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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

An analysis of long and medium-haul air passenger demand, volume 1

A basic model was developed which is a two equation pair econometric system in which air passenger demand and airline level-of-service are the endogenous variables. The model aims to identify the relationship between each of these two variables and its determining factors, and to identify the interaction of demand and level-of-service with each other. The selected variable for the measure of air passenger traffic activity in a given pair market is defined as the number of passengers in a given time that originate in one region and fly to the other region for purposes other than to make a connection to a third region. For medium and long haul markets, the model seems to perform better for larger markets. This is due to a specification problem regarding the route structure variable. In larger markets, a greater percentage of nonlocal passengers are accounted for by this variable. Comparing the estimated fare elasticities of long and medium haul markets, it appears that air transportation demand is more price elastic in longer haul markets. Long haul markets demand will saturate with a fewer number of departures than will demand in medium haul markets.

Eriksen, S. E.↗
NASA NTRS2002

Stimulus-response compatibility and psychological refractory period effects: implications for response selection

The purpose of this paper was to provide insight into the nature of response selection by reviewing the literature on stimulus-response compatibility (SRC) effects and the psychological refractory period (PRP) effect individually and jointly. The empirical findings and theoretical explanations of SRC effects that have been studied within a single-task context suggest that there are two response-selection routes-automatic activation and intentional translation. In contrast, all major PRP models reviewed in this paper have treated response selection as a single processing stage. In particular, the response-selection bottleneck (RSB) model assumes that the processing of Task 1 and Task 2 comprises two separate streams and that the PRP effect is due to a bottleneck located at response selection. Yet, considerable evidence from studies of SRC in the PRP paradigm shows that the processing of the two tasks is more interactive than is suggested by the RSB model and by most other models of the PRP effect. The major implication drawn from the studies of SRC effects in the PRP context is that response activation is a distinct process from final response selection. Response activation is based on both long-term and short-term task-defined S-R associations and occurs automatically and in parallel for the two tasks. The final response selection is an intentional act required even for highly compatible and practiced tasks and is restricted to processing one task at a time. Investigations of SRC effects and response-selection variables in dual-task contexts should be conducted more systematically because they provide significant insight into the nature of response-selection mechanisms.

Review Literature↗
NASA NTRS2001

Application of multivariate statistics to vestibular testing: discriminating between Meniere's disease and migraine associated dizziness

Meniere's disease (MD) and migraine associated dizziness (MAD) are two disorders that can have similar symptomatologies, but differ vastly in treatment. Vestibular testing is sometimes used to help differentiate between these disorders, but the inefficiency of a human interpreter analyzing a multitude of variables independently decreases its utility. Our hypothesis was that we could objectively discriminate between patients with MD and those with MAD using select variables from the vestibular test battery. Sinusoidal harmonic acceleration test variables were reduced to three vestibulo-ocular reflex physiologic parameters: gain, time constant, and asymmetry. A combination of these parameters plus a measurement of reduced vestibular response from caloric testing allowed us to achieve a joint classification rate of 91%, independent quadratic classification algorithm. Data from posturography were not useful for this type of differentiation. Overall, our classification function can be used as an unbiased assistant to discriminate between MD and MAD and gave us insight into the pathophysiologic differences between the two disorders.

NASA Discipline Neuroscience↗
NASA NTRS1975

Pneumatic load compensating or controlling system

A pneumatic load compensating or controlling system for restraining a load with a predetermined force or applying a predetermined force to the load is described; it includes a source of pressurized air, a one-way pneumatic actuator operatively connected to a load, and a fluid conduit fluidically connecting the actuator with the source of pressurized air. The actuator is of the piston and cylinder type, and the end of the fluid conduit is connected to the upper or lower portion of the cylinder whereby the actuator alternatively and selectively restrains the load with a predetermined force or apply a predetermined force to the load. Pressure regulators are included within the system for variably selectively adjusting the pressurized fluid to predetermined values as desired or required; a pressure amplifier is included within the system for multiplying the pressurized values so as to achieve greater load forces. An accumulator is incorporated within the system as a failsafe operating mechanism, and visual and aural alarm devices, operatively associated with pressure detecting apparatus, readily indicate the proper or improper functioning of the system.

Rogers, J. R.↗
NASA NTRS2017

GEOS-5 Seasonal Forecast System

Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere-Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9 month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.

GEOS-5↗
DOE OSTI2023

Salinization and sedimentation drive contrasting assembly mechanisms of planktonic and sediment‐bound bacterial communities in agricultural streams

Abstract Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy‐stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, whereas nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.

DeVilbiss, Stephen E.↗
DOE OSTI2024

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1975

Advanced supersonic propulsion system technology study, phase 2

Variable cycle engines were identified, based on the mixed-flow low-bypass-ratio augmented turbofan cycle, which has shown excellent range capability in the AST airplane. The best mixed-flow augmented turbofan engine was selected based on range in the AST Baseline Airplane. Selected variable cycle engine features were added to this best conventional baseline engine, and the Dual-Cycle VCE and Double-Bypass VCE were defined. The conventional mixed-flow turbofan and the Double-Bypass VCE were on the subjects of engine preliminary design studies to determine mechanical feasibility, confirm weight and dimensional estimates, and identify the necessary technology considered not yet available. Critical engine components were studied and incorporated into the variable cycle engine design.

Allan, R. D.↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to demonstrate the use of design and analysis of computer experiments (DACE) methods in Sandia’s DAKOTA software package for surrogate modeling and optimization. These methods were applied to a flow- path fueled with an interdigitated flushwall injector suitable for scramjet applications at hyper- velocity conditions and ascending along a constant dynamic pressure flight trajectory. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. Because the RAS of this case are computationally expensive, surrogate models are used for optimization. To build a surrogate model a RAS database is created. The sequence of the design variables comprising the database were generated using a Latin hypercube sampling (LHS) method. A methodology was also developed to automatically build geometries and generate structured grids for each design point. The ensuing RAS analysis generated the simulation database from which the two objective functions were computed using a one-dimensionalization (1D) of the three-dimensional simulation data. The data were fitted using four surrogate models: an artificial neural network (ANN), a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model predicted an optimal solution set that exhibited high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts may be required to lower the surrogate model errors and perform more accurate surrogate-model-based optimization.

Shenoy, Rajiv R.↗
DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗
NASA NTRS1977

Advanced supersonic propulsion study, phases 3 and 4

An evaluation of various advanced propulsion concepts for supersonic cruise aircraft resulted in the identification of the double-bypass variable cycle engine as the most promising concept. This engine design utilizes special variable geometry components and an annular exhaust nozzle to provide high take-off thrust and low jet noise. The engine also provides good performance at both supersonic cruise and subsonic cruise. Emission characteristics are excellent. The advanced technology double-bypass variable cycle engine offers an improvement in aircraft range performance relative to earlier supersonic jet engine designs and yet at a lower level of engine noise. Research and technology programs required in certain design areas for this engine concept to realize its potential benefits include refined parametric analysis of selected variable cycle engines, screening of additional unconventional concepts, and engine preliminary design studies. Required critical technology programs are summarized.

Allan, R. D.↗
NASA NTRS1981

A comparative analysis of rawinsonde and NIMBUS 6 and TIROS N satellite profile data

Comparisons are made between rawinsonde and satellite profiles in seven areas for a wide range of surface and weather conditions. Variables considered include temperature, dewpoint temperature, thickness, precipitable water, lapse rate of temperature, stability, geopotential height, mixing ratio, wind direction, wind speed, and kinematic parameters, including vorticity and the advection of vorticity and temperature. In addition, comparisons are made in the form of cross sections and synoptic fields for selected variables. Sounding data from the NIMBUS 6 and TIROS N satellites were used. Geostrophic wind computed from smoothed geopotential heights provided large scale flow patterns that agreed well with the rawinsonde wind fields. Surface wind patterns as well as magnitudes computed by use of the log law to extrapolate wind to a height of 10 m agreed with observations. Results of this study demonstrate rather conclusively that satellite profile data can be used to determine characteristics of large scale systems but that small scale features, such as frontal zones, cannot yet be resolved.

Scoggins, J. R.↗
NASA NTRS2008

GRCop-84 Rolling Parameter Study

This report is a section of the final report on the GRCop-84 task of the Constellation Program and incorporates the results obtained between October 2000 and September 2005, when the program ended. NASA Glenn Research Center (GRC) has developed a new copper alloy, GRCop-84 (Cu-8 at.% Cr-4 at.% Nb), for rocket engine main combustion chamber components that will improve rocket engine life and performance. This work examines the sensitivity of GRCop-84 mechanical properties to rolling parameters as a means to better define rolling parameters for commercial warm rolling. Experiment variables studied were total reduction, rolling temperature, rolling speed, and post rolling annealing heat treatment. The responses were tensile properties measured at 23 and 500 C, hardness, and creep at three stress-temperature combinations. Understanding these relationships will better define boundaries for a robust commercial warm rolling process. The four processing parameters were varied within limits consistent with typical commercial production processes. Testing revealed that the rolling-related variables selected have a minimal influence on tensile, hardness, and creep properties over the range of values tested. Annealing had the expected result of lowering room temperature hardness and strength while increasing room temperature elongations with 600 C (1112 F) having the most effect. These results indicate that the process conditions to warm roll plate and sheet for these variables can range over wide levels without negatively impacting mechanical properties. Incorporating broader process ranges in future rolling campaigns should lower commercial rolling costs through increased productivity.

Loewenthal, William S.↗
NASA NTRS1974

Experimental investigation of model variable-geometry and ogee tip rotors

An experimental investigation was conducted to systematically explore the effects of inter-blade spatial relationships and pitch variations on rotor performance and wake geometry. Variable-geometry rotors consisting of various combinations of blade length, axial spacing, azimuth spacing, and collective pitch were tested at model scale in hover and forward flight. In addition, a hover test of a model rotor with an ogee blade tip design was conducted to determine its performance and wake characteristics. The results of this investigation indicate that properly selected variable geometry rotor configurations can offer substantial improvements in hover performance without adversely affecting forward flight performance. Axial spacing of alternate blades was found to provide the greatest performance benefit, and further improvements were achieved by combining azimuth spacing with axial spacing. The performance benefit appears to be related to the relief of local adverse aerodynamic phenomena produced by vortex interference. The ogee tip design was found to substantially reduce the concentrated core intensity of the tip vortex, and could thus prove beneficial for the relief of blade-vortex interaction problems. However, the ogee tip was found to reduce hover performance at model scale.

Landgrebe, A. J.↗
DOE OSTI2020

Effect of Computational Schemes on Coupled Flow and Geo-Mechanical Modeling of CO 2 Leakage through a Compromised Well

Carbon capture, utilization, and storage (CCUS) describes a set of technically viable processes to separate carbon dioxide (CO 2 ) from industrial byproduct streams and inject it into deep geologic formations for long-term storage. Legacy wells located within the spatial domain of new injection and production activities represent potential pathways for fluids (i.e., CO 2 and aqueous phase) to leak through compromised components (e.g., through fractures or micro-annulus pathways). The finite element (FE) method is a well-established numerical approach to simulate the coupling between multi-phase fluid flow and solid phase deformation interactions that occur in a compromised well system. We assumed the spatial domain consists of a three-phases system: a solid, liquid, and gas phase. For flow in the two fluids phases, we considered two sets of primary variables: the first considering capillary pressure and gas pressure (PP) scheme, and the second considering liquid pressure and gas saturation (PS) scheme. Fluid phases were coupled with the solid phase using the full coupling (i.e., monolithic coupling) and iterative coupling (i.e., sequential coupling) approaches. The challenge of achieving numerical stability in the coupled formulation in heterogeneous media was addressed using the mass lumping and the upwinding techniques. Numerical results were compared with three benchmark problems to assess the performance of coupled FE solutions: 1D Terzaghi’s consolidation, Liakopoulos experiments, and the Kueper and Frind experiments. We found good agreement between our results and the three benchmark problems. For the Kueper and Frind test, the PP scheme successfully captured the observed experimental response of the non-aqueous phase infiltration, in contrast to the PS scheme. These exercises demonstrate the importance of fluid phase primary variable selection for heterogeneous porous media. We then applied the developed model to the hypothetical case of leakage along a compromised well representing a heterogeneous media. Considering the mass lumping and the upwinding techniques, both the monotonic and the sequential coupling provided identical results, but mass lumping was needed to avoid numerical instabilities in the sequential coupling. Additionally, in the monolithic coupling, the magnitude of primary variables in the coupled solution without mass lumping and the upwinding is higher, which is essential for the risk-based analyses.

deformation flow↗
NASA NTRS2003

Thermo-Mechanical Processing in Friction Stir Welds

Friction stir welding is a solid-phase joining, or welding process that was invented in 1991 at The Welding Institute (TWI). The process is potentially capable of joining a wide variety of aluminum alloys that are traditionally difficult to fusion weld. The friction stir welding (FSW) process produces welds by moving a non-consumable rotating pin tool along a seam between work pieces that are firmly clamped to an anvil. At the start of the process, the rotating pin is plunged into the material to a pre-determined load. The required heat is produced by a combination of frictional and deformation heating. The shape of the tool shoulder and supporting anvil promotes a high hydrostatic pressure along the joint line as the tool shears and literally stirs the metal together. To produce a defect free weld, process variables (RPM, transverse speed, and downward force) and tool pin design must be chosen carefully. An accurate model of the material flow during the process is necessary to guide process variable selection. At MSFC a plastic slip line model of the process has been synthesized based on macroscopic images of the resulting weld material. Although this model appears to have captured the main features of the process, material specific interactions are not understood. The objective of the present research was to develop a basic understanding of the evolution of the microstructure to be able to relate it to the deformation process variables of strain, strain rate, and temperature.

Schneider, Judy↗
NASA NTRS1978

A stochastic analysis of terrain evaluation variables for path selection

A stochastic analysis was performed on the variables associated with the characteristics of the terrain encountered by a roving system with an autonomous navigation system. A laser rangefinder is employed to detect terrain features at ranges up to 75 m. Analytic expressions and a numerical scheme were developed to calculate the variance of data on these four variables: (1) body clearance, (2) in-path slope, (3) tilt slope, and (4) wheel deviation. The variance is due to noise in the range data. It was found that the standard deviation of these terrain variables is large enough to warrant the use of a safety margin to aid the roving vehicle in avoiding high risk areas.

Donohue, J. G.↗
DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗