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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

STUDY OF THE η'π 0 SYSTEM IN GLUEX

The η'π is an important decay channel for the potentially exotic hybrid meson π 1 (1600) that has been detected in the COMPASS, VES and E852 experiments. These previous experiments used a charged pion beam as a probe. In contrast, the GlueX experiment is designed to search for the exotic hybrid mesons using a linearly polarized photon beam. This thesis project analyzes the η'π 0 system produced in GlueX by the reaction γp → η'π 0 p. After optimizing the event selection to minimize background contributions, a mass independent partial wave analysis is performed in search of an exotic signature. Within the currently available statistical precision, no signal was found.

Dotel, Rupesh↗
DOE OSTI2022

Experimental Evaluation of Thermal Storage Performance of a Dual-Purpose Underground Thermal Battery

A geothermal heat pump (GHP) system is an energy-efficient building heating and cooling technology with great potential for reducing energy consumption and decarbonization. However, applications of GHP are still limited due to the high cost, of which 30% is related to the cost of installing the conventional vertical bore ground heat exchangers, which are usually installed in boreholes 60 meters deep. A dual-purpose underground thermal battery (DPUTB) has been developed to offer a low-cost ground heat exchanger with a built-in thermal storage capacity. The DPUTB innovatively integrates a shallow-bore ground heat exchanger (the outer tank), which can be installed in a borehole less than 6 m deep, with thermal energy storage (TES) (the inner tank). DPUTB has the potential to reduce the cost of a ground source heat pump system while allowing shifting the electric demand of the building served by the GHP system from peak to off-peak hours of the electric grid by charging and discharging the thermal storage. A lab-scale (1:125 in volume) DPUTB prototype was built. Phase change material (PCM) was added to increase the thermal storage capacity and maintain the supply water temperature from the TES within the desired range for direct cooling operation during the discharge period. As PCMs are critical to the TES performance of the DPUTB, this study compared the influence of different PCMs (including salt hydrate and organic PCMs) on the discharge performance of the DPUTB. The thermal State of Charge (SoC) of the DPUTB was used to compare the performance resulting from using different PCMs. Test results indicate that the organic PCM (Methyl Laurate) outperforms salt-hydrate PCMs due to a lower melting temperature and a narrower melting temperature range during the phase change process. The results of this study provide a guide for PCM selection and the optimal design of DPUTB.

Wang, Lingshi↗
DOE OSTI2024

Generating An Advanced Cross-section Library For HTGR Pebble Bed Depletion Calculations Using Reduced-Order Model Generation Techniques

For code development, Advanced Reactor Technologies - Gas Cooled Reactors Program (ART-GCR) rely on a collaboration with the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, but the cross sections generation and the methodology definition is part of this program area goals. Based on previous studies in FY23, the size of microscopic cross section libraries increases rapidly with the number of tabulations, requiring significant amount of memory and drastically slowing down the Griffin calculations when evaluating cross sections via the multivariate linear interpolation approach. Rising to these challenges, this work investigates constructing Reduced-order Models (ROMs) for the multi-group microscopic cross sections to accelerate the cross section evaluation in Griffin. A database of multigroup cross sections is first collected considering all possible parameters that a designer could change for optimization. Down-selection of the ROM techniques afterward shows Deep Neural Network (DNN) as the best candidate when jointly consider memory efficiency, predictive accuracy, computational cost, scalability, flexibility and ease of implementation of the algorithms in comparison to the multidimensional interpolation. This work develops a specific interface that enables the cross section predictions using pre-trained DNN models into Griffin leveraging the existing ROM capabilities. DNNs have been trained for all isotopes for use in Griffin. Preliminary Griffin testing shows that DNNs exhibit exceptional predictive accuracy and the use of DNNs provides orders of magnitude improvement in memory efficiency compared to conventional interpolation techniques. With such ROM techniques, it holds great promise to further increase the fidelity of the Pebble Bed Reactor (PBR) simulation by increasing the number of tabulations/state variables during cross section evaluation, while maintaining the computational cost affordable in Griffin.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Iterative optimization yields Mcl-1–targeting stapled peptides with selective cytotoxicity to Mcl-1–dependent cancer cells

Bcl-2 family proteins regulate apoptosis, and aberrant interactions of overexpressed antiapoptotic family members such as Mcl-1 promote cell transformation, cancer survival, and resistance to chemotherapy. Discovering potent and selective Mcl-1 inhibitors that can relieve apoptotic blockades is thus a high priority for cancer research. An attractive strategy for disabling Mcl-1 involves using designer peptides to competitively engage its binding groove, mimicking the structural mechanism of action of native sensitizer BH3-only proteins. We transformed Mcl-1–binding peptides into α-helical, cell-penetrating constructs that are selectively cytotoxic to Mcl-1–dependent cancer cells. Critical to the design of effective inhibitors was our introduction of an all-hydrocarbon cross-link or “staple” that stabilizes α-helical structure, increases target binding affinity, and independently confers binding specificity for Mcl-1 over related Bcl-2 family paralogs. Two crystal structures of complexes at 1.4 Å and 1.9 Å resolution demonstrate how the hydrophobic staple induces an unanticipated structural rearrangement in Mcl-1 upon binding. Systematic sampling of staple location and iterative optimization of peptide sequence in accordance with established design principles provided peptides that target intracellular Mcl-1. Here, this work provides proof of concept for the development of potent, selective, and cell-permeable stapled peptides for therapeutic targeting of Mcl-1 in cancer, applying a design and validation workflow applicable to a host of challenging biomedical targets.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2014

Probiotics in the Space Food System: Delivery, Microgravity Effects, and the Potential Benefit to Crew Health

As mission distance and duration increase, the need grows for non‐invasive disease prevention and immunomodulation, especially given the limited medical response capability expected for these missions and the immune dysregulation documented in crew. Additionally, changes in diet, lifestyle, antibiotic usage, and the environmental stresses during spaceflight may alter crewmembers' intestinal microbiome. The addition of probiotic bacteria to the space food system is expected to confer immunostimulatory benefits on crewmembers, with the potential to counteract the immune dysregulation that has been documented in spaceflight. Based on previous studies that demonstrated unique microbiological responses to the low shear environment of spaceflight, probiotic organisms hold the potential to induce enhanced beneficial responses through mechanisms, such as beneficial interactions with human immune cells and repression of colonization of pathogens on the mucosa. The work presented here will begin to address two research gaps related to providing probiotics in spaceflight: 1) delivery, and 2) the effect of the low shear microgravity environment on probiotic attributes. The probiotic Lactobacillus acidophilus was selected for investigation due to its wide commercial use and documented benefits that include inhibition of virulence related gene expression in pathogens and mucosal stimulation of immune cells. The delivery system for probiotics has not been determined for spaceflight, where the food system is shelf stable and the lack of refrigeration prevents the use of traditional dairy delivery methods. In order to demonstrate the potential of the space food system to deliver viable probiotic bacteria to crewmembers, the probiotic L. acidophilus was packaged in high barrier flight packaging in nonfat dry milk (NFDM) or retained in commercial capsule form. Viable cells were enumerated over 8 months of storage at 22, 4, and ‐80ºC. The survival of L. acidophilus rehydrated in NFDM, in a PBS control, and directly from the capsule was also evaluated following stress challenge with simulated gastric and intestinal juices to determine the method that would deliver the most viable cells to the intestine, where they would be expected to confer beneficial effects. L. acidophilus was found to be stable to gastric and intestinal juice challenge when delivered in rehydrated NFDM, even after two hours of exposure. In comparison, L. acidophilus was reduced by 1‐5 logs when exposed to gastric and intestinal juice directly and when rehydrated in a PBS control. Shelf life data indicated that L. acidophilus would require refrigerated or frozen storage to remain viable at adequate levels over the multi‐year storage periods required for spaceflight. This study suggests that the protective effect provided by the dairy matrix, and not merely rehydration prior to consumption, extends probiotic viability and stress tolerance during storage in spaceflight and in simulated digestion conditions more adequately than a capsule. In addition to effective delivery, it is essential to understand the microgravity effects on the stress tolerances and genetic expression of probiotic bacteria to enable optimization of growth, survival, strain selection, and conferred benefits in spaceflight. In our current study, ground‐based characterization of the growth, stress response, and transcriptomic response of L. acidophilus will be accomplished using the low shear modeled microgravity (LSMMG) culture environment in the rotating wall vessel (RWV). We hypothesize that L. acidophilus will maintain or improve its growth and stress response following culture in the LSMMG environment, and that transcriptomic analysis will define the associated molecular mechanism(s), resulting in the ability to optimize strain selection.

Castro, S. L.↗
DOE OSTI2024

SMARTER Rules-Based Distributed Deconfliction of ADMS Applications

A conceptual numerical methodology derived from Grid Architecture principles is introduced for deconflicting setpoints issued by multiple advanced distribution management system applications. The methodology applies technical, economic, environmental, and social rules to eliminate non-viable combinations. The concept of temporal equipment controls budgets is introduced to preserve the health of physical assets and avoid equipment damage through repeated controls cycling. The rules are combined with a multi-criteria decision-making framework to select a near-optimal set of deconflicted setpoints using a set of qualitative and quantitative decision criteria selected by the distribution system operator. Numerical results are demonstrated on the IEEE 123-bus test feeder for three competing applications. Three alternative distributed schemes are used to decompose the problem: by topological area, by phase, and fully decentralized. The fully decentralized implementation is shown to yield near-optimal deconfliction results with significantly reduced computational time.

Anderson, Alexander A.↗
NASA NTRS2021

Optimizing serendipitous detections of kilonovae: cadence and filter selection

The rise of multimessenger astronomy has brought with it the need to exploit all available data streams and learn more about the astrophysical objects that fall within its breadth. One possible avenue is the search for serendipitous optical/near-infrared counterparts of gamma-ray bursts (GRBs) and gravitational-wave (GW) signals, known as kilonovae. With surveys such as the Zwicky Transient Facility (ZTF), which observes the sky with a cadence of ∼3 d, the existing counterpart locations are likely to be observed; however, due to the significant amount of sky to explore, it is difficult to search for these fast-evolving candidates. Thus, it is beneficial to optimize the survey cadence for realtime kilonova identification and enable further photometric and spectroscopic observations. We explore how the cadence of wide field-of-view surveys like ZTF can be improved to facilitate such identifications. We show that with improved observational choices, e.g. the adoption of three epochs per night on a ∼ nightly basis, and the prioritization of redder photometric bands, detection efficiencies improve by about a factor of two relative to the nominal cadence. We also provide realistic hypothetical constraints on the kilonova rate as a form of comparison between strategies, assuming that no kilonovae are detected throughout the long-term execution of the respective observing plan. These results demonstrate how an optimal use of ZTF increases the likelihood of kilonova discovery independent of GWs or GRBs, thereby allowing for a sensitive search with less interruption of its nominal cadence through Target of Opportunity programs.

methods: observational↗
DOE OSTI2022

Load Mitigating Feedback Control of WECs

This report outlines the development of load-mitigating feedback control for wave energy converters. A simple, self-tuning multi-objective controller is demonstrated in simulation for a 3-DOF (surge, heave, pitch) point absorber. In previous work, the proposed control architecture has been shown to be effective in experiment for a variety of device archetypes for the single objective of the maximization of electrical power capture: here this architecture is extended to reduce device loading as well. In particular, PTO actuation forces and the minimization of fatigue damage (determined from the sum of wave-exerted and PTO forces) are considered as additional objectives for the self-tuning controller. This controller is demonstrated for two similar, but distinct systems: one described by the identified linear models from physical testing of the WaveBot device, and another based upon a WEC-Sim simulation that expands upon boundary element method data from the WaveBot device. In both cases, because the power surface is consistently fairly flat in the vicinity of control parameters that maximize power capture in contrasting sea-states, it is found to be generally possible to mitigate either fatigue damage or PTO load. However, PTO load is found to conflict with fatigue damage in some sea-states, limiting the efficacy of control objectives that attempt to mitigate both simultaneously. Additionally, coupling between the surge and pitch DOFs also limits the extent to which fatigue damage can be mitigated for both DOFs in some sea-states. Because control objectives can be considered a function of the sea-state (e.g., load mitigation may not be a concern until the sea is sufficiently large) a simple transition strategy is proposed and demonstrated. This transition strategy is found to be effective with some caveats: firstly, it cannot circumvent the aforementioned objective contradictions. Secondly, this objective transition is too slow to act as a system constraint, and objective thresholds must thus be considered quite conservatively. Improvement of the adjustment strategy is demonstrated through the addition of an integral term. Selection of well-performing transition parameters can be a function of sea-state. While a simple selection procedure is proposed, it is non-optimal, and a more robust selection procedure is suggested for future work.

16 TIDAL AND WAVE POWER↗
NASA NTRS2022

Landing Site Selection with a Variable-Resolution SLAM-Refined Map

In many scenarios it is desirable for planetary landers to select or modify their landing sites autonomously during descent. We present a landing site selection algorithm which is optimized to work in conjunction with a Simultaneous Localization and Mapping system. Our algorithm selects landing sites based on site slope, roughness, and operator-defined interest. In addition, we generate guidance commands and approximate fuel consumption for the highest ranked sites. We validate our algorithm with LiDAR and inertial data gathered by a vertical take-off and landing vehicle.

Chen, Po-Ting↗
DOE OSTI2024

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Measurement of $\Delta m^2_{32}$ and $\sin^2\!\theta_{23}$ using Muon Neutrino and Antineutrino Beams in the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment located in the mid-west United States. It consists of two functionally identical tracking calorimeters, known as the near and far detector, that measure neutrino interactions induced by the NuMI beam at baselines of 1~km and 810~km. NuMI can be configured to provide a muon neutrino or antineutrino beam. Analysis of \numu~+~\numubar disappearance allows constraint of the $|\Delta m^2_{32}|$ and \sint oscillation parameters. This thesis presents the first NOvA disappearance results using both neutrino and antineutrino data - previous NOvA analyses have only used neutrino beam data. Two analysis improvements are delineated in dedicated chapters - the design of selection criteria to identify events that are fully contained in the far detector and the optimization of particle identification selection criteria in a multi-dimensional parameter space. The full-detector equivalent beam exposures used for this thesis are $8.85\ti mes 10^{20}$ and $6.91\times 10^{20}$~protons~on~target for neutrino and antineutrino data respectively. Under the assumption of a normal (inverted) neutrino mass hierarchy, analysis of the data gives $\Delta m^2_{32}=+2.49^{+0.09}_{-0.07}\times10^{-3}~\text{eV}^2$ ($\Delta m^2_{32}=-2.54\pm0.08\times10^{-3}~\text{eV}^2$) and $\sin^2\theta_{23} = 0.59\pm0.03$ ($\sin^2\theta_{23} = 0.44\pm0.03$). Maximal mixing ($\sin^2\theta_{23} = 0.5$) is disfavoured at the 1.7~\sig level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Graphene-polyelectrolyte multilayer membranes with tunable structure and internal charge

One great advantage of graphene-polyelectrolyte multilayer (GPM) membranes is their tunable structure and internal charge for improved separation performance. In this study, we synthesized GO-dominant GPM membrane with internal negatively-charged domains, polyethyleneimine (PEI)-dominant GPM membrane with internal positively-charged domains and charge-balanced dense/loose GPM membranes by simply adjusting the ionic strength and pH of the GO and PEI solutions used in layer-by-layer membrane synthesis. A combined system of quartz crystal microbalance with dissipation (QCM-D) and ellipsometry was used to analyze the mass deposition, film thickness, and layer density of the GPM membranes. The performance of the GPM membranes were compared in terms of both permeability and selectivity to determine the optimal membrane structure and synthesis strategy. One effective strategy to improve the GPM membrane permeability-selectivity tradeoff is to assemble charge-balanced dense membranes under weak electrostatic interactions. This balanced membrane exhibits the highest MgCl 2 selectivity (~86%). Another effective strategy for improved cation removal is to create PEI-dominant membranes that provide internal positively-charged barrier to enhance cation selectivity without sacrificing water permeability. Finally, these findings shine lights on the development of a systematic approach to push the boundary of permeability-selectivity tradeoff for GPM membranes.

42 ENGINEERING↗
DOE OSTI2018

Optimization, Inference and Learning for Advanced Networks

This piece of software aims at providing an implementation of methods to perform efficient optimization, probabilistic inference and model selection and learning for various type of networked systems. The software contains a suite of methods and functions that: 1) perform probabilistic inference and random sampling on networked models 2) select or learn networked models from data 3) solve optimization tasks with a networked structure Network models that are supported by these features include, but are not limited to, factor graphs and Markov random fields such as Ising models, Potts models, p-spins models, Gaussian random fields and restricted Boltzmann machine.

Hijazi, Hassan↗
NASA NTRS1991

SeGRAm - A practical and versatile tool for spacecraft trajectory optimization

An implementation of the Sequential Gradient/Restoration Algorithm, SeGRAm, is presented along with selected examples. This spacecraft trajectory optimization and simulation program uses variational calculus to solve problems of spacecraft flying under the influence of one or more gravitational bodies. It produces a series of feasible solutions to problems involving a wide range of vehicles, environments and optimization functions, until an optimal solution is found. The examples included highlight the various capabilities of the program and emphasize in particular its versatility over a wide spectrum of applications from ascent to interplanetary trajectories.

Rishikof, Brian H.↗