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NASA NTRS2014

Evaluation of Mixed-Phase Microphysics Within Winter Storms Using Field Data and In Situ Observations

Snow prediction within models is sensitive to the snow densities, habits, and degree of riming within the BMPs. Improving these BMPs is a crucial step toward improving both weather forecasting and climate predictions. Several microphysical schemes in the Weather Research and Forecasting (WRF) model down to 1.33-km grid spacing are evaluated using aircraft, radar, and ground in situ data from the Global Precipitation Mission Cold-season Precipitation Experiment (GCPEx) experiment over southern Ontario, as well as a few years (12 winter storms) of surface measurements of riming, crystal habit, snow density, and radar measurements at Stony Brook, NY (SBNY on north shore of Long Island) during the 2009-2012 winter seasons. Surface microphysical measurements at SBNY were taken every 15 to 30 minutes using a stereo microscope and camera, and snow depth and snow density were also recorded. During these storms, a vertically-pointing Ku band radar was used to observe the vertical evolution of reflectivity and Doppler vertical velocities. The GCPex presentation will focus on verification using aircraft spirals through warm frontal snow band event on 18 February 2012. All the BMPs realistically simulated the structure of the band and the vertical distribution of snow/ice aloft, except the SBU-YLIN overpredicted slightly and Thompson (THOM) underpredicted somewhat. The Morrison (MORR) scheme produced the best slope size distribution for snow, while the Stony Brook (SBU) underpredicted and the THOM slightly overpredicted. Those schemes that have the slope intercept a function of temperature (SBU and WSM6) tended to perform better for that parameter than others, especially the fixed intercept in Goddard. Overall, the spread among BMPs was smaller than in other studies, likely because there was limited riming with the band. For the 15 cases at SBNY, which include moderate and heavy riming events, the non-spherical snow assumption (THOM and SBU-YLIN) simulated a more realistic distribution of reflectivity than spherical snow assumptions in the WSM6 and MORR schemes. The MORR, WSM6, and SBU schemes are comparable to the observed velocity distribution in light and moderate riming periods. The THOM is approx. 0.25 m/s too slow with its velocity distribution in these periods. In heavier riming, the vertical Doppler velocities in the WSM6, THOM, and MORR schemes were approx. 0.25 m/s too slow, while the SBU was 0.25 to 0.5 m/s too fast because of some excessive cloud water issues.

Colle, Brian A.↗
NASA NTRS2016

Generalized Fluid System Simulation Program, Version 6.0

The Generalized Fluid System Simulation Program (GFSSP) is a general purpose computer program for analyzing steady state and time-dependent flow rates, pressures, temperatures, and concentrations in a complex flow network. The program is capable of modeling real fluids with phase changes, compressibility, mixture thermodynamics, conjugate heat transfer between solid and fluid, fluid transients, pumps, compressors, and external body forces such as gravity and centrifugal. The thermofluid system to be analyzed is discretized into nodes, branches, and conductors. The scalar properties such as pressure, temperature, and concentrations are calculated at nodes. Mass flow rates and heat transfer rates are computed in branches and conductors. The graphical user interface allows users to build their models using the 'point, drag, and click' method; the users can also run their models and post-process the results in the same environment. Two thermodynamic property programs (GASP/WASP and GASPAK) provide required thermodynamic and thermophysical properties for 36 fluids: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutene, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride, ammonia, hydrogen peroxide, and air. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. The users can also supply property tables for fluids that are not in the library. Twenty-four different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include pipe flow, flow through a restriction, noncircular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, Joule-Thompson device, control valve, heat exchanger core, parallel tube, and compressible orifice. The program has the provision of including additional resistance options through User Subroutines. GFSSP employs a finite volume formulation of mass, momentum, and energy conservation equations in conjunction with the thermodynamic equations of state for real fluids as well as energy conservation equations for the solid. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. The application and verification of the code has been demonstrated through 30 example problems.

Majumdar, A. K.↗
NASA NTRS2015

Numerical Modeling of Self-Pressurization and Pressure Control by Thermodynamic Vent System in a Cryogenic Tank

This paper presents a numerical model of a system-level test bed - the multipurpose hydrogen test bed (MHTB) using Generalized Fluid System Simulation Program (GFSSP). MHTB is representative in size and shape of a fully integrated space transportation vehicle liquid hydrogen (LH2) propellant tank and was tested at Marshall Space Flight Center (MSFC) to generate data for cryogenic storage. GFSSP is a finite volume based network flow analysis software developed at MSFC and used for thermo-fluid analysis of propulsion systems. GFSSP has been used to model the self-pressurization and ullage pressure control by Thermodynamic Vent System (TVS). A TVS typically includes a Joule-Thompson (J-T) expansion device, a two-phase heat exchanger, and a mixing pump and spray to extract thermal energy from the tank without significant loss of liquid propellant. Two GFSSP models (Self-Pressurization & TVS) were separately developed and tested and then integrated to simulate the entire system. Self-Pressurization model consists of multiple ullage nodes, propellant node and solid nodes; it computes the heat transfer through Multi-Layer Insulation blankets and calculates heat and mass transfer between ullage and liquid propellant and ullage and tank wall. TVS model calculates the flow through J-T valve, heat exchanger and spray and vent systems. Two models are integrated by exchanging data through User Subroutines of both models. The integrated models results have been compared with MHTB test data of 50% fill level. Satisfactory comparison was observed between test and numerical predictions.

Majumdar, Alok↗
NASA NTRS2017

Description of the TCERT Vetting Reports for Data Release 25

The Q1Q17 DR25 TCERT Vetting Reports are a collection of plots and diagnostics used by the Threshold Crossing Event Review Team (TCERT) to evaluate threshold crossing events (TCEs). While designation of Kepler Objects of Interest (KOIs) and classification of them as Planet Candidates (PCs) or False Positives (FPs) is completely automated via a robotic vetting procedure (the Robovetter) for the Q1Q17 DR25 planet catalog, as described in Thompson et al. (2017), these reports help to visualize the metrics used by the Robovetter and evaluate those robotic decisions for individual objects. For each Q1Q17 DR25 TCE, these reports include the following products: (a) the DV one-page summary, (b) selected pertinent diagnostics and plots from the full DV report, and (c) additional plots and diagnostics not included in the full DV report, including an alternate means of data detrending.

Kepler Objects of Interest↗
NASA NTRS2017

Dynamical Conditions of Ice Supersaturation and Ice Nucleation in Convective Systems: A Comparative Analysis Between in Situ Aircraft Observations and WRF Simulations

Occurrence frequency and dynamical conditions of ice supersaturation (ISS, where relative humidity with respect to ice (RHi) greater than 100%) are examined in the upper troposphere around convective activity. Comparisons are conducted between in situ airborne observations and the Weather Research and Forecasting model simulations using four double-moment microphysical schemes at temperatures less than or or equal to -40degdegC. All four schemes capture both clear-sky and in-cloud ISS conditions. However, the clear-sky (in-cloud) ISS conditions are completely (significantly) limited to the RHi thresholds of the Cooper parameterization. In all of the simulations, ISS occurrence frequencies are higher by approximately 3-4 orders of magnitude at higher updraft speeds (greater than 1 m s(exp -1) than those at the lower updraft speeds when ice water content (IWC) greater than 0.01 gm(exp -3), while observations show smaller differences up to approximately 1-2 orders of magnitude. The simulated ISS also occurs less frequently at weaker updrafts and downdrafts than observed. These results indicate that the simulations have a greater dependence on stronger updrafts to maintain/generate ISS at higher IWC. At lower IWC (less than or equal or 0.01 gm(exp -3), simulations unexpectedly show lower ISS frequencies at stronger updrafts. Overall, the Thompson aerosol-aware scheme has the closest magnitudes and frequencies of ISS greater than 20% to the observations, and the modified Morrison has the closest correlations between ISS frequencies and vertical velocity at higher IWC and number density. The Cooper parameterization often generates excessive ice crystals and therefore suppresses the frequency and magnitude of ISS, indicating that it should be initiated at higher ISS (e.g.,lees than or equal to 25%).

D’Alessandro, John J.↗
NASA NTRS2016

Kepler Data Release 25 Notes (Q0-Q17)

These Data Release Notes provide information specific to the current reprocessing and re-export of the Q0-Q17 data. The data products included in this data release include target pixel files, light curve files, FFIs,CBVs, ARP, Background, and Collateral files. This release marks the final processing of the Kepler Mission Data. See Tables 1 and 2 for a list of the reprocessed Kepler cadence data. See Table 3 for a list of the available FFIs. The Long Cadence Data, Short Cadence Data, and FFI data are documented in these data release notes. The ancillary files (i.e., cotrending basis vectors, artifact removal pixels, background, and collateral data) are described in the Archive Manual (Thompson et al., 2016).

Data Release Notes↗
NASA NTRS2017

Kepler Commissioning Data for Measurement of the Pixel Response Function and Focal Plane Geometry

This document describes the Kepler PRF/FPG data release. This data was taken on April 27-29, 2009, during Kepler's commissioning phase in order to measure the pixel response function (PRF) (Bryson et al., 2010a) and focal plane geometry (FPG) (Tenenbaum and Jenkins, 2010). 33,424 stellar targets were observed for 243 long cadences, each with a duration of 14.7 minutes (half the duration of a normal Kepler long cadence). During these 243 cadences the Kepler photometer was moved, pointing in a dither pattern to facilitate PRF measurement. Motion occurred during the even cadences (second, fourth, etc.), with the telescope in stable fine point at each pointing in the dither pattern during the odd cadences (first, third, etc.). The first and last cadences were at the center of the dither pattern. Motion cadences are included in this release, but they do not contain any data. For details on how this data was used to derive the Kepler PRF and FPG models, see Bryson et al. (2010a) and Tenenbaum and Jenkins (2010). Descriptions of the PRF and FPG models are found in Thompson et al. (2016), x2.3.5.17 and x2.3.5.16 respectively. The data in this release can be used to recompute the Kepler PRF and FPG. Such a reconstruction, however, would not reflect measured seasonal changes in the PRF described in Van Cleve et al. (2016b), x5.2.The dither pattern is shown in Figure 1. The crosses show the commanded pointings and the circles show the measured pointings. Measured pointings are different from the commanded pointings due to the early state of calibration of the fine guidance sensors during commissioning (Van Cleve et al., 2016a). The measured offsets from the center of the pattern are given in RADEC offsets and pixel offsets in Table 1. The order of the offsets was randomized during data collection to avoid time-dependent systematics.

Kepler↗
NASA NTRS2017

Kepler Planet Detection Metrics: Window and One-Sigma Depth Functions for Data Release 25

This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.

Planet Detection Metrics↗
NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Flux-Level Transit Injection Tests of TPS for Data Release 25

Quantifying the ability of a transiting planet survey to recover transit signals has commonly been accomplished through Monte-Carlo injection of transit signals into the observed data and subsequent running of the signal search algorithm (Gilliland et al., 2000; Weldrake et al., 2005; Burke et al., 2006). In order to characterize the performance of the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017) on a sample of over 200,000 stars, two complementary injection and recovery tests are utilized:1. Injection of a single transit signal per target into the image or pixel-level data, hereafter referred to as pixel-level transit injection (PLTI), with subsequent processing through the Photometric Analysis (PA), Presearch Data Conditioning (PDC), Transiting Planet Search (TPS), and Data Validation (DV) modules of the Kepler pipeline. The PLTI quantification of the Kepler pipeline's completeness has been described previously by Christiansen et al. (2015, 2016); the completeness of the final SOC 9.3 Kepler pipeline acting on the Data Release 25 (DR25) light curves is described by Christiansen (2017).2. Injection of multiple transit signals per target into the normalized flux time series data with a subsequent transit search using a stream-lined version of the Transiting Planet Search (TPS) module. This test, hereafter referred to as flux-level transit injection (FLTI), is the subject of this document. By running a heavily modified version of TPS, FLTI is able to perform many injections on selected targets and determine in some detail which injected signals are recoverable. Significant numerical efficiency gains are enabled by precomputing the data conditioning steps at the onset of TPS and limiting the search parameter space (i.e., orbital period, transit duration, and ephemeris zero-point) to a small region around each injected transit signal.The PLTI test has the advantage that it follows transit signals through all processing steps of the Kepler pipeline, and the recovered signals can be further classified as planet candidates or false positives in the exact same manner as detections from the nominal (i.e., observed) pipeline run (Twicken et al., 2016, Thompson et al., in preparation). To date, the PLTI test has been the standard means of measuring pipeline completeness averaged over large samples of targets (Christiansen et al., 2015, 2016; Christiansen, 2017). However, since the PLTI test uses only one injection per target, it does not elucidate individual-target variations in pipeline completeness due to differences in stellar properties or astrophysical variability. Thus, we developed the FLTI test to provide a numerically efficient way to fully map individual targets and explore the performance of the pipeline in greater detail. The FLTI tests thereby allow a thorough validation of the pipeline completeness models (such as window function (Burke and Catanzarite, 2017a), detection efficiency (Burke Catanzarite, 2017b), etc.) across the spectrum of Kepler targets (i.e., various astrophysical phenomena and differences in instrumental noise). Tests during development of the FLTI capability revealed that there are significant target-to-target variations in the detection efficiency.

DR25↗
NASA NTRS2018

Towards an Enhanced Droplet Activation Scheme for Multi-Moment Bulk Microphysics Schemes

Initial droplet spectra produced upon activation impact the ensuing chain of microphysical processes andtherefore play a crucial role in cloud evolution. This work re-examines dependencies of newly formed clouddroplet size distribution (CDSD) characteristics on environmental and aerosol properties via parcel model simulationsthat serve as the basis for a multi-moment bulk microphysics droplet activation scheme suitable for acloud-resolving model (CRM). It is found that applying a fixed size threshold to define activated droplets versusemploying physical considerations can lead to erroneous activation and overly broad CDSDs for high aerosolconcentration and weak updraft conditions. Aerosol distributions characterized by larger median sizes and/orincreased solubility can result in greater activated droplet numbers, whereas impacts of these parameters onCDSD spectral width depend on both aerosol number concentration and updraft velocity. An expansion of theactivation scheme to include CDSD spectral width is proposed to aid efforts to extend high-order momentprediction to cloud droplet categories in CRMs as well as better represent variability in the activation process onthe cloud scale.simulations to investigate the regime dependence of the relative dispersion(d)1 of newly activated CDSDs, where d is the ratio of dropletradius standard deviation (σ) to the mean radius (r ). C16 demonstratedthat increasing Na resulted in increasing (decreasing) d values via reducedcondensational narrowing (spectral broadening) rates within theAL (UL) regime, with d values peaking in the TR regime. Their findingssuggest a similar regime dependence for d as R09 noted for Nc and helpexplain reportedly conflicting relationships between Na and CDSDspectral characteristics (cf. Hudson and Noble, 2014; Liu et al., 2014),although the applicability of these results within bulk microphysicalschemes was not addressed.Simulating aerosol-cloud interactions with CRMs employing bulkmicrophysics requires that the scheme minimally predict two CDSDparameters, namely mass and number concentrations, and represent thedroplet activation process. Various activation schemes aim to determineNc from aerosol and environmental properties and include analyticalexpressions (e.g., Abdul-Razzak et al., 1998; Morrison et al., 2005) aswell as lookup tables (LUTs) based on detailed parcel model calculations(e.g., Saleeby and Cotton, 2004, hereafter SC04; Segal and Khain,2006; Thompson and Eidhammer, 2014). Expressions to diagnose CDSDspectral width from Nc (Grabowski, 1998; Liu et al., 2006; Morrison andGrabowski, 2007) or cloud water content (Geoffroy et al., 2010) havealso been developed, although more robust methods to obtain CDSDspectral width upon activation are presently lacking. This latter point isrelevant for triple-moment (3 M) bulk microphysics that aim to predictdistribution spectral width alongside number and mass concentrations(e.g., Loftus et al., 2014; Milbrandt and Yau, 2005).The current work extends the findings of C16 to the current LUTbasedaerosol activation scheme used in the Regional AtmosphericModeling System (RAMS) (Cotton et al., 2003; SC04; Saleeby and vanden Heever, 2013, hereafter SvdH13) and additionally examinesaerosol size and solubility impacts on newly activated CDSD properties.Because early cloud development processes such as condensationalgrowth, evaporation, and droplet self-collection depend on and impactCDSD spectral width (Hudson and Yum, 1997; Seifert and Beheng 2001;Lu and Seinfeld, 2006; Igel and van den Heever, 2017), an expansion ofthe activation LUTs to include CDSD spectral width is proposed as apreliminary step for extending 3M prediction to CDSDs in CRMs forimproved simulations of aerosol-cloud interactions.2. MethodologyThe current RAMS two-moment microphysics module determinesthe fractional number of aerosol particles that activate to cloud dropletsfrom five-dimensional LUTs based on model predicted air temperature(T), w, Na, and the geometric median radius (rg) and soluble fraction (ε)of the aerosol size distribution (SvdH13). These LUTs are created offlineusing a one-dimensional Lagrangian adiabatic parcel model (Feingoldand Heymsfield, 1992; Heymsfield and Sabin, 1989; SC04) to simulateexplicit droplet activation and initial CDSD growth for a range of ambientatmospheric conditions [T, w] and binned lognormal aerosol sizedistributions given by= ⎡⎣ ⎢− ⎤⎦ ⎥N r Nr π σr rσ( )2 lnexp[ln( / )]2(ln )aggg22 (1)where r is the dry aerosol particle bin radius and σg is the geometricstandard deviation of the distribution. As the parcel model simulationsfocus on the activation process, other processes such as coalescence,sedimentation, and mixing are not considered. Details of the parcelmodel can be found in SC04 and SvdH13, and only a brief description isprovided here. At the onset of parcel model calculations, the initiallydry aerosol particles in all bins first deliquesce and reach theirequilibrium diameters in a sub-saturated environment based on theKöhler equation for solution droplets. The parcel is then lifted at a fixedupward velocity w, and particle growth by vapor diffusion, along withconcurrent changes in the ambient environment, are iteratively computedusing the Variable-coefficient Ordinary Differential Equation(VODE) solver (Brown et al., 1989). The time resolution of these calculationsis determined within the VODE solver, and the frequency atwhich the solver is called is controlled by a longer model time stepbased on fixed upward parcel displacement increments (Δz) at thespecified w (Δt=Δz/w). Model calculations proceed until the parcelreaches a height 50m beyond the level of maximum saturation ratio(Smax) or total parcel displacement exceeds 2 km. Upon model termination,Smax and the fractional number of aerosols (factv) resulting innewly formed cloud droplets, defined as particles having diameters of atleast 2 μm, are cataloged in the LUTs according to the specified T, w, Na,rg, and ε parameter values.A critical point regarding the creation of these LUTs is the use of afixed minimum diameter (Dmin) to define cloud droplets in the parcelmodel, which can produce erroneous CDSD characteristics, particularlywithin the UL regime. For aerosol distributions with large rg valuesunder low SS conditions, for example, deliquesced aerosols within thelarge tail of the distribution can exceed 2 μm in diameter yet remainunactivated as ‘haze’ particles (Levin and Cotton, 2009; McFigganset al., 2006). For this study, aerosol particles activate to cloud dropletsbased on the critical diameter Dcrit as a function of parcel supersaturationratio (Sr) as in R09:D = σ MS RTρ83 ln( ) critsol wr w (2)where σsol is the surface tension of a solution droplet, Mw and ρw are themolar mass and density of liquid water, respectively, and R is theuniversal gas constant. Additionally, at relatively large w values withinthe AL regime, Nc stabilizes shortly after reaching supersaturation.However, parcel ascent and condensational growth continue beyondthe level of Smax, potentially causing additional narrowing of the CDSD.In the current work, model calculations terminate upon reaching Smaxas changes in Nc are negligible with continued ascent (Peng et al., 2007;R09).Parcel model simulations are performed to examine the sensitivitiesof CDSD characteristics to w, Na, rg, and ε, with the ranges for theseparameters listed in Table 1. Aerosols are assumed to be a mix of solubleand insoluble material of equal density, specified by ε, where fullysoluble aerosols correspond to ammonium sulfate with hygroscopicityparameter κ=0.61 (Petters and Kreidenweis, 2007). FollowingSvdH13, aerosol geometric standard deviation is fixed at σg=1.8, andaerosol distributions (Eq. 1) are partitioned into 100 logarithmicallyspacedbins spanning a size range specific to each rg value. For all simulations,Δz=1 m, and initial values of relative humidity, air temperatureand pressure are set to RH=0.99, T=10 °C and p=900 hPa,respectively.

Loftus, Adrian M.↗
NASA NTRSDate not supplied

Surviving and Thriving in Space and on Earth's Oceans, Human Logistics and Sustainability: Comparisons and Considerations

Ocean exploration sailing journeys from hundreds of years ago typically required large vessels and large crews (in comparison with today’s space capsules) to travel between the continents and around the world. Modern sailors of today are able to complete similar distant voyages, in small vessels, with a minimal crew, comparable in size to modern space travel crews. This paper uses a systems engineering approach (e.g. using the NASA Human Integration Design Handbook (HIDH), NASA-SP-2010-3407, 2010 and the “Advanced Life Support Baseline Values and Assumptions Document, (BVAD)” NASA-CR-2004-208941, 2004.), to examine and compare the logistics and sustainability aspects of a small crew traveling on Earth's oceans in sailing vessels versus humans traveling in space. The “Mālama Honua Worldwide Voyage” of the Hōkūleʻa, a replica of an ancient Hawaiian double hulled sailing canoe, will be used as a baseline minimalist case study. This is a good comparison case since the Polynesian exploration of the vast (and virtually empty) Pacific Ocean with limited resources is an analogue to human space travel. A modern sailboat is compared to the ancient Polynesian methods and then a space craft is assessed with similar functional decomposition methods. In 1992 during his second Space Shuttle mission (STS-52, Columbia) Astronaut Lacy Veach received a radio message from a student: "What are the similarities and differences between canoe and space travel?" Astronaut Charles Lacy Veach answered, "Both are voyages of exploration. Hōkūle‘a is in the past, Columbia is in the future." Navigator Nainoa Thompson added from the sailing canoe, "Columbia is the highest achievement of modern technology today, a voyaging canoe was the highest achievement of technology in its day." This paper is dedicated to the memory of two great Hawaiian astronauts: US Air Force Colonel Charles Lacy Veach and US Air Force Colonel Ellison Onizuka and to legendary waterman and Hōkūleʻa crew member Eddie Aikau who was lost at sea in 1978, at the beginning of a 30-day, 2,500-mile (4,000km) journey by the Hōkūleʻa to follow the ancient route of the Polynesian migration between the Hawaiian and Tahitian island chains.

Robert P Mueller↗
NASA NTRS2021

Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar

Understanding the spatial variability of the snowpack is valuable for hydrologists and ecologists seeking to predict hydrological processes in a cold region. Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as solar radiation, wind speed and direction, and soil moisture. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenging due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) model to identify primary controls over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in the MaxEnt framework to predict locations of deeper or shallower snowpacks, respectively, across a mixed temperate forested and field landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, down-scaling remotely sensed snow products, and understanding field scale snow states.

Eunsang Cho↗
NASA NTRSDate not supplied

Towards Accurate and Efficient Predictions of Martensitic Transition Temperatures for Shape Memory Alloys from First Principles

Recent rapid progresses in physics theory and computational power have made it possible to predict the martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles [1-3]. In particular, rigorous while time-consuming thermodynamic integration has been employed to compute the anharmonic phonon free energies, which play a crucial role in determining martensitic phase transitions in SMAs. However, this approach has only been applied to simple binaries, and its accuracy is unsatisfying for certain SMAs such as the most commonly used NiTi. In this work, we report on several new developments to our method that bring first-principles theory and experiment much closer into agreement including the MTT of NiTi, and that improve the computational efficiency significantly. We have applied our refined approach to investigate the Ni0.5Ti0.5-xHfx and PdxNi0.5-xTi0.5 ternaries, and the predicted MTT for each composition is within 100K compared with experiment. We will address various techniques to overcome the difficulty encountered in studying ternaries. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties. [1] J.B. Haskins, A.E. Thompson,and J.W. Lawson, Phys. Rev B 94, 214110 (2016). [2] J.B. HaskinsandJ.W. Lawson, J. App. Phys. 121, 205103 (2017). [3] J.B. Haskins, H. Malmir, S. J. Honrao, L. A. Sandoval, and J.W. Lawson, Acta Materialia 212, 116872 (2017).

Zhigang Wu↗
NASA NTRSDate not supplied

Prediction of Calcium Oxalate Stone Risk with Interventions

Introduction Mitigations intended to reduce bone mineral loss in-flight also influence the level of renal stone risk to astronauts. Extending a recent study completed by our group [1], we investigate the likely influence of combined fluid intake and Ca excretion mitigation strategies. Methods A computational biochemistry model representing CaOx crystal precipitation, growth, and agglomeration [2] is combined with a probabilistic analysis to predict the in-flight CaOx renal stone incidence risk ratio (IRR) relative to pre-flight values using 1517 astronaut 24-hour urine chemistries. The output of this simulation is used to assess the combined impact of controlling fluid intake and Ca excretion on maintaining IRR < 1.2 for > 95% of the population, a threshold determined from initial analysis of the model output compared to referent populations [1]. Results and Conclusions Figure 1 illustrates the simulated in-flight astronaut population (n=40000) with CaOx IRR > 1.2. The red lines indicate the known clinical risk boundary of each factor, and the white box is considered the area of high combined clinical risk. The simulation and identified risk threshold conform well to the expected clinical risk boundaries. The results, assuming insensible water losses equal or exceed 0.7 L/day, imply mitigations achieve risks similar to pre-flight levels when either Ca < 150 mg/day or fluid volume intake > 3.2 L/day individually, or combined Ca < 200 and fluid volume intake > 2.5 L/day. We continue to investigate similar compounded effects and look to apprise engineering trades by informing relative risk changes during mission design. References [1] D. A. Goodenow-Messman, S. A. Gokoglu, M. Kassemi, and J. Myers, Jerry G., “Characterization of Astronaut CaOx Renal Stone Incidence Rates to Quantify In-flight and Post-flight Relative Risk,” npj Microgravity, in review, 2021. [2] M. Kassemi and D. Thompson, “Prediction of renal crystalline size distributions in space using a PBE analytic model. 1. Effect of microgravity-induced biochemical alterations,” Am. J. Physiol. Physiol., vol. 311, no. 3, pp. F520–F530, Sep. 2016, doi: 10.1152/ajprenal.00401.2015.

Debra A Goodenow↗
NASA NTRSDate not supplied

Axisymmetric Two-Dimensional Modeling of No Vent Filling of a Cryogenic Tank using Generalized Fluid System Simulation Program

This paper presents preliminary analysis using a finite volume based computational method for network flow analysis for the simulation of chill and fill of a cryogenic tank. An axisymmetric two-dimensional model of a spherical tank was developed using a nodal approach. No Vent Filling (NVF) was accomplished using a Thermodynamic Vent System assisted injector where the injector was cooled by the cold gas generated by Joule-Thompson expansion of the liquid propellant. The numerical model accounts for a) different regimes of pool boiling heat transfer, b) condensation of vapor around spray droplets, and c) condensation of vapor at the interface of the cooled injector and ullage. The numerical predictions of tank pressure, filling rate, and wall temperatures were compared with the test data and one-dimensional (1D) model predictions. It was demonstrated that the axisymmetric 2D model provided a more realistic prediction than the 1D model.

Cryogenics↗
NASA NTRSDate not supplied

Active and Passive Storage Solutions for Low Temperature Lunar Sample Return

Active and passive thermal storage solutions to return lunar biological, physical science and/or geology samples to the Earth are under development. Temperature requirements for biological/physical science sample conditioning range from -100˚C to -153˚C and possibly down to -253˚C for geological samples. The lower limit for geological samples is derived from the temperature of permanently shadowed regions on the lunar surface and the sublimation temperature of specific volatiles of interest (i.e., H2O, NH3 and CH4). Individual samples for the lunar application are expected to be less than 50 kg with a 700 kg allowance for the container and refrigeration. Requiring no electrical power or heat rejection, passive approaches, comprised of high performance insulation and consumable Joule-Thompson cooling, may be preferred for shorter duration missions (< 30 days) to provide significant mass savings. Active storage approaches with cryogenic cooling may be necessary to preserve samples for longer periods of time. A notional passive storage concept with an internal vapor cooled shield is shown below. The vapor cooled shield contains the sample and is isolated from the outer container with concentric reflective rigid shields and conventional multi-layer and/or aerogel insulation on the outer layer. A special removable, insulating end cap to stow or retrieve the sample is included.

Lunar Sample Return↗
NASA NTRS2024

Martensitic Phase Transition in Complex NiTi-Based Shape Memory Alloys from First-Principles Calculations

Recent rapid progresses in physics theory and computational power have made it possible to accurately predict the phase transitions and martensitic transition temperatures (MTTs) in shape memory alloys (SMAs) from first principles. However, previously theory and calculations [1-4] were applied only to study highly ordered stoichiometric binary alloys such as NiTi, PdTi and NiHf. Here we report on our recent first-principles investigations [5,6] on Ni 0.5 Ti 0.5-x Hf x and Pd x Ni 0.5-x Ti 0.5 ternaries and off-stoichiometric NiTi, and the predicted martensitic phase transitions in these complex SMAs are in good agreement with experimental findings. In particular, the calculated MTTs for all these compositions are within 100 K compared with the corresponding measured data, and our results also reveal the origin of the striking asymmetry in MTT of the off-stoichiometric NiTi near equiatomic compositions. We will address various techniques to overcome the difficulty encountered in studying ternaries and off-stoichiometic binaries associated with disorder and/or much lowered symmetry. Our theoretical approach is expected to be a broadly applicable and predictive theory for designing complex SMAs with desirable properties. References: [1] J. B. Haskins, A. E. Thompson, and J. W. Lawson, Phys. Rev B 94 , 214110 (2016). [2] J. B. Haskins and J. W. Lawson, J. App. Phys. 121 , 205103 (2017). [3] J. B. Haskins, H. Malmir, S. J. Honrao, L. A. Sandoval, and J. W. Lawson, Acta Materialia 212 , 116872 (2017). [4] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 106 , L140102 (2022). [5] Z. Wu, H. Malmir, O. Benafan, and J. W. Lawson, Acta Materialia 261 , 119362 (2023). [6] Z. Wu, J. W. Lawson, and O. Benafan, Phys. Rev. B 108 , L140103 (2023).

Zhigang Wu↗
NASA NTRSDate not supplied

Boundary-Layer Cloud Modeling Challenges on the North Slope of Alaska

The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model. Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.

cloud base heights↗