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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 NTRS2000

Influence of central set on anticipatory and triggered grip-force adjustments

The effects of predictability of load magnitude on anticipatory and triggered grip-force adjustments were studied as nine normal subjects used a precision grip to lift, hold, and replace an instrumented test object. Experience with a predictable stimulus has been shown to enhance magnitude scaling of triggered postural responses to different amplitudes of perturbations. However, this phenomenon, known as a central-set effect, has not been tested systematically for grip-force responses in the hand. In our study, predictability was manipulated by applying load perturbations of different magnitudes to the test object under conditions in which the upcoming load magnitude was presented repeatedly or under conditions in which the load magnitudes were presented randomly, each with two different pre-load grip conditions (unconstrained and constrained). In constrained conditions, initial grip forces were maintained near the minimum level necessary to prevent pre-loaded object slippage, while in unconstrained conditions, no initial grip force restrictions were imposed. The effect of predictable (blocked) and unpredictable (random) load presentations on scaling of anticipatory and triggered grip responses was tested by comparing the slopes of linear regressions between the imposed load and grip response magnitude. Anticipatory and triggered grip force responses were scaled to load magnitude in all conditions. However, regardless of pre-load grip force constraint, the gains (slopes) of grip responses relative to load magnitudes were greater when the magnitude of the upcoming load was predictable than when the load increase was unpredictable. In addition, a central-set effect was evidenced by the fewer number of drop trials in the predictable relative to unpredictable load conditions. Pre-load grip forces showed the greatest set effects. However, grip responses showed larger set effects, based on prediction, when pre-load grip force was constrained to lower levels. These results suggest that anticipatory processes pertaining to load magnitude permit the response gain of both voluntary and triggered rapid grip force adjustments to be set, at least partially, prior to perturbation onset. Comparison of anticipatory set effects for reactive torque and lower extremity EMG postural responses triggered by surface translation perturbations suggests a more general rule governing anticipatory processes.

Non-NASA Center↗
DOE OSTI2023

Bayesian Exploration of Phenomenological EoS of Neutron/Hybrid Stars with Recent Observations

The description of the stellar interior of compact stars remains as a big challenge for the nuclear astrophysics community. The consolidated knowledge is restricted to density regions around the saturation of hadronic matter ρ 0 =2.8 × 10 14 g cm -3 , regimes where our nuclear models are successfully applied. As one moves towards higher densities and extreme conditions up to the quark/gluons deconfinement, little can be said about the microphysics of the equation of state (EoS). Here, we employ a Markov Chain Monte Carlo (MCMC) strategy to access the variability at high density regions of polytropic piecewise models for neutron star (NS) EoS or possible hybrid stars, i.e., a NS with a small quark-matter core. With a fixed description of the hadronic matter for low density, below the nuclear saturation density, we explore a variety of models for the high density regimes leading to stellar masses near to 2.5 M ⊙ , in accordance with the observations of massive pulsars. The models are constrained, including the observation of the merger of neutrons stars from VIRGO-LIGO and with the pulsar observed by NICER. In addition, we also discuss the possibility of the use of a Bayesian power regression model with heteroscedastic error. The set of EoS from the Laser Interferometer Gravitational-Wave Observatory (LIGO) was used as input and treated as the data set for the testing case.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Predicting LANDSAT MSS endmember signatures from corresponding higher resolution TM fraction images

The approach to predicting Landsat Multispectral Scanner System (MSS) endmember signatures from fraction images derived from high resolution Landsat Thematic Mapper (TM) data is presented. The purpose, conducted in the Mogi-Guacu study site located in Sao Paulo State, Brazil, was to determine if information derived from a mixture model applied to higher resolution TM data could serve as ground truth for the lower resolution MSS sensor. The Constrained Least Squares (CLS) method was used to generate vegetation, soil, and shade fraction images from the TM data. The resulting images were then used to estimate the endmember spectral response for MSS data by regressing each MSS spectral channel against the corresponding proportion values estimated for the same resolution cells from the TM mixture model. The evaluation of the predicted signatures was performed by comparing the corresponding fraction images derived from both the MSS and TM data acquired on 14 Sep. 1986. The techniques serves as a potential tool for integrating information in global studies where remote sensors with different spectral and spatial resolutions were used.

Shimabukuro, Yosio E.↗
DOE OSTI2021

Spatial biases of information influence global estimates of soil respiration: how can we improve global predictions?

Soil respiration (Rs), the efflux of CO2 from soils to the atmosphere, is a major component of the terrestrial carbon cycle, but is poorly constrained from regional to global scales. The global soil respiration database (SRDB) is a compilation of in-situ Rs observations from around the globe that has been consistently updated with new measurements over the past decade. It is unclear whether the addition of data to new versions of the SRDB has produced better-constrained global Rs estimates. We compared two versions of the SRDB (v3.0 n=5173 and v5.0 n=10366) to determine how additional data influenced the global sum and uncertainty of Rs as predicted by a quantile regression forest model. A model parameterized using SRDBv3 yielded a global Rs sum of 88.6 Pg C yr-1, and associated uncertainty of 29.9 (mean absolute error) and 57.9 (standard deviation) Pg C yr-1, whereas parameterization using SRDBv5 yielded 96.5 Pg C yr-1 and associated uncertainty of 30.2 (mean average error) and 73.4 (standard deviation) Pg C yr-1. Empirically estimated global heterotrophic respiration (Rh) from v3 and v5 were 49.9-50.2 (mean 50.1) and 53.3-53.5 (mean 53.4) Pg C yr-1, respectively. Interestingly, SRDBv5’s inclusion of new data from underrepresented regions (e.g., Asia, Africa, South America) thus resulted in overall higher model uncertainty. The SRDBv5 is still biased towards northern latitudes and temperate zones, so we tested that an optimized global distribution of Rs measurements resulted in a global sum of 96.4 ± 21.4 Pg C yr-1 and lower model uncertainty. These results support current global estimates of Rs but highlight spatial inconsistencies that influence model parameterization and interpretation that should be addressed to improve regional- to global-scale predictions.

Stell, Emma↗
DOE OSTI2023

Reduced-Order Models of Static Power Grids based on Spectral Clustering

For large-scale interconnected power systems that cover large geographical areas, certain electrical studies are required so that appropriate decisions ensure system reliability and low cost. For such studies, it is often neither practical nor necessary to model in detail the entire power system, which is increasingly complex due to a more diverse range of grid assets to choose from in both short and long-term planning. The goal of this paper is to present a methodology to reduce the order of large-scale power networks based on spectral graph theory given that current methods for static network reduction are not scalable. A brief analysis of some spectral clustering properties to determine which graph Laplacian matrix should be used and why is included. The analysis shows that the utilization of the normalized graph Laplacian is more advantageous for clustering purposes. Techniques are proposed to approximate cost functions for the aggregated generators. This is done via linear regression. The reduced-order model obtained with the proposed methodology has an accuracy above 94% and solves the scalability issue commonly present in other reduction methods. If the utilization of the reduced-order model is either constrained to load levels above mid-peak demand, or cost functions of aggregated units are approximated via a piecewise quadratic approach, then the error distribution is in the order of 10^-3. .

Baquedano-Aguilar, Mario D.↗
DOE OSTI2021

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Equilibrated Gas and Carbonate Standard‐Derived Dual (Δ 47 and Δ 48 ) Clumped Isotope Values

Abstract Carbonate clumped isotope geochemistry has primarily focused on mass spectrometric determination of m/z 47 CO 2 for geothermometry, but theoretical calculations and recent experiments indicate paired analysis of the m/z 47 ( 13 C 18 O 16 O) and m/z 48 ( 12 C 18 O 18 O) isotopologues (referred to as Δ 47 and Δ 48 ) can be used to study non‐equilibrium isotope fractionations and refine temperature estimates. We utilize 5,448 Δ 47 and 3,400 Δ 48 replicate measurements of carbonate samples and standards, and 183 Δ 47 and 195 Δ 48 replicate measurements of gas standards from 2015 to 2021 from a multi‐year and multi‐instrument data set to constrain Δ 47 and Δ 48 values for 27 samples and standards, including Devils Hole cave calcite, and study equilibrium Δ 47 ‐Δ 48 , Δ 47 ‐temperature, and Δ 48 ‐temperature relationships. We compare results to previously published findings and calculate equilibrium regressions based on data from multiple laboratories. We report acid digestion fractionation factors, Δ* 63‐47 and Δ* 64‐48 , and account for their dependence on the initial clumped isotope values of the mineral.

58 GEOSCIENCES↗
NASA NTRS1990

Constrained optimization using design of experiment surfaces

An algorithm for solving constrained optimization problems is presented. First, design of experiment techniques are used to survey the design space. After evaluating the objective and constraint functions, as specified by Taguchi orthogonal arrays, analytical models of these functions are generated using a least-squares regression analysis. Next, a nonlinear programming package is used to optimize the analytical model. Based on the optimization information, the design space is reduced so as to close in around the minimum, and the entire procedure is repeated until convergence. An important feature of the algorithm is that function gradients are not required; therefore, for problems in which gradients would have to be estimated using finite-differences the number of function evaluations required for the optimization is significantly reduced, when compared with traditional nonlinear programming techniques. In addition, there is no requirement that the gradients must be smooth and continuous.

Bolt, Marvin Vance↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2021

Relative effect of anthropogenic warming and natural climate variability to changes in Compound drought and heatwaves

Compound drought and heatwave (CDHW) events can be influenced by large scale teleconnections and anthropogenic warming, leading to severe socio-economic impacts across various climate regions. Here, the relative influence of six different teleconnection patterns and anthropogenic global warming on the global CDHW occurrences is quantified systematically using the instrumental data period, 1982–2016. The results from the study suggest a substantial increase in the CDHW events (1–5 events per year) across various parts of the globe at the beginning of 21st century (2000–2016). A Bayesian approach is implemented to identify the most vulnerable climate regions based on the degree of susceptibility of heatwaves (DSHW) towards drought. As such, top ten most vulnerable regions are selected based on the DSHW magnitude, and a partial correlation analysis is performed to select the natural and anthropogenic drivers of CDHW in those regions, separately. A logistic regression model is then used to determine significant changes in the odds of CDHW due to changes in the selected drivers that suggest a significantly positive, and multiplicative effect of anthropogenic global warming in the top ten most vulnerable climate regions. Finally, the same logistic regression model, integrated with an analytical framework, is applied to determine the relative influence of anthropogenic global warming on the changes in odds of CDHW for the future, 1.5 °C and 2 °C warming limits. Finally, the results suggest that relative to the 2 °C global warming, constraining to the 1.5 °C global warming limit may conduce about 17-fold reduction in the odds of CDHW in the most vulnerable climate region, East Asia, 5–8-fold reduction in Western North America, Northern Australia, Central North America, Central Europe, South Asia, and the Mediterranean region, and 3–4-fold reduction in Northeastern Brazil, Eastern North America, and West Asia.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Temporal variabilities of soil carbon dioxide fluxes from cornfield impacted by temperature and precipitation changes through high-frequent measurement and DAYCENT modelling

Soil carbon dioxide (CO 2 ) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO 2 fluxes (R s ) with soil temperature (T s ) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on R s using the measured high-frequency data. R s trend was tested by Mann–Kendall and Sen Estimator. Predicted R s s under different climate scenarios were compared using Parallel-line Analysis. Herein, the findings indicated that daily R s exponentially increased with T s constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between R s and T s , but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, T s × θ) significantly impacted R s with linear regression R s 0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in R s s and relationships among R s and T s and θ in different years. Predicted yearly R s s, root R s s and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the R s s significantly increased as temperature rose by 1°C or higher. Predicted yearly R s s, root R s s and yield reduced with precipitation scenario increase, and the root R s s and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly R s from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield R s and evaluating sensitivities of different cropland R s s to temperature changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

We report the design/discovery of new materials is highly nontrivial owing to the near-infinite possibilities of material candidates and multiple required property/performance objectives. Thus, machine learning tools are now commonly employed to virtually screen material candidates with desired properties by learning a theoretical mapping from material-to-property space, referred to as the forward problem. However, this approach is inefficient and severely constrained by the candidates that the human imagination can conceive. Thus, in this work on polymers, we tackle the materials discovery challenge by solving the inverse problem: directly generating candidates that satisfy desired property/performance objectives. We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions: (1) high temperatures, (2) high electric field, and (3) high temperature and high electric field, useful for critical structural, electrical, and energy storage applications. This approach to learn from and augment) human ingenuity is general and can be extended to discover polymers with other targeted properties and performance measures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Discrete generative diffusion models without stochastic differential equations: A tensor network approach

Diffusion models (DMs) are a class of generative machine learning methods that sample a target distribution by transforming samples of a trivial (often Gaussian) distribution using a learned stochastic differential equation. In standard DMs, this is done by learning a “score function” that reverses the effect of adding diffusive noise to the distribution of interest. Here we consider the generalisation of DMs to lattice systems with discrete degrees of freedom, and where noise is added via Markov chain jump dynamics. We show how to use tensor networks (TNs) to efficiently define and sample such “discrete diffusion models” (DDMs) without explicitly having to solve a stochastic differential equation. We show the following: (i) by parametrising the data and evolution operators as TNs, the denoising dynamics can be represented exactly; (ii) the auto-regressive nature of TNs allows to generate samples efficiently and without bias; (iii) for sampling Boltzmann-like distributions, TNs allow to construct an efficient learning scheme that integrates well with Monte Carlo. We illustrate this approach to study the equilibrium of two models with non-trivial thermodynamics, the d = 1 constrained Fredkin chain and the d = 2 Ising model. Published by the American Physical Society 2025

Causer, Luke (ORCID:0000000194243473)↗
DOE OSTI2025

Extratropical Cloud Feedback Constrained by Cloud Sources and Sinks in Cyclones

Constraining cloud feedback in global climate models (GCMs) using observations is important for establishing accurate predictions of future climate. Uncertainty in shortwave cloud feedback (SW FB ) dominates uncertainty in total cloud feedback. Recent studies show a shift toward more positive extratropical SW FB in the latest generations of GCMs leading to the emergence of very high equilibrium climate sensitivity (ECS). In this study, we use precipitation efficiency and albedo susceptibility to constrain liquid water path (LWP) response to warming and SW FB in the Southern Ocean (SO; 50°–80°S). We analyze precipitation in extratropical cyclones (ECs) to learn about extratropical condensed water sink processes, combined with observations of clouds and moisture convergence, and use the analysis to better understand and constrain SW FB . We utilize a perturbed parameter ensemble (PPE) hosted in the Community Atmosphere Model, version 6 (CAM6), to provide a constraint on SW FB based on observations from Clouds and the Earth’s Radiant Energy System (CERES) and Multisensor Advanced Climatology of LWP (MAC-LWP). We apply Gaussian process regression to emulate the model response to all parameters perturbed in the PPE. Confronting the emulator output with observations provides a new estimated response of Earth to global warming. Furthermore, our new estimates of SO LWP reduce the PPE range by 66%–72%, which results in a shortwave cloud radiative effect estimated range that is 27%–34% less than the PPE range. Observations suggest a more positive SO SW FB than the Community Earth System Model, version 2 (CESM2), and consequently do not reject the high climate sensitivity GCMs emerging from the Coupled Model Intercomparison Project phase 6 (CMIP6).

Atmosphere↗
NASA NTRS2016

Hybrid Propulsion In-Situ Resource Utilization Test Facility Results for Performance Characterization

Hybrid propulsion presents a promising alternative to conventional systems for in-space propulsion applications using In-Situ Propellant Production (ISPP). A hybrid propulsion test facility has been built at the NASA Jet Propulsion Laboratory (JPL) with the capability of evaluating ISPP oxidizers (gaseous mixtures of O2 and CO2) and modifying fuel type and chamber geometry. This work is the result of a continuing e ort at JPL to characterize the burn characteristics and performance of different propellant combinations and motor configurations in order to re ne and support preliminary designs. Twelve tests using gaseous oxygen and two paraffin based fuels (Black Paraffin and SP1X) from the 2015 campaign are presented. Different reduction techniques are compared in an e ort to determine the uncertainty associated with deriving a regression rate law and evaluating c* efficiency with the collected data.Hybrid motors are mechanically and chemically simple propulsion systems that have the potential forlong-term storability with low gross mass. The oxidizer (such as gaseous O2) can be produced in-situ,signicantly reducing the landed mass. The solid, inert fuel is stored in the combustion chamber in its most dense state, further reducing the total structural mass and volume. Considerable eorts have been focusedon ISPP for Environmental Control and Life Support Systems (ECLSS) and human exploration of Mars.At optimum oxidizer to fuel ratio (O/F), about 70% of the total propellant mass could be produced in-situ.Paran based fuels exhibit a fast regression rate behavior due to the formation of a thin liquid layer on thefuel surface.1 A high burn rate is necessary in order to reach a medium to high thrust range. In order toachieve an O/F ratio close to optimum performance (between 2.1 and 2.7 for the O2/Paran combination),the fuel mass ow needs to be suciently large compared to the oxidizer ow. The slow regression rateof classical hybrid rocket fuels leads to long fuel grain designs or complicated multi-port structures. Singleport, high regression rate fuels have the potential to reduce residual propellant mass and have a grainlength to diameter ratio (L/D) more favorable for packaging. However, hybrid motors remain at a fairly lowTechnology Readiness Level (TRL), and solutions to accurately measure and increase performance are stilllacking, especially for small-scale motors that are constrained to tight packaging.A total of 12 hotres were performed and analyzed in this campaign. All of these tests were conductedat pressures above the critical pressure of paran wax, approximately 6.7 bar (97 psi).2 Three dierentmotor congurations were explored while conserving a single cylindrical port fuel grain. Additionally, sincethrottling is a crucial advantage of hybrid rocket propulsion systems, low oxidizer mass ows (60 - 100 g/s)and uxes (5 - 30 g/cm2/s) were investigated to determine the performance impact in this regime.

Mechentel, F. S.↗
NASA NTRS1989

Fluctuation history of Great Salt Lake, Utah, during the last 13,000 years, part 2

Great Salt Lake level fluctuations from 13,000 yr B.P. to the present were interpreted by examination of shoreline geomorphic features, shoreline deposits, archeologic sites, isotopic data, and palynologic data. After the conclusion of the Bonneville paleolake cycle, between 13,000 and 12,000 yr B.P. the lake regressed to levels low enough to deposit a littoral oxidized red bed stratum and a pelagic Glauber's salt layer. A late Pleistocene lake cycle occurred between 12,000 and 10,000 yr B.P. depositing several beaches, the highest reaching an altitude of about 4250 ft (1295.3 m). The lake regressed after 10,000 yr B.P., only to rise to 4230 ft (1289.2 m) between 9700 and 9400 yr B.P. and then gradually lower at least 15 ft (4.5 m) or more. Lake levels fluctuated between 4212 and 4180 ft (1284 and 1274 m) for the next 4000 years. A late Holocene lake cycle, constrained by radiocarbon ages between 3440 and 1400 yr B.P., is reported at a highest static level of 4221 ft (1286.5 m). After a lake level drop to altitudes ranging between 4210 and 4205 ft (1283.2 and 1281.6 m), a 4217 ft (1285.7 m) level was reached after 400 yr B.P. This level lowered to 4214 ft (1284.4 m) in the mid to late 1700 s A.D. The lake levels have since stabilized aroung a 4200 ft (1280 m) mean.

Murchison, Stuart B.↗