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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

NASA Space Launch System Operations Outlook

The National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program, managed at the Marshall Space Flight Center (MSFC), is working with the Ground Systems Development and Operations (GSDO) Program, based at the Kennedy Space Center (KSC), to deliver a new safe, affordable, and sustainable capability for human and scientific exploration beyond Earth's orbit (BEO). Larger than the Saturn V Moon rocket, SLS will provide 10 percent more thrust at liftoff in its initial 70 metric ton (t) configuration and 20 percent more in its evolved 130-t configuration. The primary mission of the SLS rocket will be to launch astronauts to deep space destinations in the Orion Multi- Purpose Crew Vehicle (MPCV), also in development and managed by the Johnson Space Center. Several high-priority science missions also may benefit from the increased payload volume and reduced trip times offered by this powerful, versatile rocket. Reducing the lifecycle costs for NASA's space transportation flagship will maximize the exploration and scientific discovery returned from the taxpayer's investment. To that end, decisions made during development of SLS and associated systems will impact the nation's space exploration capabilities for decades. This paper will provide an update to the operations strategy presented at SpaceOps 2012. It will focus on: 1) Preparations to streamline the processing flow and infrastructure needed to produce and launch the world's largest rocket (i.e., through incorporation and modification of proven, heritage systems into the vehicle and ground systems); 2) Implementation of a lean approach to reach-back support of hardware manufacturing, green-run testing, and launch site processing and activities; and 3) Partnering between the vehicle design and operations communities on state-of-the-art predictive operations analysis techniques. An example of innovation is testing the integrated vehicle at the processing facility in parallel, rather than sequentially, saving both time and money. These themes are accomplished under the context of a new cross-program integration model that emphasizes peer-to-peer accountability and collaboration towards a common, shared goal. Utilizing the lessons learned through 50 years of human space flight experience, SLS is assigning the right number of people from appropriate backgrounds, providing them the right tools, and exercising the right processes for the job. The result will be a powerful, versatile, and capable heavy-lift, human-rated asset for the future human and scientific exploration of space.

Hefner, William Keith↗
DOE OSTI2021

Variational Information Planning for Sequential Decision Making

We consider the setting of sequential decision making where, at each stage, potential actions are evaluated based on expected reduction in posterior uncertainty, given by mutual information (MI). As MI typically lacks a closed form, we propose an approach which maintains variational approximations of, both, the posterior and MI utility. Our planning objective extends an established variational bound on MI to the setting of sequential planning. The result, variational information planning (VIP), is an efficient method for sequential decision making. We further establish convexity of the variational planning objective and, under conditional exponential family approximations, we show that the optimal MI bound arises from a relaxation of the well-known exponential family moment matching property. Here, we demonstrate VIP for sensor selection, experiment design, and active learning, where it meets or exceeds methods requiring more computation, or those specialized to the task.

Pacheco, Jason↗
NASA NTRS1994

Propagation of experimental uncertainties from the tunnel to the body coordinate system in 3-D LDV flow field studies

An analysis of experimental laser Doppler velocimetry (LDV) data uncertainties that propagate from measurements in the tunnel coordinate system to results in the model system are provided. Calculations of uncertainties as functions of the variables that comprise the final result requires assessment of the contribution each variable makes. Such an analysis enables and necessitates the experimentalists to identify and address the contributing error sources in the experimental measurement system. This provides an opportunity to improve the quality of data derived from experimental systems. This is especially important in experiments where small changes in test conditions are expected to produce small, detectable changes in results. In addition, the need for high-quality experimental data for CFD method validation demands a thorough assessment of experimental uncertainty. Transforming from one Cartesian coordinate system to another by three sequential rotations, equations were developed to transform the variables initially obtained in the original coordinates into variables in the final coordinate system. Based on the transformation equations, propagation equations for errors in the experimentally-derived flow quantities were derived for a model at angle of attack. Experimental uncertainties were then propagated from the tunnel coordinate system into the model system.

Neuhart, Dan H.↗
DOE OSTI2023

Manganese sources and rates impact plant Mn concentrations and soil Mn fractions

Manganese (Mn) is an essential micronutrient for all organisms. In plants, Mn plays a critical role in photosynthesis and as a structural component of enzymes. In soils, Mn exists as different fractions of varying availability to plants. Mn fertilizers can be used to increase Mn availability to plants but are easily converted from a plant-available fraction (exchangeable Mn) to an unavailable fraction (Mn-oxides). Little research has been done in agricultural settings on soil Mn fractions; thus, the objective of this experiment was to study the effect of Mn additions on soil Mn fractions and plant Mn concentration. Here, a greenhouse experiment was conducted by growing soybean (Glycine max) treated with three Mn application rates (recommended or 1×, 10×, and 50×) of two sources (MnSO 4 and Mn ethylenediamine tetraacetic acid [MnEDTA]). Soil Mn fractions (Mehlich-1 extractable, exchangeable, organic-bound, Mn-oxide, residual) were quantified via sequential Mn extraction procedures. Bioavailability was evaluated by measuring soybean leaf Mn concentrations. Both fertilizer types increased available soil Mn fractions. Leaf Mn concentration increased with MnSO 4 50× application at the V3 stage and decreased with MnEDTA addition at the V3 and R2 stages. Mn additions likely resulted in the conversion of Mn into unavailable fractions. This was amplified by higher application rates, where total Mn increased by 18.5%, but available Mn decreased by 4.3% relative to initial soil values. Thus, our study showed that adding Mn to soils does not necessarily increase plant-available Mn.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Enhanced Catalytic Dechlorination of Polyvinyl Chloride (PVC) and H2 Production Enabled by Synergistic Gaδ+/Ga0 Active Sites in Liquid Metal Particles

Polyvinyl chloride (PVC) is ubiquitous yet challenging to recycle due to its tendency to thermally decompose above 250 °C, releasing toxic, corrosive chlorinated compounds, and its inability to melt. Here, we report a catalytic strategy for PVC upcycling at 160 °C using gallium liquid metal particles (Ga-LMP) featuring a dynamic Ga-GaOOH core–shell architecture. These catalysts enable concurrent dechlorination and hydrogen evolution, yielding up to 7% H2 (based on initial hydrogen atoms in PVC) along with a highly dechlorinated (>95%) carbonaceous solid and aqueous HCl. Mechanistic investigations combining X-ray photoelectron spectroscopy, infrared spectroscopy, solid-state NMR, inelastic neutron scattering, and ab initio molecular dynamics reveal a synergistic interplay between Gaδ+ sites in the GaOOH shell and metallic Ga0 in the core. Cationic Ga initiates C–Cl bond activation and HCl formation, while progressive reduction of the shell exposes Ga0 sites that promote C–H activation and H2 evolution. Control experiments with a Ga salt and bulk Ga liquid metal confirmed that neither oxidation state alone can achieve both transformations efficiently. This work establishes a dynamic dual-site paradigm for liquid metal catalysis, in which the in situ evolution and coexistence of oxidized and metallic species enable sequential and cooperative bond activation pathways. These findings provide a general design principle for novel liquid metal catalysts that target challenging polymer transformations under mild conditions.

Zingg, Benjamin [ORNL] (ORCID:0009000914530153)↗
DOE OSTI2021

Combination of DGA and LN Columns: A Versatile Option for Isotope Production and Purification at Oak Ridge National Laboratory

The chromatographic resins DGA and LN have been used sequentially to separate curium, californium, plutonium, and various fission products (FPs) to obtain products with a pure radioisotope or a radioelement with high isotopic purity. This 2-step combination of resins has found applications in the 252 Cf and 238 Pu campaigns performed at Oak Ridge National Laboratory. Experiments have been carried out in glove boxes, shielded caves, and hot cells, demonstrating that these resins are adequate and maintain their performance in highly radiolytic environments. We find that using these resins has led to the recovery of curium highly enriched (>95%) in 248 Cm, the clean separation of 251 Cf for the superheavy element research, and the recovery of 147 Pm from the FP stream obtained during the 238 Pu production campaign while demonstrating that the raffinate post DGA is essentially free of transuranic elements.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

AIF for Vis (Active Inference for simulating human interpretation of data visualization) [SWR-26-084]

AIF for Vis contains the Active Inference models and analysis scripts used to study a simple visualization-interpretation task: estimating the average value of two bars in a bar chart. The work is a proof of concept for translating hypothesized cognitive strategies into executable, inspectable process models. We implement two idealized strategies inspired by dual-process accounts of visualization-aided decision making: *Fast model: a compressed, heuristic strategy that estimates the visual midpoint of the two bars and maintains a single belief over their average. *Slow model: a sequential, analytic strategy that estimates the two bar heights separately and maintains them in working memory before computing an average. Both models use a common Active-Inference-inspired framework for sequential perception, belief updating, action selection, and reporting. Their different internal representations produce distinct predicted vulnerabilities: *the Fast model is more susceptible to tick-salience bias; *the Slow model is more susceptible to working-memory decay. The repository includes the model implementations, scripts used for the experiments reported in the paper, precomputed trial-level results, and plotting scripts.

Goldwyn, Harrison [National Laboratory of the Rock↗
DOE OSTI2020

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗
NASA NTRS1985

Development of the VOLT-A shuttle experiment

The NASA Lewis Research Center (LeRC) is investigating potential problems associated with the operation of high voltage solar cell arrays in the space plasma environment. At high voltages, interactions between the solar array and the space plasma could result in unacceptable levels of electrical discharge (arcing) and/or parasitic losses (current drains from the array to the plasma). The objective of the Voltage Operating Limit Tests (VOLT-A) Shuttle bay experiment is to characterize space plasma/solar cell panel interactions in low earth orbit. VOLT-A consists of an experiment plate subassembly which contains four solar panels, an electronics subassembly and a Langmuir probe subassembly mounted on an MPESS carrier. During a given 8.25 hour data taking period (5-1/2 continuous orbits), the solar panels, which represent state-of-the-art solar cell technologies, will be sequentially subjected to bias voltages in steps ranging from minus 626 V to plus 313 V. Appropriate measurements will be made at each voltage to characterize arcing and parasitic losses. Corresponding measurements of the plasma environment (plasma density, electron temperature and neutral density) will also be made. Data will be recorded on an on-board tape recorder for subsequent data reduction and analysis.

Bifano, W. J.↗
NASA NTRS1985

Development of the VOLT-A Shuttle experiment

The NASA Lewis Research Center (LeRC) is investigating potential problems associated with the operation of high voltage solar cell arrays in the space plasma environment. At high voltages, interactions between the solar array and the space plasma could result in unacceptable levels of electrical discharge (arcing) and/or parasitic losses (current drains from the array to the plasma). The objective of the Voltage Operating Limit Tests (VOLT-A) Shuttle bay experiment is to characterize space plasma/solar cell panel interactions in low earth orbit. VOLT-A consists of an experiment plate subassembly which contains four solar panels, an electronics subassembly and a Langmuir probe subassembly mounted on an MPESS carrier. During a given 8.25 hour data taking period (5-1/2 continuous orbits), the solar panels, which represent state-of-the-art solar cell technologies, will be sequentially subjected to bias voltages in steps ranging from minus 626 V to plus 313 V. Appropriate measurements will be made at each voltage to characterize arcing and parasitic losses. Corresponding measurements of the plasma environment (plasma density, electron temperature and neutral density) will also be made. Data will be recorded on an on-board tape recorder for subsequent data reduction and analysis.

Bifano, William J.↗
DOE OSTI2020

Validation of Hygrothermal Simulations with Wall Performance Experiments in an Environmental Chamber

Oak Ridge National Laboratory is developing a web tool, built on a rule-based expert system, that aids stakeholders in designing energy-efficient moisture-durable walls. Currently, the tool’s expert system database is mostly populated with expert opinion, but work is being done to incorporate durability assessment based on stochastic hygrothermal modeling. Chamber experiments have been conducted to validate the hygrothermal models. The measured temperature and humidity have been compared with those predicted by the hygrothermal model. Experiments and comparison to one-dimensional hygrothermal modeling results were completed with two walls, a Structural Insulated Panel (SIP) based wall and a Concrete Masonry Unit (CMU) based wall. The two walls were succumbed to typical Chicago weather in ORNL’s Heat, Air and Moisture chamber. The walls were sequentially exposed to different scenarios, including diffusion, wetting, air leakage and solar radiation. For the most part, the hygrothermal simulations can be successfully used to predict the performance of these wall systems for the moisture transport phenomenon. Errors between measured and simulated values decreased as measurements got closer to the interior side of the wall. The root mean square error was larger for relative humidity (up to 17.5%-RH for CMU) than for temperature (up to 4.5°C for CMU wall). The errors were larger for the CMU wall than the wood frame wall. The phenomenon, including liquid water, caused large discrepancies between measurement and simulation results, and simulated results showed slower drying of materials than measured results. The one-dimensional nature of the simulation model made simulating air leaks difficult but not impossible.

Salonvaara, Mikael↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2025

Bioremediation of Chlorinated Volatile Organic Compounds: DOE Experiences and Lessons Learned

From the mid-1980s to the present, the Department of Energy (DOE) has developed, tested, and deployed diverse bioremediation strategies for chlorinated volatile organic compounds (cVOCs). A systematic review of these projects after decades of activity provides an opportunity to identify crosscutting themes and lessons learned. The knowledge provided by a DOE bioremediation retrospective represents a resource to support current and future bioremediation operations, and future decisions related to cVOC bioremediation. This systematic review examined the design, objectives, performance and outcomes for remediation projects at DOE sites including Savannah River, Hanford, Idaho, Mound and Pinellas. The results were used to identify emergent themes to provide actionable insights. The bioremediation retrospective technical team first developed standardized criteria to support the systematic review. Then, the evaluation was performed using a sequential process that was informed by local technical experts who identified and provided the structured information that served as the basis for the evaluation. The participation of these experts was invaluable to the effort. Importantly, DOE cVOC bioremediation efforts were implemented based on the foundational knowledge developed by U.S. Department of Defense (DoD) strategic and applied environmental technology development and certification programs, as well as technical, policy and regulatory guidance from the U.S. Environmental Protection Agency (EPA), Interstate Technology and Regulatory Council (ITRC), U.S. Geological Survey (USGS), industry, and universities. To maximize the value of the DOE cVOC bioremediation retrospective, the systematic review strategy focused on identifying important DOE-specific experiences, trends and lessons learned that would extend the knowledge available from these other key entities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Near minimum-time maneuvers of large space structures using parameter optimization

Near minimum-time attitude maneuvers for large, inherently-flexible space structures with finite fuel supplies are investigated. The open loop maneuver is determined with the Sequential Quadratic Programming (SQP) algorithm, which optimizes a bang-off-bang control parameter set for the given maneuver. Torque smoothing is used to prevent discontinuities in the control which would excite the flexible structure. Additional system dynamics such as thruster inefficiency, spring forces and pressure leaks are identified from preliminary experiments on the ASTREX test article.

Carter, M. T.↗
NASA NTRS1994

Improved solution accuracy for Landsat-4 (TDRSS-user) orbit determination

This paper presents the results of a study to compare the orbit determination accuracy for a Tracking and Data Relay Satellite System (TDRSS) user spacecraft, Landsat-4, obtained using a Prototype Filter Smoother (PFS), with the accuracy of an established batch-least-squares system, the Goddard Trajectory Determination System (GTDS). The results of Landsat-4 orbit determination will provide useful experience for the Earth Observing System (EOS) series of satellites. The Landsat-4 ephemerides were estimated for the January 17-23, 1991, timeframe, during which intensive TDRSS tracking data for Landsat-4 were available. Independent assessments were made of the consistencies (overlap comparisons for the batch case and convariances for the sequential case) of solutions produced by the batch and sequential methods. The filtered and smoothed PFS orbit solutions were compared with the definitive GTDS orbit solutions for Landsat-4; the solution differences were generally less than 15 meters.

Oza, D. H.↗
NASA NTRS1999

Moisture Forecast Bias Correction in GEOS DAS

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

Dee, D.↗
NASA NTRS1985

GPS navigation experiment using high precision GPS timing receivers

Global Positioning System (GPS) Time Transfer receivers were developed by the Naval Research Laboratory (NRL) to provide synchronization for the NASA Global Laser Tracking Network (GLTN). The capabilities of the receiver are being expanded mainly through software modification to: Demonstrate the position location capabilities of a single channel receiver unsign the GPS C/A code; and Demonstrate the time/navigation capability of the receiver onboard a moving platform, by sequential tracking of GPS satellites.

Buisson, J. A.↗