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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Modern Aerocapture Guidance to Enable Reduced-Lift Vehicles at Neptune

Aerocapture is covered extensively in the literature as means of achieving orbital insertion with dramatic mass-saving results compared to fully-propulsive systems. One of the primary obstacles facing aerocapture is the inherent uncertainty associated with passing through a planet’s upper atmosphere. In-flight dispersions due to delivery errors, environment variables, and aerodynamic performance impose a large flight envelope. System studies for aerocapture often select high lift-to-drag ratios to compensate for these uncertainties. However, modern predictor-corrector guidance strategies have shown promise in recent years to provide robust control schemes in-situ. These algorithms do not rely on a pre-calculated reference trajectory and instead employ a numerical optimizer to continuously solve nonlinear equations of motion each guidance cycle. Numerical predictor-corrector strategies may provide considerable accuracy over heritage guidance schemes. The goal of this study is reproduce a landmark study of Neptune aerocapture and apply modern guidance to illustrate relative performance improvements and cost-saving potential. Capture constraints based on the theoretical corridor width are considered. Results indicate that heritage vehicles with moderate lift-to-drag ratios, lower than previous studies have indicated, may prove viable for aerocapture at Neptune.

Heidrich, C. R.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-33626-DND

NASA is conducting a series of computational experiments to quantify atmospheric effects on low noise sonic booms. In the current study, simulated cruise nearfield pressure data from NASA?s X-59 Quiet Supersonic Technology aircraft was propagated from the aircraft to the ground at four cardinal headings through five years of realistic atmospheric profiles at 30 locations across the eastern USA. Statistical design of experiments was used to select the locations where primary sonic thump carpet widths (CW) and metric levels at the ground were computed. Atmospheric profiles were taken from the Climate Forecast System Reanalysis database, which contains reanalyzed atmospheric profiles four times daily. Decision tree analyses were performed to determine relative importance of predictors for CW and metric levels. Predictors included were latitude, longitude, date, time of day, season, climate, aircraft heading, and ground elevation. Results of this study indicate the propagation resolution needed to adequately characterize the distributions of CW and ground metric data, i.e., necessary separation distance between propagation locations and total number of atmospheres through which to propagate. This resolution will be used for a follow-on study of simulated X-59 carpets across the entire US mainland.

Will Doebler↗
NASA NTRS2020

Investigation of Direct Force Control For Aerocapture at Neptune

In this work, a direct force control numerical predictor-corrector guidance architecture is developed to enable Neptune aerocapture using blunt body aeroshells. A linear aerodynamics model is formulated for a Mars Science Laboratory-derived aeroshell. The application of optimal control theory shows that the∆V-minimizing angle of attack and side-slip angle control laws are bang-bang. A closed-loop numerical predictor-corrector direct force control guidance algorithm is developed and numerically simulated using the Program to Optimize Simulated Trajectories II. A series of Monte Carlo simulations are conducted to assess the guidance robustness to uncertainties in vehicle aerodynamics,atmospheric density, and entry state. For the reference set of uncertainties, the direct force control vehicle achieves 99.7% successful science orbit insertion within a 330 m/s total∆V budget for periapsis raise, apoapsis, inclination,and ascending node corrections. Improved atmospheric knowledge and delivery state accuracy are shown to improve the success to 100% and reduce the∆V to 230 m/s. Direct force control is demonstrated to be an enabling technology for blunt body aerocapture at Neptune while providing comparable performance to existing slender body vehicles studied in literature

aerocapture↗
NASA NTRSDate not supplied

Algorithmic Detection of Elemental Biosignatures

Machine learning models that classify a sample as indicative or non-indicative of life could play an important role in life-detection missions. Their predictions result from agnostic algorithms and thereby add redundancy to judgements resulting from human expertise. Additionally, their important features can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental abundance was chosen as a starting set of features due to its availability in diverse sample types, which are needed to train a generalizable model. A standardized dataset was collected, including 35 non-indicative, e.g., lunar rock, basalt; 19 indicative mixed, e.g., seawater, agricultural soil; 46 indicative non-alive, e.g., coal, chalk; and 10 indicative alive, e.g., biofilm, bacteria. This dataset could be valuable for complementary biosignature research. The samples were standardized to the same limit of detection of a simulated mission scenario. Four classification models were used: k-nearest neighbors (KNN), logistic regression (LR), linear support vector machines (SVM), and Gaussian naïve Bayes (GNB). To obtain feature importances, KNN was run on three principal components of the training data and LR and SVM were run with L1 and L2 regularization. The performances and feature importances of the six model variants on 40:60 train to validation ratios were assessed with Monte Carlo simulations. ROC AUC and mean accuracy scores ranged between 82% - 94%, with sensitivity greater than specificity. For indicative of life predictors, all models had C and Ca as strong and Cl as medium; a majority of models had N, K, and P as medium. For non-indicative of life predictors, all models had Si as strong, and a majority of models had Mg, Al, and Ti as medium. Varied elements were Fe (slightly non-indicative), H (slightly indicative), O (widely varied), Na, Mn, and S. These results serve as a proof of concept and suggest important elemental signals beyond merely the CHNOPS of Earth-based life.

Algorithmic↗
NASA NTRSDate not supplied

Climate change reshapes the drivers of false spring risk across European trees

Temperate and boreal forests are shaped by late spring freezing events after budburst, which are also known as false springs. Research has generated conflicting results on whether or not these events will change with climate change, potentially because---to date---no study has compared the myriad climatic and geographic factors that contribute to a plant's risk of a false spring. We assessed and compared the strength of the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six temperate, decidious tree species across 11,648 sites in Central Europe and how these predictors shifted with climate change. Across species before recent warming, mean spring temperature and distance from the coast were the strongest predictors, with higher mean spring temperatures associated with decreased risk in false springs (–7.64% per 2°C increase) and sites further from the coast experiencing an increased risk (5.32% per 150km from the coast). Elevation (2.23% per 200m increase in elevation) and NAO index (1.91% per 0.3 increase) also increased false spring risk. With climate change, elevation and distance from coast---i.e., the geographic factors---remain relatively stable, while climatic factors shifted in magnitude for mean spring temperature (down to -2.84% in risk per 2°C), and in direction, with positive NAO phases leading to lower risk (-9.15% per 0.3). The residual effects of climate change---unexplained by the climatic and geographic factors already included in the model---magnified the species-level variation in risk, with risk increasing among early-leafout species (i.e., Aesculus hippocastanum, Alnus glutinosa and Betula pendula) but a decline or no change in risk among late-leafout species (i.e., Fagus sylvatica, Fraxinus excelsior and Quercus robur). Our results show that climate change has reshaped the major drivers of false spring risk and highlight how considering multiple factors can yield a better understanding of the complexities of climate change.

Climate change↗
NASA NTRS2020

A Regime-Oriented Approach to Observationally Constraining Extratropical Shortwave Cloud Feedbacks

The extratropical shortwave (SW) cloud feedback is primarily due to increases in extratropical liquid cloud extent and optical depth. Here, we examine the response of extratropical (35°-75°) marine cloud liquid water path (LWP) to a uniform 4 K increase in sea surface temperature (SST) in global climate models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) and variants of the HadGEM3-GC3.1 GCM. Compositing is used to partition data into periods inside and out of cyclones. The response of extratropical LWP to a uniform SST increase and associated atmospheric response varies substantially among GCMs, but the sensitivity of LWP to cloud controlling factors (CCFs) is qualitatively similar. When all other predictors are held constant, increasing moisture flux drives an increase in LWP. Increasing SST, holding all other predictors fixed, leads to a decrease in LWP. The combinations of these changes lead to LWP, and by extension reflected SW, increasing with warming in both hemispheres. Observations predict an increase in reflected SW over oceans of 0.8 W/sq.m to 1.6 W/sq.m per K SST increase (35°N-75°N) and 1.2 W/sq.m to 1.9 W/sq.m per K SST increase (35°S- 75°S). This increase in reflected SW is mainly due to increased moisture convergence into cyclones because of increasing available moisture. The efficiency at which converging moisture is converted into precipitation determines the amount of liquid cloud. Thus, cyclone precipitation processes are critical to constraining extratropical cloud feedbacks.

extratropical shortwave (SW) cloud feedback↗
NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗
NASA NTRS2021

Colorado Front Range Disasters: Understanding the Impact of Forest Management on the Cameron Peak and CalWood Fire

Along the Colorado Front Range, forest management has gained significant attention due to uncharacteristically large fires that burned late in 2020. The Cameron Peak Fire (largest in Colorado recorded history) and the CalWood Fire collectively burned an estimated 219,019 acres from August through December of 2020. Project partners at the Coalition for the Poudre River Watershed, Colorado State Forest Service, Ben Delatour Scout Ranch, The Nature Conservancy, Colorado Forest Restoration Institute, and Colorado State University were interested in understanding the effectiveness of previous forest treatments in reducing burn severity within the Cameron Peak Fire and the CalWood Fire. We first collated a forest treatment dataset from pre-existing datasets by reclassifying over 29,000 treatments, which occurred across the Northern Colorado Front Range between 1970-2020. Secondly, we mapped three burn severity indices using Landsat 8 OLI and Sentinel-2 MSI Earth observations and compared them to soil burn severity field data. Thirdly, a total of 35 topographic, disturbance, forest structure, and treatment predictor variables were generated across the fires. Finally, we assessed relationships between these predictor variables and burn severity using the random forest algorithm. Model results indicate that the primary drivers of burn severity were elevation and distance to treatment edge for the Cameron Peak Fire and fire area and forest canopy cover for the CalWood Fire. Further analysis of these variables paired with field data is necessary to understand the relationship between burn severity and treatments to guide future restoration efforts, improve forest resiliency, and mitigate fire risks.

Neal Swayze↗
NASA NTRSDate not supplied

Planning Satellite Swarm Measurements for Climate Models: Comparing Dynamic Constraint Processing and MILP Methods

We present D-SHIELD, a challenging climate science application to plan coordinated measurements (observations) for a constellation of satellites, each containing two different sensors, each with 61 pointing angle options. The L-band and P-band radar sensors collect data fed into a soil moisture model which tracks and predicts soil moisture across 1.67 million Ground Positions (GP). Soil moisture is an important predictor of wildfires, and then a predictor of floods, landslides and debris flow after a fire. Each measurement covers multiple GP due to the sensor footprint. Each GP has a "model error" which represents the uncertainty of the the soil moisture state prediction. Model error changes at different rates for each GP as the time since last observation increases and after significant events like rain. The planner's goal is to select measurements which maximize soil moisture model improvement (reduce model uncertainty). This problem is combinatorically explosive, involving many degrees of freedom for planner choices. Good domain heuristics can find solutions within a reasonable time for our application needs but cannot be proven optimal. In this paper we compare two different planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.

Rich Levinson↗
NASA NTRS2020

Neighbors on the Red Planet: Mars Science Laboratory Relay Coordination Post InSight Arrival

The Mars Science Laboratory (MSL) strategic communications planning toolset selects orbiter relay opportunities that the rover will use for transmitting data back to Earth. The toolset was extensively reworked in preparation for the Interior Exploration using Seismic Investigations, Geodesy and Heat Transport's (InSight) arrival at Mars in November 2018 and the regular use of Mars Atmosphere and Volatile Evolution (MAVEN) and Trace Gas Orbiter (TGO) orbiters as relay assets in addition to Mars Reconnaissance Orbiter (MRO) and 2001 Mars Odyssey (ODY). Overflight selection criteria was automated in the new toolset to take into account overflight deconfliction and down-selection as well as tactical timeline planning impacts and total data return. This was done while remaining flexible and configurable for changing mission priorities. As the Curiosity rover ages, the MSL planning team must overcome issues such as reduced budgets, memory bank anomalies, and reduced power availability. These are some examples of factors that affect the strategic communications planning toolset. In addition to adapting to evolving internal mission needs, the toolset must also be flexible to changes in the relay planning interface with other landers. The concept of shared relay or “split passes” was introduced when InSight began operating on Mars just 600 kilometers away and at the same longitude as MSL. This proximity meant that orbiters could now communicate with two landers during the same relay session. Over time, this new operational use-case became common practice as InSight and MSL settled into relay planning negotiations. Today, many TGO relay sessions are shared between MSL and InSight. As more orbiters pursue integrating this capability into nominal relay operations, and more landers arrive on Mars, the strategic communications process and toolsets increases in complexity. Development of a single tool that schedules relay sessions for all orbiters and landers simultaneously may become necessary as the number of Martian spacecraft increases. Examples of how toolset selection criteria and capabilities have helped or hindered MSL planning will be presented. Ongoing improvements to MSL toolsets and processes, as well as to shared relay tools such as Mars Relay Operations Service (MaROS) and General Telecom Predictor (GTP) are also discussed in this paper. Examples of how toolset selection criteria and capabilities have helped or hindered MSL planning will be presented. Ongoing improvements to MSL toolsets and processes, as well as to shared relay tools such as Mars Relay Operations Service (MaROS) and General Telecom Predictor (GTP) are also discussed in this paper.

Vandermey, Nancy↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRSDate not supplied

Performance Analysis of SmallSat Aerocapture at Venus

Aerocapture is a method of spacecraft orbital insertion that has the potential to provide mass savings as compared to aerobraking. In particular, aerocapture at Venus is desirable due to high atmospheric density and potential for science return. While promising, accurate orbital insertion by means of aerocapture requires an active guidance, navigation, and control architecture. This paper documents the initial assessment of the performance of two guidance algorithms, a generalized numerical predictor corrector and the Fully-Numeric Predictor- corrector for Aerocapture Guidance, applied to smallsat aerocapture at Venus using two different simulation environments with equivalent simulation inputs. Nominal trajectory performance is assessed. In addition, Monte Carlo analysis is performed to compare each guidance algorithm’s performance under uncertainty.

aerocapture↗
NASA NTRSDate not supplied

Performance Analysis of SmallSat Aerocapture at Venus

Aerocapture is a method of spacecraft orbital insertion that has the potential to provide mass savings as compared to aerobraking. In particular, aerocapture at Venus is desirable due to high atmospheric density and potential for science return. While promising, accurate orbital insertion by means of aerocapture requires an active guidance, navigation, and control architecture. This paper documents the initial assessment of the performance of two guidance algorithms, a generalized numerical predictor corrector and the Fully-Numeric Predictor-corrector for Aerocapture Guidance, applied to smallsat aerocapture at Venus using two different simulation environments with equivalent simulation inputs. Nominal trajectory performance is assessed. In addition, Monte Carlo analysis is performed to compare each guidance algorithm’s performance under uncertainty.

aerocapture↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
DOE OSTI2024

Methane fluxes in tidal marshes of the conterminous United States

Abstract Methane (CH 4 ) is a potent greenhouse gas (GHG) with atmospheric concentrations that have nearly tripled since pre‐industrial times. Wetlands account for a large share of global CH 4 emissions, yet the magnitude and factors controlling CH 4 fluxes in tidal wetlands remain uncertain. We synthesized CH 4 flux data from 100 chamber and 9 eddy covariance (EC) sites across tidal marshes in the conterminous United States to assess controlling factors and improve predictions of CH 4 emissions. This effort included creating an open‐source database of chamber‐based GHG fluxes ( https://doi.org/10.25573/serc.14227085 ). Annual fluxes across chamber and EC sites averaged 26 ± 53 g CH 4 m −2 year −1 , with a median of 3.9 g CH 4 m −2 year −1 , and only 25% of sites exceeding 18 g CH 4 m −2 year −1 . The highest fluxes were observed at fresh‐oligohaline sites with daily maximum temperature normals (MATmax) above 25.6°C. These were followed by frequently inundated low and mid‐fresh‐oligohaline marshes with MATmax ≤25.6°C, and mesohaline sites with MATmax >19°C. Quantile regressions of paired chamber CH 4 flux and porewater biogeochemistry revealed that the 90th percentile of fluxes fell below 5 ± 3 nmol m −2 s −1 at sulfate concentrations >4.7 ± 0.6 mM, porewater salinity >21 ± 2 psu, or surface water salinity >15 ± 3 psu. Across sites, salinity was the dominant predictor of annual CH 4 fluxes, while within sites, temperature, gross primary productivity (GPP), and tidal height controlled variability at diel and seasonal scales. At the diel scale, GPP preceded temperature in importance for predicting CH 4 flux changes, while the opposite was observed at the seasonal scale. Water levels influenced the timing and pathway of diel CH 4 fluxes, with pulsed releases of stored CH 4 at low to rising tide. This study provides data and methods to improve tidal marsh CH 4 emission estimates, support blue carbon assessments, and refine national and global GHG inventories.

54 ENVIRONMENTAL SCIENCES↗