Stimulation-Enhanced Visual Target Detection

Transcranial direct current stimulation (tDCS) is a noninvasive form of brain stimulation that involves passing a weak electrical current between electrodes on the scalp to modulate underlying neural tissue. TDCS has been shown to modulate cognition in a variety of domains, including memory, attention, and visual processing. Prior work from our laboratory has shown positive effects of tDCS on learning to detect target objects hidden in complex naturalistic visual scenes and learn rules for categorizing images, though the mechanism for these benefits remains unknown. One possibility is that tDCS optimizes visual search by modulating visual attention or via the reduction in search errors. One method of quantifying visual attention is to use eye tracking to record search patterns to determine if and how visual search is adjusted under verum stimulation conditions. Eye tracking data allows classification of errors into error types, including sampling errors (failing to look in the relevant region), recognition errors (looking at the critical portion of a scene, but failing to recognize it as such as evidenced by visual fixation), and decision-making errors (fixating on the relevant portion of a scene, but making the wrong determination). Our results indicate that the benefit tDCS confers on visual search for targets stems from the reduction in decision-making errors when targets are present (Cohen’s d = 0.86). Also reported is a replication of previous findings showing a tDCS-dependent improvement in learning this task, learning score (Cohen’s d = 0.88); d’ (Cohen’s d = 1.00). This provides support for moving tDCS into the application space by pairing it with analysts who are concerned with the type of search error that is corrected via stimulation.

attention↗
DOE OSTI2019

Investigating YAP’s role in Human Mammary Epithelial Cell (HMEC) Immortalization and Cancer Stem Cell (CSC) Phenotype Acquisition during Early Breast Cancer Progression

The malignant aspect of tumorigenesis in breast cancer is initiated when human mammary epithelial cells (HMEC) first immortalize (gain the ability to proliferate indefinitely) and subsequently undergo oncogenic activation. Our laboratory has developed a two-step HMEC immortalization process that we believe accurately models early breast cancer progression. Normal finite pre-stasis HMEC must overcome two distinct senescence barriers to achieve full immortality: stasis (stress-associated senescence) and replicative senescence, respectively. HMEC bypass stasis, and become post-stasis, when errors in the retinoblastoma pathway are present. Replicative senescence, a product of continuously shortening telomeres producing telomere dysfunction, is overcome by the reactivation of telomerase. Our lab has called HMEC that acquire the error allowing telomerase expression conditionally immortal (CI); CI cells may still have low telomerase expression. Full immortality is achieved when sufficient telomerase expression exists to maintain short stable telomeres following a process we have called conversion. The transcriptional coactivator YAP, upregulated in several human cancers, is closely correlated with a variety of oncogenic effects including proliferation and induction of cancer stem cell (CSC) properties. Our preliminary data have shown that YAP-transduced post-stasis HMEC expressed properties of CI cells. Growth curves indicated that some cell lines achieved full immortality; however, these cells had acquired an additional error – amplified expression of c-Myc, an oncogene known to induce telomerase in post-stasis or CI cells. The absence of uniform immortalization in the YAP-transduced cells suggests that YAP causes post-stasis HMEC to become conditionally immortal. This is further supported given that YAP-transduced post-stasis HMEC have decreased levels of the lncRNA MORT and rapidly go through conversion when p53 function is abrogated, well-documented attributes of CI HMEC. Previous studies have also reported that YAP controls the differentiation of breast progenitor cells; as a result, it is likely that YAP plays a role in inducing CSC phenotypes. In this study, we attempt to gain a greater understanding of YAP’s role in HMEC immortalization and induction of CSC properties in early breast cancer progression. We measured gene expression levels and attempted to identify proteins indicative of a CSC phenotype in YAP-transduced post-stasis HMEC. Our initial results did not reveal an association between YAP and HMEC conditional immortality or induction of CSC attributes in the cultures examined. However, using a YAP construct that promotes nuclear localization (where YAP is most active) has led to a potentially promising YAP-derived cell line that we presume to be immortal. We go on to demonstrate that YAP may be acting via non-transcriptional oncogenic roles in our HMEC specimens. Thus, we recommend future studies into YAP and the HMEC immortalization process given that they are promising targets for breast cancer prevention therapies.

Shah, Manav↗
DOE OSTI2020

Machine Learning for Automated Extraction of Building Geometry

As data science comes to buildings, the promise of using machine learning and novel sources of data has received much attention. Advances in machine learning and computer vision algorithms, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics – cost-effectively, and at scale. Acquisition of features such as footprint are time consuming and costly to acquire with today’s manual methods, but can be streamlined through intelligent software-based solutions applied to satellite images. When combined with aerial RGB and thermal images, full 3D geometries and thermal maps can be constructed to determine additional characteristics such as window to wall ratio, height, number of stories and envelope thermal characteristics. In this paper we present three contributions to accelerate these high potential opportunities: (1) a methodical analysis of how these features can be integrated into today’s simulation and data driven software tools to enhance efficiency measure identification and owner/operator decision making; (2) development and accuracy testing of open source deep neural network methods to extract building footprints from satellite imagery, including the curation and application of openly available GIS datasets for training and continued development by others; and (3) an open framework for drone-based image capture and creation of 3D building geometries. This work represents an important bridge between high-level studies that span diverse application areas and those that detail point solutions yet cannot be easily replicated or extended.

Touzani, Samir↗
DOE OSTI2017

Multi-platform ’Omics Analysis of Human Ebola Virus Disease Pathogenesis

The pathogenesis of human Ebola virus disease (EVD) is complex. EVD is characterized by high levels of virus replication and dissemination, dysregulated immune responses, extensive virus- and host-mediated tissue damage, and disordered coagulation. To clarify how host responses contribute to EVD pathophysiology, we performed multi-platform ’omics analysis of peripheral blood mononuclear cells and plasma from EVD patients. Our results indicate that EVD molecular signatures overlap with those of sepsis, imply that pancreatic enzymes contribute to tissue damage in fatal EVD, and suggest that Ebola virus infection may induce aberrant neutrophils whose activity could explain hallmarks of fatal EVD. Moreover, integrated biomarker prediction identified putative biomarkers from different data platforms that differentiated survivors and fatalities early after infection. We report this work reveals insight into EVD pathogenesis, suggests an effective approach for biomarker identification, and provides an important community resource for further analysis of human EVD severity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

TeMPI Shim

This is the manual for the TeMPI Shim library, whose goal is to facilitate scientific apps being loosely coupled through MPI. TeMPI Shim can be used to create and manage Message Passing Interface (MPI, see [MPI]) Communicators for Single Program, Multiple Data (SPMD, see [SPMD]) and Multiple Programs, Multiple Data (MPMD, see [MPMD]) workflows. MPI provides the MPI_APPNUM command number to each of the processes within its context. This number, starting at 0, is the application number being executed. In a case where 3 applications are being run in MPMD mode, there would be MPI_APPNUM values of 0, 1, and 2. TeMPI Shim creates intra- and inter-communicators between each pair of MPI_APPNUM values. In the aforementioned case, application 0 would have intra- and inter-communicators to speak to itself, application 1, and application 2. This is replicated for each of the applications. Additionally, TeMPI Shim creates intra- and inter-communicators for the first MPI rank of each application to directly communicate only with each other. Finally, TeMPI Shim creates its own copy of the default world communicator, i.e., MPI_COMM_WORLD. In the case where there is only a single application, it will have the communicators to only speak with itself. TeMPI Shim is useful in this case since it is considered good practice for MPI applications to copy the default world communicator and reference this copy (see [Duplicate World]_) anyways. Ultimately, it provides value independent of the number of applications present.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2017

SJL bone marrow-derived macrophages do not have IRF3 mutations and are highly susceptible to Theiler's virus infection

It is well known that SJL mice are susceptible to Theiler's murine encephalomyelitis virus (TMEV)-induced demyelinating disease while C57BL6 (B6) and B10 mice are resistant, and H-2{sup s} on a B10 background (B10.S) contributes modestly to susceptibility. A recent study linked two IRF3 non-conservative mutations in SJL compared to B10.S mice to resistance to TMEV infection of SJL peritoneal-derived macrophages, an observation of practical interest in light of the central role of IRF3 transcription factor in the type I interferon (IFN) response. However, we did not find these non-conservative mutations among SJL, B10.S, B6 and B10 mice in the IRF3 amino acid sequence, and show SJL bone marrow-derived macrophages infected with TMEV exhibit increased virus RNA replication and infectious virus yields as well as greater IL-6 production than C57Bl strain (including B10.S) cultures. - Highlights: • We confirmed that Theiler's murine encephalomyelitis virus (TMEV)-infection in bone marrow-derived macrophages is significantly greater in susceptible SJL macrophages than in resistant C57BL macrophages. • We failed to confirm two non-conservative amino acid changes in IRF3 identified previously in SJL mice or differences in IRF3 activation after infection between the two mouse strain. However, IL-6 levels were significantly greater in infected SJL macrophages late in the infectious cycle. • The data also suggest that addition of the cytokine IL-6 to infected macrophage cultures is modestly antiviral.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Characterization of NORM in an Urban Environment using HPGe Measurements and MCNP6 Simulations

This work is part of a larger project aimed at producing an operationally relevant, validated simulation environment that can be used as a 'virtual testbed' for counterterrorism and counterproliferation efforts. Examples of such efforts include searches for illicit radiation sources before and during large special events, such as the presidential nominating conventions, inaugurations, and large sporting events. Once validated by laboratory and field measurements, this testbed can be used to test and validate algorithms and detector systems designed for various search missions in an urban environment. The detection of radioactive sources in a city is greatly complicated by natural background radiation, which emanates from various materials common to an urban environment, such as roadways, sidewalks, and building exteriors. The methods and results offered here represent the conclusion of an effort to characterize the concentration of naturally occurring radioactive material (NORM) in various elements in a controlled, but representative, urban environment. Some of the methods discussed in previous work will be briefly reiterated here, but more detail can be found in the preceding work. The location chosen for this effort was a Military Operations on Urban Terrain (MOUT) environment at the Fort Indiantown Gap (FTIG) National Guard training facility in Pennsylvania. This site was chosen because it replicates a small town where traffic and pedestrian access could be easily controlled, as to not interfere with the background measurements. This endeavor involved over 70 measurements using a high-purity germanium (HPGe) detector designed to ascertain the NORM concentrations in the soil, asphalt roads, gravel lots, concrete intersections, sidewalks, foundations, and walls found throughout the site. The NORM concentrations determined from this work will be incorporated into a large-scale simulation that can be used to investigate various search techniques and algorithms, detector types and setups, and to produce representative synthetic data for future work.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2018

Diphenyleneiodonium enhances oxidative stress and inhibits Japanese encephalitis virus induced autophagy and ER stress pathways

Highlights: 1.Diphenyleneiodonium (DPI) enhances ROS levels in Japanese encephalitis virus (JEV) infection. 2.DPI blocks autophagy and activation of ER stress pathways in JEV infected neuronal cells. 3.DPI inhibits JEV particle release. 4.During JEV infection autophagy and ER stress are likely to be modulated by DPI sensitive enzymes. Diphenyleneiodonium (DPI) and N-acetyl-l-cysteine (NAC), two widely used anti-oxidants, were employed to evaluate the role of oxidative stress in Japanese encephalitis virus (JEV) induced autophagy, stress responses and replication. DPI and NAC exerted opposite effects on ROS levels in JEV infected mouse neuronal cells (Neuro2a), mouse embryonic fibroblasts (MEFs) and human epithelial cells (HeLa). While NAC effectively quenched ROS, DPI enhanced ROS levels, suggesting that DPI induces oxidative stress in JEV infected cells. DPI treatment of JEV infected Neuro2a cells further blocked autophagy induction and activation of all three arms of the ER stress pathway, and, inhibited virus particle release. Autophagy induction in JEV infection has been previously shown to be linked to the activation of XBP1 and ATF6 ER stress sensors. Our data suggests that DPI mediated block of autophagy is a result of inhibition of ER stress responses and is not associated with an anti-oxidative effect. Since DPI has a wide inhibitory potential for all Flavin dependent enzymes, it is likely that the signalling pathways for ER stress and autophagy during JEV infection are modulated by DPI sensitive enzymes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Evaluation of accessible mineral surface areas for improved prediction of mineral reaction rates in porous media

The rates of mineral dissolution reactions in porous media are difficult to predict, in part because of a lack of understanding of mineral reactive surface area in natural porous media. Common estimates of mineral reactive surface area used in reactive transport models for porous media are typically ad hoc and often based on average grain size, increased to account for surface roughness or decreased by several orders of magnitude to account for reduced surface reactivity of field as opposed to laboratory samples. In this paper, accessible mineral surface areas are determined for a sample from the reservoir formation at the Nagaoka pilot CO 2 injection site (Japan) using a multi-scale image analysis based on synchrotron X-ray microCT, SEM QEMSCAN, XRD, SANS, and FIB-SEM. This analysis not only accounts for accessibility of mineral surfaces to macro-pores, but also accessibility through connected micro-pores in smectite, the most abundant clay mineral in this sample. While the imaging analysis reveals that most of the micro- and macro-pores are well connected, some pore regions are unconnected and thus inaccessible to fluid flow and diffusion. To evaluate whether mineral accessible surface area accurately reflects reactive surface area a flow-through core experiment is performed and modeled at the continuum scale. The core experiment is performed under conditions replicating the pilot site and the evolution of effluent solutes in the aqueous phase is tracked. Various reactive surface area models are evaluated for their ability to capture the observed effluent chemistry, beginning with parameter values determined as a best fit to a disaggregated sediment experiment (Beckingham et al., 2016) described previously. Simulations that assume that all mineral surfaces are accessible (as in the disaggregated sediment experiment) over-predict the observed mineral reaction rates, suggesting that a reduction of RSA by a factor of 10–20 is required to match the core flood experimental data. While the fit of the effluent chemistry (and inferred mineral dissolution rates) greatly improve when the pore-accessible mineral surface areas are used, it was also necessary to include highly reactive glass phases to match the experimental observations, in agreement with conclusions from the disaggregated sediment experiment. Finally, it is hypothesized here that the 10–20 reduction in reactive surface areas based on the limited pore accessibility of reactive phases in core flood experiment may be reasonable for poorly sorted and cemented sediments like those at the Nagaoka site, although this reflects pore rather than larger scale heterogeneity.

58 GEOSCIENCES↗
DOE OSTI2015

Charge transport model to predict intrinsic reliability for dielectric materials

Several lifetime models, mostly empirical in nature, are used to predict reliability for low-k dielectrics used in integrated circuits. There is a dispute over which model provides the most accurate prediction for device lifetime at operating conditions. As a result, there is a need to transition from the use of these largely empirical models to one built entirely on theory. Therefore, a charge transport model was developed to predict the device lifetime of low-k interconnect systems. The model is based on electron transport and donor-type defect formation. Breakdown occurs when a critical defect concentration accumulates, resulting in electron tunneling and the emptying of positively charged traps. The enhanced local electric field lowers the barrier for electron injection into the dielectric, causing a positive feedforward failure. The charge transport model is able to replicate experimental I-V and I-t curves, capturing the current decay at early stress times and the rapid current increase at failure. The model is based on field-driven and current-driven failure mechanisms and uses a minimal number of parameters. All the parameters have some theoretical basis or have been measured experimentally and are not directly used to fit the slope of the time-to-failure versus applied field curve. Despite this simplicity, the model is able to accurately predict device lifetime for three different sources of experimental data. The simulation's predictions at low fields and very long lifetimes show that the use of a single empirical model can lead to inaccuracies in device reliability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Base excision repair of the N -(2-deoxy- d - erythro -pentofuranosyl)-urea lesion by the hNEIL1 glycosylase

Abstract The N-(2-deoxy-d-erythro-pentofuranosyl)-urea DNA lesion forms following hydrolytic fragmentation of cis-5R,6S- and trans-5R,6R-dihydroxy-5,6-dihydrothymidine (thymine glycol, Tg) or from oxidation of 7,8-dihydro-8-oxo-deoxyguanosine (8-oxodG) and subsequent hydrolysis. It interconverts between α and β deoxyribose anomers. Synthetic oligodeoxynucleotides containing this adduct are efficiently incised by unedited (K242) and edited (R242) forms of the hNEIL1 glycosylase. The structure of a complex between the active site unedited mutant CΔ100 P2G hNEIL1 (K242) glycosylase and double-stranded (ds) DNA containing a urea lesion reveals a pre-cleavage intermediate, in which the Gly2 N-terminal amine forms a conjugate with the deoxyribose C1′ of the lesion, with the urea moiety remaining intact. This structure supports a proposed catalytic mechanism in which Glu3-mediated protonation of O4′ facilitates attack at deoxyribose C1′. The deoxyribose is in the ring-opened configuration with the O4′ oxygen protonated. The electron density of Lys242 suggests the ‘residue 242-in conformation’ associated with catalysis. This complex likely arises because the proton transfer steps involving Glu6 and Lys242 are hindered due to Glu6-mediated H-bonding with the Gly2 and the urea lesion. Consistent with crystallographic data, biochemical analyses show that the CΔ100 P2G hNEIL1 (K242) glycosylase exhibits a residual activity against urea-containing dsDNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Envision Charlotte Project Final Report

The Envision Charlotte Project (EC Project), supported by partners at the Center for Climate and Energy Solutions, the University of North Carolina at Charlotte’s Center for Sustainably Integrated Buildings and Sites and the City of Charlotte, has successfully demonstrated that existing buildings can benefit from operations improvements and low-cost efficiency upgrades, and that behavioral change campaigns can be effectively designed for measurability, longevity and impact. Support from this grant gave Envision Charlotte and its partners the resources and flexibility to demonstrate a variety of approaches to improve coordination with stakeholders in the implementation of programs to reduce the consumption of energy, water and waste. The project team also gained insights into the motivations and abilities that influence stakeholder engagement in sustainability initiatives at multiple levels. Importantly, as this report describes, strong partnerships between nonprofits, industry partners and research institutions can produce tangible impacts and replicable results in building efficiency. Further, building operators working alongside student researchers can form productive relationships that can advance building energy management goals. Key Findings of the EC Project This project focused on advancing solutions to energy efficiency in U.S. commercial buildings. The Building Re-Tuning™ Training (BRT) developed by the U.S. DOE’s Pacific Northwest National Lab (PNNL), designed to detect energy savings opportunities and implement improvements with resources and online tools, provided the starting point for the curriculum developed by stakeholders in the EC Project. Although the project partners encountered some issues with data consistency, the overall project outcomes fell short of the energy savings stated in the original proposal, and the total participating building targets were lower than anticipated, important findings, outcomes and lessons learned were achieved. EC Project grantees observed that the PNNL BRT, as currently written, was not compatible with the skills of average frontline mechanical personnel in the Charlotte region where the grant program was performed. Frontline staff are not, typically, energy managers, engineers or building commissioning agents, but they can play a key role in building performance. As detailed in this report, the grantees therefore focused on changes to the BRT that improved the level of training and competency of the frontline workers in this region. Among the technical observations related to improving the BRT that were observed in the EC Project: centralized facilities management often has an impact on building performance; operators can be overly reliant on service contracts; individual behaviors matter, and technology cannot be viewed as a crutch; current Building Automation Systems (BAS) are often too complex to support building efficiency optimization, but BAS coupled with appropriate training can support better outcomes; the “perfectly optimized” building is not a near-term possibility, but better education, especially of executives and managers, on the role of behavioral intervention is a promising direction the building science community can and should pursue; a lack of standards and protocols, and competition between proprietary software from service contracts, remain challenges for this industry. These technical observations, in turn, informed the EC Project’s overall findings, which were: Accomplishing building efficiency is a process of education for all involved in the building design, construction and operation supply chain. Building operators, as a professional class, have an opportunity for further training and education that will lead to better on-the-job performance. An updated Building Re-Tuning™ Training curriculum, based on the findings of this report, could be an avenue for these operators’ professional development.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Experiments and numerical modeling of surrogate droplet combustion for a 93 octane gasoline calibration fuel

Gasoline is the world’s most prolific liquid transportation fuel for ground transportation vehicles. Yet, a fundamental understanding of its burning characteristics is still lacking. Complicating this matter is the highly multicomponent nature of gasoline (comprised of hundreds of chemical species) and the seasonable variability of its composition due to variations in the crude oil feedstocks used in refining. The need to bring order to this complex problem has led to development of calibration fuels (Tier II in presented study) which on average will provide repeatable metrics that replicate performance of refined gasoline for various engine designs. This study presents results of experiments using the configuration of an isolated droplet to study the combustion dynamics of calibration fuel and three different surrogates (S4, S5, S6) for the calibration fuel. Droplet burning is under microgravity conditions, where gas transport is determined entirely by fuel evaporation in order to promote spherical symmetry in the burning process. The initial droplet diameters ranged between 0.55 mm and 0.60 mm. The droplets were spark-ignited and their burning histories were recorded by digital video cameras to obtain quantitative data. For each fuel, the evolutions of droplet, flame, and soot-shell diameters were measured to calculate the burn rates, flame-droplet diameter ratios (FSR) and soot-droplet diameter ratios. Additionally, a detailed numerical modeling is presented. This simulation includes multicomponent phase equilibrium, unsteady gas and liquid transport, radiative heat transfer, internal liquid diffusion and detailed combustion chemistry of different fuels during their burning process. Experiment results show that the calibration fuel and surrogates have a close burn rates and FSR, and S5 presents a relatively similar results with calibration fuel. Predictions from the numerical simulation using the surrogate are compared with experiment measurements, which showed a good agreement with the experiment results, including the droplet burning rate and FSR.

Guo, Songtao↗
DOE OSTI2016

NORM Concentration Determination in Common Materials in an Urban Environment

The United States government performs searches for illicit radiation sources before and during large special events, such as State of the Union addresses, presidential inaugurations, or the recent papal visit. Clear detection of sources is difficult due to natural background radiation, which can vary greatly throughout a given search area. Background radiation is mostly a function of the materials used in roadways, sidewalks, and building exteriors. A team of scientists from Oak Ridge National Laboratory (ORNL), Lawrence Livermore National Laboratory (LLNL), the Remote Sensing Laboratory (RSL), and Lawrence Berkeley National Laboratory (LBNL) is exploring the nature and variability of background count rates in order to improve the performance of searches in urban areas. A series of background measurements have been and will continue to be taken at a Military Operations on Urban Terrain (MOUT) environment at the Fort Indiantown Gap (FTIG) National Guard training facility in Pennsylvania. This site replicates a small town with asphalt roads, concrete intersections, buildings, sidewalks, curbs, and gravel parking areas. Unlike an actual urban area, traffic and pedestrian access can be easily controlled to avoid interference with measurements. This paper documents the efforts focused on characterizing the various concentrations of radioactive material found in these materials at the site. The NORM concentrations determined from this work will be incorporated in to a large-scale simulation (a 'virtual test-bed') that can be used to investigate various search techniques and algorithms, detector suite configurations, and to produce representative synthetic data for future work. A combination of measurement and simulation was used to determine the concentration of {sup 40}K and the radioactive nuclides in the uranium series and the thorium series in several materials at the FTIG MOUT site. The predicted concentrations are consistent with other published results, and they matched favorably with synthetic data generated with MCNP6 models using the determined NORM concentrations and estimates of the FTIG environment as described by available construction plans and using standard material assumptions. Future measurements will incorporate lessons learned from this study. More measurements will be made of the concrete intersections at the site. The methodology explained in this report will also be carried out on building materials in an effort to characterize NORM concentrations in these background sources. Other modeling and simulation work that should be conducted includes a robust sensitivity analysis in which the effects of small changes to the model (and measurement) system can be characterized to help bound the uncertainty on the final NORM concentration predictions. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Developing a Digital Twin for SRF Cavity Assembly at Fermilab

When assembling Superconducting Radio Frequency (SRF) Cavities, maintaining an environment devoid of particulates like dust and other small particles is essential. If a single spec of dust enters the cavity a significant degradation of performance can occur. To avoid a cavity failure Fermilab assembles the SRF cavities within a ISO-4 (Class 10) environment. This environment though is still susceptible to foreign contaminants when technicians enter and new components are added to the cleanroom. To reduce the risk even more Fermilab has introduced a cobot manipulator into the cleanroom environment to speed up the assembly time which will reduce the time that the technicians operate in the cleanroom. But, this still leaves the potential of contaminants to enter the cleanroom if new components need to be tested within the cleanroom. This project aims to lay the groundwork to develop a Digital Twin environment of the cleanroom to aid in manufacturing processes and testing. In its simplest form, a digital twin is a bidirectional link between a physical system and its digital counterpart or twin. NVIDIA Isaac Sim is used as the digital twin foundation for the digital representation of the cleanroom, specifically for the UR16e assembly area. A simulated UR16e was used to validate the performance of Isaac Sim as a testing environment by comparing the tool center points (TCP) positional data between the simulated and digital representation of the UR16e. Due to a new vision based robotic assembly process being introduced to the cleanroom a digital representation of the physical camera was tested and validated to ensure that it will produce close to the same outcome as the physical environment. The TCP comparison results showed a peak translational error of approximately 0.1mm and rotational errors of up to 8 between the simulated and digital UR16es. While the camera validation performed with high repeatability across multiple runs, it still requires minor tuning before it can accurately replicate a physical camera.

Imburgia, Joseph [Northern Illinois U.]↗