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NASA NTRS2016

Variation in Nest Temperatures of the American Alligator Found on the Kennedy Space Center Merritt Island National Wildlife Refuge

Information on nest temperatures of the American Alligator (Alligator mississippiensis) constructed in the wild is limited. Nesting temperatures during a critical thermal sensitive period determine the sex of alligators and are therefore critical in establishing the sex biases in recruitment efforts of alligators within a given community. Nest components, varying environmental conditions, and global warming could have a significant impact on nest temperatures, thus affecting future generations of a given population. One hundred and seventy four programmable thermistors were inserted into fifty eight nests from 2010 through 2015 nesting cycles. Three thermistors were placed inside each nest cavity (one on top of the eggs, one in the middle of the eggs, and one at the bottom of the clutch of the eggs) to collect temperature profiles in the incubation chamber and throughout the entire incubation period. One thermistor was also placed near or above these nests to obtain an ambient air temperature profile. Once retrieved, data from these thermistors were downloaded to examine temperature profiles throughout the incubation period as well as during the period of sexual determination. These data would help establish survival rates related to nest temperature and predict sex ratio of recruited neonates at the Kennedy Space Center. Over three million temperatures have been recorded since 2010 for the alligator thermistor study giving us insight to the recruitment efforts found here. Precipitation was the largest influence on nesting temperatures outside of daily photoperiod, with immediate changes of up to eight degrees Celsius.

Alligator↗

NASA NTRS1944

NACA Investigation of a Jet-Propulsion System Applicable to Flight

Following a brief history of the NACA investigation of jet-propulsion, a discussion is given of the general investigation and analyses leading to the construction of the jet-propulsion ground-test mock-up. The results of burning experiments and of test measurements designed to allow quantitative flight-performance predictions of the system are presented and correlated with calculations. These calculations are then used to determine the performance of the system on the ground and in the air at various speeds and altitudes under various burning conditions. The application of the system to an experimental airplane is described and some performance predictions for this airplane are made. It was found that the main fire could be restricted to an intense, small, and short annular blue flame burning steadily and under control in the intended combustion space. With these readily obtainable combustion conditions, the combustion chamber the nozzle walls and the surrounding structure could be maintained at normal temperatures. The system investigated was found to be capable of burning one-half the intake air up the fuel rates of 3 pounds per second. Calculations were shown to agree well with experiment. It was concluded that the basic features of the jet-propulsion system investigation in the ground-test mock-up were sufficiently developed to be considered applicable to flight installation. Calculations indicated that an airplane utilizing this jet-propulsion system would have unusual capabilities in the high-speed range above the speeds of conventional aircraft and would, in addition, have moderately long cruising ranges if only the engine were used.

DUCTS↗

NASA NTRS2012

Receivers Gather Data for Climate, Weather Prediction

Signals from global positioning system (GPS) satellites are now being used for more than just location and navigation information. By looking at the radio waves from GPS satellites, a technology developed at NASA s Jet Propulsion Laboratory (JPL) not only precisely calculates its position, but can also use a technique known as radio occultation to help scientists study the Earth s atmosphere and gravity field to improve weather forecasts, monitor climate change, and enhance space weather research. The University Corporation for Atmospheric Research (UCAR), a nonprofit group of universities in Boulder, Colorado, compares radio occultation to the appearance of a pencil when viewed though a glass of water. The water molecules change the path of visible light waves so that the pencil appears bent, just like molecules in the air bend GPS radio signals as they pass through (or are occulted by) the atmosphere. Through measurements of the amount of bending in the signals, scientists can construct detailed images of the ionosphere (the energetic upper part of the atmosphere) and also gather information about atmospheric density, pressure, temperature, and moisture. Once collected, this data can be input into weather forecasting and climate models for weather prediction and climate studies. Traditionally, such information is obtained through the use of weather balloons. In 1998, JPL started developing a new class of GPS space science receivers, called Black Jack, that could take precise measurements of how GPS signals are distorted or delayed along their way to the receiver. By 2006, the first demonstration of a GPS radio occultation constellation was launched through a collaboration among Taiwan s National Science Council and National Space Organization, the U.S. National Science Foundation, NASA, the National Oceanic and Atmospheric Administration (NOAA), and other Federal entities. Called the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), JPL was responsible for designing COSMIC s primary instrument - based on its revolutionary Black Jack receiver.

Source record↗

NASA NTRS2000

Modeling Heat-Transfer in Animal Habitats in the Shuttle Orbiter Middeck

A mathematical model has been developed to evaluate the heat transfer characteristics of an Animal Enclosure Module (AEM) in the microgravity environment. The AEM is a spaceflight habitat that provides life support for up to six rodents in the Space Shuttle Middeck. Currently, temperatures within the AEM are recorded in real time using a solid state data recorder; however, the data are only available for analysis post-flight. This temperature information is useful for characterizing the thermal environment of the AEM for researchers, but is unavailable during flight operations. Because animal health in microgravity is directly linked to the thermal environment, the ability to predict internal AEM temperatures is extremely useful to life science researchers. NASA flight crews typically carry hand-held temperature measurement devices which allow them to provide ground researchers with near real time readings of AEM inlet temperature; however, higher priority operations limit the frequency at which these measurements can be made and subsequently downlinked. The mathematical model developed allows users to predict internal cage volume temperatures based on knowledge of the ambient air temperature entering the AEM air intake ports. Additionally, an average convective heat transfer coefficient for the AEM has been determined to provide engineers with the requisite information to facilitate future design improvements and product upgrades. The model has been validated using empirical data from a series of three Space Shuttle missions.

Eodice, Michael T.↗

NASA NTRS2009

Sensitivity of Short-Term Weather Forecasts to Assimilated AIRS Data: Implications for NPOESS Applications

The Atmospheric Infrared Sounder (AIRS) is acting as a heritage and risk reduction instrument for the Cross-track lnfrared Sounder (CrIS) to be flown aboard the NPP and NPOESS satellites. The hyperspectral nature of AIRS and CrIS provides high-quality soundings that, along with their asynoptic observation time over North America, make them attractive sources to fill the spatial and temporal data voids in upper air temperature and moisture measurements for use in data assimilation and numerical weather prediction. Observations from AlRS can be assimilated either as direct radiances or retrieved thermodynamic profiles, and the Short-Term Prediction Research and Transition (SPORT) Center at NASA's Marshall Space Flight Center has used both data types to improve short-term (0-48h), regional forecasts. The purpose of this paper is to share SPORT'S experiences using AlRS radiances and retrieved profiles in regional data assimilation activities by showing that proper handling of issues-including cloud contamination and land emissivity characterization-are necessary to produce optimal analyses and forecasts.

Zavodsky, Bradley↗

DOE OSTI2023

Envisioning urban environments resilient to vector-borne diseases: a protocol to study dengue in Vietnam

Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1992

Thermally-included convection in confined air

This study includes the development and design of a space experiment for the investigation of the thermally-induced convection heat transfer process in an enclosure containing air. Various features of the design including the optical method used in the fluid temperature measurement are presented and discussed. A series of ground experiments were carried out in support of the in-space experiment design. Their results are discussed briefly and compared with numerical predictions.

Crocker, D. S.↗

NASA NTRS2020

Attribution of Chemistry-Climate Model Initiative (CCMI) Ozone Radiative Flux Bias from Satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6-μm ozone band is a fundamental quantity for understanding chemistry-climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6-μm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Inter-comparison Project (ACCMIP) simulations. The principal culprits are tropical mid and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mWm-2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mWm-2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mWm-2). We found that AM3 and CMAM have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to difference processes. Overall, the multi-model ensemble mean bias is –133±98 mWm-2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation.

Aura Tropospheric Emission Spectrometer (TES) meas↗

NASA NTRS2009

The GLACE-2 Experiment

A major motivation for the study of the coupled land-atmosphere system is the idea that soil moisture anomalies may affect future meteorological variables through their effects on future surface energy and water budgets. If true, the accurate initialization of soil moisture in a subseasonal or seasonal forecast system may improve forecast skill, making the forecast products more valuable to society. The Global Land-Atmosphere Coupling Experiment (GLACE-2) project is examining, with a wide variety of models, the degree to which subseasonal (out to two months) precipitation and air temperature forecasts improve through the realistic initialization of soil moisture. For the first time ever, a global consensus should emerge regarding the value of land initialization for forecasts, perhaps motivating national forecast centers to make full use of land moisture initialization in their operations. Participants in GLACE-2 perform two series of forecasts, each consisting of 100 2-month forecast ensembles (10 members per ensemble) covering ten boreal spring and summer start-dates in each of the years 1986-1995. Series 1 utilizes realistic land surface state initialization, provided through a decadal offline simulation using realistic meteorological forcing, as provided by the Global Soil Wetness Project - Phase 2 (GSWP-2), a research activity of the Global Land-Atmosphere System Study (GLASS) of GEWEX. Series 2 is identical to Series 1 in every way except for the fact that it does not benefit from realistic land state initialization. Through the comparison of Series 1 and 2, we isolate the impact of land initialization on the forecasts. Optional extensions to these base runs include forecasts covering additional years, using alternative meteorological forcing for the land initialization. To date, GLACE-2 has garnered participation from eleven modeling groups, covering 13 atmospheric models. Analysis of available results is already well underway, both at the individual modeling institutions and at the NASA Goddard Space Flight Center, which is coordinating the project. Analysis focuses on two elements of the forecast problem: (i) the quantification of model-specific "predictability" (i.e., the degree to which simulated atmospheric chaos will foil a forecast, even under the assumption of"perfect" model physics, initialization data, and validation data) and its decay with time; and (ii) the quantification of forecast skill, determined through a comparison of predicted precipitation and air temperature against observations. Indeed, the specific contribution of land initialization to both these elements is isolated through a comparison of the Series 1 and 2 forecasts. We examine the two elements at four different forecast leads: 1-15, 16-30, 31-45, and 46-60 days. Statistics-based approaches for enhancing skill (essentially using observational statistics to reduce the impact of the models' climatic biases) are also tested. In the present talk, we provide an update of progress in GLACE-2, featuring quantifications of predictability and forecast skill for a number of the participating models and providing a "first look" at the desired consensus view of land impacts on subseasonal forecasts.

Koster, Randal↗

NASA NTRS2018

Multi-Scale Modeling of Low-Density Carbon-Phenolic Ablators

Protecting a spacecraft during atmospheric entry is one of highest risk factors that needs to be mitigated during design of a space exploration mission. At entry speeds from space, air turns into high-temperature plasma, and spacecraft Thermal Protection Systems (TPS) are needed to protect the vehicle payload. Modern successful material architectures of spacecraft shields use a porous carbon fiber substrate impregnated with phenolic as an ablator material. In the lecture, efforts to build a Predictive Material Modeling framework for porous ablators from micro-scale to macro-scale will be presented. Several numerical methods and techniques will be summarized that use voxelized images to compute geometrical properties of the porous substrate. These computed properties include porosity, specific surface area and tortuosity that are otherwise indirectly measured through experimental techniques. Direct simulation Monte Carlo (DSMC), a particle-based method for approximating the Boltzmann equation, is used to compute the permeability coefficient of the porous substrate based on its digitized representation. The method computes the flow within the microstructure, where the size of the pores may approach the mean-free-path of the flow. Finally, a high-fidelity model implemented in PATO (Porous-material Analysis Toolbox) is discussed, and some examples of ablative material response are presented, including for the first time 3D simulations of the full tiled heat shield for the Mars Science Laboratory (MSL) capsule.

Ablators↗

NASA NTRS2010

NASA's Advancements in Space-Based Spectrometry Lead to Improvements in Weather Prediction and Understanding of Climate Processes

AIRS (Atmospheric Infra-Red Sounder), was launched, in conjunction with AMSU-A (Advanced Microwave Sounding Unit-A) on the NASA polar orbiting research satellite EOS (Earth Observing System) Aqua satellite in May 2002 as a next generation atmospheric sounding system. Atmospheric sounders provide information primarily about the vertical distribution of atmospheric temperature and water vapor distribution. This is achieved by measuring outgoing radiation in discrete channels (spectral intervals) which are sensitive primarily to variations of these geophysical parameters. The primary objectives of AIRS/AMSU were to utilize such information in order to improve the skill of numerical weather prediction as well as to measure climate variability and trends. AIRS is a multi-detector array grating spectrometer with 2378 channels covering the spectral range 650/cm (15 microns) to 2660/cm (3.6 microns) with a resolving power (i/a i) of roughly 1200 where a i is the spectral channel bandpass. Atmospheric temperature profile can be determined from channel observations taken within the 15 micron (the long-wave CO2 absorption band) and within the 4.2 micron (the short-wave CO2 absorption band). Radiances in these (and all other) spectral intervals in the infrared are also sensitive to the presence of clouds in the instrument?s field of view (FOV), which are present about 95% of the time. AIRS was designed so as to allow for the ability to produce accurate Quality Controlled atmospheric soundings under most cloud conditions. This was achieved by having 1) extremely low channel noise values in the shortwave portion of the spectrum and 2) a very flat spatial response function within a channel?s FOV. IASI, the high spectral resolution IR interferometer flying on the European METOP satellite, does not contain either of these important characteristics. The AIRS instrument was also designed to be extremely stabile with regard to its spectral radiometric characteristics, which is critical with regard to the ability to measure accurate long term trends.

Susskind, Joel↗

DOE OSTI2021

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (R eco ). However, accurate estimation of ET and R eco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and R eco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and R eco variables, as well as providing reliable estimation of ET and R eco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and R eco and enhances process understanding at sparsely monitored watersheds.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

WIND Validation Cases: Computational Study of Thermally-perfect Gases

The ability of the WIND Navier-Stokes code to predict the physics of multi-species gases is investigated in support of future high-speed, high-temperature propulsion applications relevant to NASA's Space Transportation efforts. Three benchmark cases are investigated to evaluate the capability of the WIND chemistry model to accurately predict the aerodynamics of multi-species chemically non-reacting (frozen) gases. Case 1 represents turbulent mixing of sonic hydrogen and supersonic vitiated air. Case 2 consists of heated and unheated round supersonic jet exiting to ambient. Case 3 represents 2-D flow through a converging-diverging Mach 2 nozzle. For Case 1, the WIND results agree fairly well with experimental results and that significant mixing occurs downstream of the hydrogen injection point. For Case 2, the results show that the Wilke and Sutherland viscosity laws gave similar results, and the available SST turbulence model does not predict round supersonic nozzle flows accurately. For Case 3, results show that experimental, frozen, and 1-D gas results agree fairly well, and that frozen, homogeneous, multi-species gas calculations can be approximated by running in perfect gas mode while specifying the mixture gas constant and Ratio of Specific Heats.

DalBello, Teryn↗

NASA NTRS1982

The influence of ground moisture conditions in North America on summer climate as modeled in the GISS GCM

A new general circulation model, developed to run on a coarse grid (8 x 10 deg resolution) at the Goddard institute for Space Studies is employed to investigate the potential use of ground moisture anomalies for seasonal climate prediction. For three different summertime simulations, the ground moisture on 1 June over the United States is reduced to 1/4 of its value in the control run. The results show that the subsequent surface air temperature is significantly higher throughout most of the summer, while the precipitation decreases, especially in June and July. Knowledge of late spring ground moisture anomalies should thus be an aid in predicting summertime climate.

Rind, D.↗

NASA NTRS1984

Hydrogen chloride and aerosol ground cloud characteristics resulting from Space Shuttle launches

Airborne measurements of gaseous HCl, gaseous and aerosol HCl, particulates, relative humidity and temperature were obtained in ground clouds produced during three Space Shuttle launches. Partitioning of HCl between HCl aerosol and gaseous HCl was investigated as the solid rocket exhaust cloud diluted with ambient air to evaluate the conditions under which aerosol formation occurs in the troposphere in the presence of hygroscopic HCl vapor. Equilibrium predictions for aqueous HCl aerosol formation generally agree with the measured HCl partitioning over HCl concentrations from 0.5 to 36 ppm. HCl concentration dispersion within four cloud segments at time t (min) was evaluated using the expression C = C(0) (t to the alpha power) where C(0) varied from 145 to 2250 ppm and alpha varied from -1.14 to -1.73. Aerosol fallout from the exhaust clouds was measured with time by monitoring HCl concentrations and aerosol distributions 100 m below the cloud as it drifted away from the launch site. Significant amounts of HCl were found to be removed by fallout of particles in the 80-220 micron diameter range up to 30 min after launch.

Sebacher, D. I.↗

NASA NTRSDate not supplied

Predicting Maximum Thermal Response in a Li-ion Cell as a Thermal Runaway Predictor for a UAV Fight

Thermal runaway↗

NASA NTRS1987

Real gas properties and Space Shuttle Main Engine fuel turbine performance prediction

The H2/H2O mixture thermodynamic and transport properties variations for the Space Shuttle Main Engine (SSME) fuel turbine over a range of temperatures and pressures are examined. The variation of molecular viscosity, specific heat at constant pressure, and Prandtl number for the hydrogen/steam mixture are fitted using polynominal relationships for future turbine performance use. The mixture property variations are calculated using GASP and WASP computer programs. The air equivalent performance of the SSME fuel turbine is computed.

Harloff, G. J.↗

NASA NTRSDate not supplied

Predicting Thermal Response in a Li-ion Cell on a UAV Fight Profile

As the energy storage devices continue to "pack" more energy in a small space, any damage, battery component failure, manufacturing defect, or electrically abusing the battery can lead to catastrophic thermal runaway events. A catastrophic thermal event in a cell leads to high temperature, in some instances spewing of battery materials due to gas development from side reactions initiated due to high internal temperatures. Also, a thermal runaway event can propagate from a single "failed" cell to the pack in a battery pack, leading to a more significant event. Mitigating a thermal runaway event is important in the commercial and automotive sectors. However, preventing such events in an electric aircraft (or air taxis) is paramount due to the lack of alternatives in the event of a failure. Battery prognostics algorithms allow the prediction of state-of-charge (SOC) and end-of-life (EOL) of a Li-ion battery in a UAV (unmanned air vehicle) [1]. For this presentation, we will extend this two-level battery predictive algorithm to predict SOC, EOL, and estimated maximum temperature during a simulated flight. The model is extended by integrating a lumped physics-driven thermal model for high current densities [2]. The parameters used to control SOC and EOL are maximum storable charge, time constant for Li-ion diffusivity in the carbon particles, and internal cell resistance. Cycling leads to an increase in the heat generated by an aged Li-ion cell with a LiyCoO2 (LCO) cathode and a LixC6 (MCMB) anode. The aging of a cell leads to increase in SEI layer thickness, the diffusion time for the lithium ions inside the electrodes, and the local reaction rates, in addition to the thermodynamic abuse caused by fixed cycling voltages controlled by a Battery Management System. As the battery ages, the cell resistance increases, while the onset temperature of the thermal runaway decreases (depends on the cell chemistry and cell abuse history). Any large deviation of the cell temperature from the estimated (expected) value can identify a faulty cell. Since SEI decomposition has the lowest onset temperature in the series of reactions leading to thermal runaway, the model considers the self-heating rate of the SEI decomposition as onset temperature (similar to Ref. [3]). The parameters in the Arrhenius equation for the SEI heating rate depend on the number of cycles, the cell's operating temperature, and the cell's abuse history [4,5]. Coupling the electrochemical, thermal, and aging model allow the prognostic algorithm to estimate a typical cell voltage and temperature as a function of age (cycling and calendar), whose departure from measured values from the BMS is used to identify a safety event. In addition, we will present the results from two simulated flight scenarios for a UAV: typical and extreme, since the power requirements vary significantly during take-off, landing, and changing altitudes, while the power requirements remain low during the cruise. For this presentation, the power requirement for a battery pack in a UAV is scaled to a single cell. This cell is cycled through a simulated profile, and the data is collected and used to predict a safety event.

Li-ion↗