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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced Zonal Infiltration Measurement Method for Multifamily Buildings: A Novel Test Procedure to Determine Air Leakage Through External and Internal Surfaces

Measuring air infiltration through the external building envelope as well as between adjacent zones within a building is important since air leakage is related to increased energy use and poor indoor air quality (IAQ). State of the art methods can determine both leakages, however it can be both time consuming and labor intensive as it requires running both guarded and compartmentalization tests. This paper presents our work on developing a simplified testing procedure to determine power law flow equation leakage parameters of both external and internal boundaries of tested zones. The proposed Zonal Multipoint Pressure Test (ZMPT) method modulates airflow in the zone using a variable airflow measurement panel (mounted in either a window or the door), and solving the mass balance equations for the leakage parameters. ZMPT results were compared to conventional test results and found to correlate to within +/-7%.

Jayarathne, Tharanga↗
DOE OSTI2021

Development of Integrated Mechanical Pods

This presentation highlights early wins, updated progress, and upcoming developments on ‘national-scale shared development platform’ for rapid prototyping, testing and validation of various integrated Mechanical Pod solutions and form factors. Such pod solutions consist of a set of all-electric heat pump mechanical equipment that have integrated functionalities through built-in controls, with heating, cooling, hot water, ventilation (including energy recovery), electrical management, and battery storage within a single package. The presentation draws inspiration from the success of bathroom pods in the US modular construction industry, UK’s efforts with unitizing mechanical systems as ‘utility cupboards’, and VEIC’s early wins in design-build of all-electric Mechanical Pod solutions in Vermont. The presentation includes researchers and partners involved with NREL in Design for Manufacturing and Assembly (DfMA), Virtual Design and Construction (VDC), and digital twin based process optimization modeling of integrated Mechanical Pod solutions. The presentation aims to highlight early wins from such a platform and how various physical and virtual tools are currently being employed as part of NREL’s ongoing multi-year project funded by US DOE. Streamlined procurement, coordination, installation, and O&M of Mechanical Pods such that the majority of work is delegated to the off-site modular factory implies monetary savings. Such a seemingly basic shift in location of the construction process leads to great reduction in complexity, first cost, lead time, and waste, and greater opportunities for innovative compartmentalization and integration of mechanical systems appropriately sized for each apartment or hotel guest room. However, past studies on unitized combination systems show that high installation costs, maintenance issues, challenges with system integration, limitations in existing electrical infrastructure, and lack of architecturally appealing solutions are key barriers. NREL and partners aim to address key barriers through DfMA approach, rapid prototyping and testing, and digital twin process optimization modeling. The presentation is also a call for interested entities to partner with NREL as part of the national-scale development platform, help drive both product and process innovation, and encourage open source sharing of learnings. Learning objectives include (1) learn about the vision of national-scale shared development platform for process-product innovation on integrated mechanical pod solutions and how to get involved, (2) gain an understanding of the components of an all-electric, high performance home, design characteristics and equipment included in an all-electric mechanical pod, integration of mechanical systems within a modular factories’ assembly line, and the system’s commissioning, operation and maintenance. The pre-planning and coordination with the factory and sub-contractors are also highlighted, (3) gain an understanding of using process modeling tools to quantify resource-constrained performance of operations (such as integration of energy efficiency strategies) to manufacture modules of varying design, (4) gain insights on virtual design, rapid prototyping, and emulated testing of various form factors across different climatic conditions. The need for such preliminary testing with open source sharing of learnings will also be highlighted.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

DYFLOW: A flexible framework for orchestrating scientific workflows on supercomputers

Modern scientific workflows are increasing in complexity with growth in computation power, incorporation of non-traditional computation methods, and advances in technologies enabling data streaming to support on-the-fly computation. These workflows have unpredictable runtime behaviors, and a fixed, predetermined resource assignment on supercomputers can be inefficient for overall performance and throughput. Inability to change resource assignments further limits the scientists to avail of science-driven opportunities or respond to failures.We introduce DYFLOW, a flexible framework that orchestrates scientific workflows on supercomputers based on user-designed policies. DYFLOW compartmentalizes orchestration stages into simplified constructs, and end-users can program and reuse them according to their workflow requirements through an easy-to-use interface. These constructs hide the intricacies involved in runtime management from end-users, for instance, procurement of information to understand the workflow state, assessment, and supervision of the runtime changes. DYFLOW is designed to work alongside existing workflow management systems and reuse the available (static) support for workflow management. We have integrated DYFLOW with an existing workflow management tool as a demonstration. With experiments performed on use cases from three types of scientific workflows and two different parallel architectures, we show that DYFLOW achieves the desired orchestration incurring a small cost to carry out the runtime changes.

Singhal, Swati↗
DOE OSTI2022

System for an ionic liquid-based electrolyte for high energy battery

A system for electrical energy production from chemical reagents in a compartmentalized cell includes: at least two electrodes, comprising at least one anode and at least one cathode; at least one separator, that separates the anodes and the cathodes; and an ionic liquid electrolyte system. The system can be a battery or one or more cells of a battery system. The ionic liquid electrolyte system comprises an ionic liquid solvent; an ether co-solvent, comprising a minority fraction, by weight, of the electrolyte; and a lithium salt. In preferred variations, the anode is a lithium metal anode and the cathode is a metal oxide cathode and the separator is a polyolefin separator.

Wang, Richard Y.↗
DOE OSTI2023

System for an ionic liquid-based electrolyte for high energy battery

A system for electrical energy production from chemical reagents in a compartmentalized cell includes: at least two electrodes, comprising at least one anode and at least one cathode; at least one separator, that separates the anodes and the cathodes; and an ionic liquid electrolyte system. The system can be a battery or one or more cells of a battery system. The ionic liquid electrolyte system comprises an ionic liquid solvent; an ether co-solvent, comprising a minority fraction, by weight, of the electrolyte; and a lithium salt. In preferred variations, the anode is a lithium metal anode and the cathode is a metal oxide cathode and the separator is a polyolefin separator.

Wang, Richard Y.↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2026

Pixel-Registered Multimodal Synchrotron XRF and FTIR Microscopies Reveal Salinity Stress Response Mechanisms in Pistachio

Background: Salinity is a major abiotic stress that negatively affects nearly all plant species at all stages of growth. Drought and poor-quality irrigation cause high soil salinity and salt accumulation via evaporation, reducing crop productivity. Despite its critical importance, the spatial localization of salt ions and associated biochemical changes within plants experiencing high salinity remains largely unknown. In this study, we developed a multimodal imaging pipeline to understand the impact of salinity on the pistachio rootstock UCB-1 (Pistacia atlantica x Pistacia integerrima). We directly link biochemical fingerprints in stem tissue architecture with salt ion localization to provide insights into the strategies pistachio uses to tolerate salinity. Results: We observed that Pistacia spp. exposed to high salt conditions accumulated Ca, Si, Cl, Al and Mg as hotspots within the pith, compared to the control (of which only Ca and Al co-locate). In contrast, there was a decrease in K between the control and salinity treatment. Hotspots of amide I and II were present in the cortex and pith of the salinity treated sample. Additionally, the salinity treatment resulted in an increased abundance of pectin and carbohydrates within the pith compared to the control, and the abundance of esters/carboxylic acid was greater in the salinity treatment. Conclusions: We determined that Cl and K, S and P, and biochemical components polysaccharide and pectin, esters and carboxylic acid, amide I and cellulose are the strongest drivers of salinity- treatment induced variability. In the cortex and phloem/xylem, a negative K-Ca correlation decreases in the salinity treatment. Several hotspots of elements and amide I (proteins) appear under salinity treatment, particularly in the cortex, suggesting an increase in the production of stress-related proteins (in response to high Cl) and/or structural proteins (i.e. Ca). Together, these results indicate that pistachio responds to salinity through ion compartmentalization coupled with a targeted biochemical adjustment, rather than a broadscale tissue-wide response. Overall, these novel, spatially resolved pixel-registered multimodal imaging data provide an enabling platform to understand the mechanisms of salinity tolerance in Pistacia spp and can be broadly applied to studying stress-related phenotype response in various plant tissues.

FTIR spectromicroscopy↗
DOE OSTI2024

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Mathematical solutions in internal dose assessment: A comparison of Python-based differential equation solvers in biokinetic modeling

Abstract In biokinetic modeling systems employed for radiation protection, biological retention and excretion have been modeled as a series of discretized compartments representing the organs and tissues of the human body. Fractional retention and excretion in these organ and tissue systems have been mathematically governed by a series of coupled first-order ordinary differential equations (ODEs). The coupled ODE systems comprising the biokinetic models are usually stiff due to the severe difference between rapid and slow transfers between compartments. In this study, the capabilities of solving a complex coupled system of ODEs for biokinetic modeling were evaluated by comparing different Python programming language solvers and solving methods with the motivation of establishing a framework that enables multi-level analysis. The stability of the solvers was analyzed to select the best performers for solving the biokinetic problems. A Python-based linear algebraic method was also explored to examine how the numerical methods deviated from an analytical or semi-analytical method. Results demonstrated that customized implicit methods resulted in an enhanced stable solution for the inhaled 60 Co (Type M) and 131 I (Type F) exposure scenarios for the inhalation pathway of the International Commission on Radiological Protection (ICRP) Publication 130 Human Respiratory Tract Model (HRTM). The customized implementation of the Python-based implicit solvers resulted in approximately consistent solutions with the Python-based matrix exponential method ( expm ). The differences generally observed between the implicit solvers and expm are attributable to numerical precision and the order of numerical approximation of the numerical solvers. This study provides the first analysis of a list of Python ODE solvers and methods by comparing their usage for solving biokinetic models using the ICRP Publication 130 HRTM and provides a framework for the selection of the most appropriate ODE solvers and methods in Python language to implement for modeling the distribution of internal radioactivity.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

A Cristae-Like Microcompartment in Desulfobacterota

Some Alphaproteobacteria contain intracytoplasmic membranes (ICMs) and proteins homologous to those responsible for the mitochondrial cristae, an observation which has given rise to the hypothesis that the Alphaproteobacteria endosymbiont had already evolved cristae-like structures and functions. However, our knowledge of microbial fine structure is still limited, leaving open the possibility of structurally homologous ICMs outside the Alphaproteobacteria. Here, we report on the detailed characterization of lamellar cristae-like ICMs in environmental sulfate-reducing Desulfobacterota that form syntrophic partnerships with anaerobic methane-oxidizing (ANME) archaea. These structures are junction-bound to the cytoplasmic membrane and resemble the form seen in the lamellar cristae of opisthokont mitochondria. Extending these observations, we also characterized similar structures in Desulfovibrio carbinolicus, a close relative of the magnetotactic D. magneticus, which does not contain magnetosomes. Despite a remarkable structural similarity, the key proteins involved in cristae formation have not yet been identified in Desulfobacterota, suggesting that an analogous, but not a homologous, protein organization system developed during the evolution of some members of Desulfobacterota.

electron microscope tomography↗
DOE OSTI2020

Hawthorne Nevada Deep Direct-Use Feasibility Study - Data Used for Geothermal Resource Conceptual Modeling and Power Capacity Estimates

This data submission includes several data components that were used to develop a conceptual model and power capacity-estimates of two low-temperature geothermal resources that define geothermal prospect A at Hawthorne, Nevada. Data are sourced from a combination of legacy publicly-available data and more recent data acquisition conducted by the US Navy Geothermal Program Office (2008-2013) and the Great Basin Center for Geothermal Energy at the University of Nevada, Reno (2008-2010). Data sets include compiled fluid geochemistry data, down-hole temperature logs for wells in the vicinity of prospect A, 2 meter temperature survey data, temperature-spinner logs acquired in well HWAAD-2A, fracture picks from image log data acquired in wells HWAAD-2 and HWAAD-3, and X-Ray Diffraction (XRD) analyses on cuttings from wells HWAAD-2A and HWAAD-3. These data have been reviewed for errors and inconsistencies, but it is possible that few errors could still remain. The resource conceptual model and power capacity estimates are included in the final report to the US Department of Energy, and are presented in a manuscript by Ayling and Hinz. A link to the manuscript published in Geothermics is linked below in this submission.

15 GEOTHERMAL ENERGY↗
NASA NTRS1987

Proton gradients in intact cyanobacteria

The internal pH values of two unicellular cyanobacterial strains were determined with electron spin resonance probes, over an external pH range of 6 to 9, in the light and in the dark. The slow growing, thylakoid-lacking Gloeobacter violaceus was found to have a low capacity for maintaining a constant internal pH. The distribution pattern of weak acid and amine nitroxide spin probes across the cell membranes of this organism, in the light and in the dark, was consistent with the assumption that it contains a single intracellular compartment. At an external pH of 7.0, intracellular pH was 6.8 in the dark and 7.2 in the light. The cells of Agmenellum quadruplicatum, a marine species, were found to contain two separate compartments; in the dark, the pH of the cytoplasmic and the intrathylakoid spaces were calculated to be 7.2 and 5.5, respectively. Upon illumination, the former increased and the latter decreased by about 0.5 pH units.

Non-NASA Center↗
NASA NTRS1990

Amyloplasts as possible statoliths in gravitropic protonemata of the moss Ceratodon purpureus

The kinetics of gravitropism and of amyloplast sedimentation were studied in dark-grown protonemata of the moss Ceratodon purpureus (Hedw.) Brid. The protonemata grew straight up at a rate of 20-25 micromoles h-1 in nutrient-supplemented agar. After they were oriented to the horizontal, upward curvature was first detected after 1-1.5 h and reached 84 degrees by 24 h. The tip cells exhibited an amyloplast zonation, with a tip cluster of non-sedimenting amyloplasts, an amyloplast-free zone, and a zone with pronounced amyloplast sedimentation. This latter zone appears specialized more for lateral than for axial sedimentation since amyloplasts sediment to the lower wall in horizontal protonemata but do not fall to the basal wall in vertical protonemata. Amyloplast sedimentation started within 15 min of gravistimulation; this is within the 12-17-min presentation time. The data support the hypothesis that some amyloplasts function as statoliths in these cells.

NASA Discipline Number 29-20↗
NASA NTRS1990

Morphometric analysis of epidermal differentiation in primary roots of Zea mays

Epidermal differentiation in primary roots of Zea mays was divided into six cell types based on cellular shape and cytoplasmic appearance. These six cell types are: 1) apical protoderm, located at the tip of the root pole and characterized by periclinally flattened cells; 2) cuboidal protoderm, located approximately 230 microns from the root pole and characterized by cuboidal cells; 3) tabular epidermis, located approximately 450 microns from the root pole and characterized by anticlinally flattened cells; 4) cuboidal epidermis, located approximately 900 microns from the root pole and characterized by cuboidal cells having numerous small vacuoles; 5) vacuolate cuboidal epidermis, located approximately 1,500 microns from the root pole and characterized by cuboidal cells containing several large vacuoles; and 6) columnar epidermis, located approximately 2,200 microns from the root pole (i.e., at the beginning of the zone of elongation) and characterized by elongated cells. We also used stereology to quantify the cellular changes associated with epidermal differentiation. The quiescent center and the apical protoderm have significantly different ultrastructures. The relative volume of dictyosomes increases dramatically during the early stages of epidermal differentiation. This increase correlates inversely with the amount of coverage provided by the root cap and mucilage.

NASA Discipline Plant Biology↗
NASA NTRS1990

Microtubule distribution in gravitropic protonemata of the moss Ceratodon

Tip cells of dark-grown protonemata of the moss Ceratodon purpureus are negatively gravitropic (grow upward). They possess a unique longitudinal zonation: (1) a tip group of amylochloroplasts in the apical dome, (2) a plastid-free zone, (3) a zone of significant plastid sedimentation, and (4) a zone of mostly non-sedimenting plastids. Immunofluorescence of vertical cells showed microtubules distributed throughout the cytoplasm in a mostly axial orientation extending through all zones. Optical sectioning revealed a close spatial association between microtubules and plastids. A majority (two thirds) of protonemata gravistimulated for > 20 min had a higher density of microtubules near the lower flank compared to the upper flank in the plastid-free zone. This apparent enrichment of microtubules occurred just proximal to sedimented plastids and near the part of the tip that presumably elongates more to produce curvature. Fewer than 5% of gravistimulated protonemata had an enrichment in microtubules near the upper flank, whereas 14% of vertical protonemata were enriched near one of the side walls. Oryzalin and amiprophos-methyl (APM) disrupted microtubules, gravitropism, and normal tip growth and zonation, but did not prevent plastid sedimentation. We hypothesize that a microtubule redistribution plays a role in gravitropism in this protonema. This appears to be the first report of an effect of gravity on microtubule distribution in plants.

NASA Program Space Biology↗