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Coherence Analysis of the Space Launch System using Unsteady Pressure Sensitive Paint

Transonic buffet during atmospheric ascent is a major source of unsteady loading on launch vehicles that, in the past, has led to structural failures. Thus, determining accurate buffet forcing functions (BFFs) to properly predict the vehicle response to buffet is of vital importance in launch vehicle design. The state of the art for obtaining the BFFs relies on wind-tunnel tests where the fluctuating pressures are measured by pressure transducers (PTs) at sparse locations on a rigid, geometrically-scaled buffet model. To compute the BFFs, the outer mold line (OML) of the vehicle is mapped onto contiguous panels centered at the location of the PTs and the measured fluctuating pressures are integrated over the panels’ areas. To mitigate conservatism due to the assumption that the measured buffet pressures act in phase across each panel, coherence factors are applied, effectively reducing the integration areas and, therefore, the buffet forces. For coherence factors to provide the proper level of BFF attenuation, accurate knowledge of the spatial and temporal correlation of the buffet pressures is paramount. Unfortunately, even with hundreds of unsteady pressure transducers instrumenting the models, compromises must be made on the spatial resolution of the PTs. Typically, the PT layout aims at resolving the pressure correlation along the longitudinal axis of the vehicle, assuming that coherent structures propagate mostly in the longitudinal direction. As a result, the distribution of azimuthal/cross-stream correlation is not well known and its impact on the estimated BFFs is often neglected. To fill this and other knowledge gaps, extremely high spatial-resolution uPSP data were collected for three different production-design configurations of the Space Launch System in the NASA Ames Research Center Unitary Plan Wind Tunnel 11-Foot Transonic Wind Tunnel. Specifically, two cargo configurations, the Block 1 and Block 1B, and one crew configuration, the Block 1B crew, were surveyed at resolutions ranging from 600,000 to over 1 million uPSP measurement locations. To shed light on the temporal and spectral behavior of are presented. Several OML regions and flow features of interest are investigated, from theexpansion/shock on the Orion Multi-Purpose Crew Vehicle, to the terminal shock environment on the core stage, and the Strouhal shedding behind the boosters forward attach. The sensitivity of these environments to the vehicle attitude is examined. Furthermore, for selected panels,coherence factors that accurately capture the azimuthal coherence distribution of the buffetpressures are derived and their impact on the estimated BFFs is discussed. Finally, distributions of the local convection velocity and cross spectrum phase are presented.

buffet↗
DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the fractional Laplacian as $\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We also provide theoretical convergence rates with respect to $\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\delta$ and $\alpha$, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\delta$. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Global Evaluation of Terrestrial Near-Surface Air Temperature and Specific Humidity Retrievals from the Atmospheric Infrared Sounder (AIRS)

Global observations of near-surface air temperature and specific humidity over land are needed for a variety of applications, including to constrain global estimates of evapotranspiration (ET). Spaceborne hyperspectral observations, such as those from NASA’s Atmospheric Infrared Sounder (AIRS) mission, show promise for meeting this need, yet there are surprisingly few validation studies of AIRS near-surface atmospheric state retrievals. In this study, we use triple collocation to validate AIRS Level 3 retrievals of near-surface atmospheric state over land using twelve years of gridded station observations and two reanalyses. Deseasonalized AIRS retrievals correlate well with deseasonalized ground observations outside the tropics, but correlate less well in the tropics. Lower temporal sensitivity near the surface in the tropics contributes to the lower correlation for near-surface air temperature and is consistent with known physics of the tropical atmosphere, in which temperatures outside the boundary layer (which dominate the AIRS retrieval signal) are poorly correlated with those near the surface. Retrievals in the tropics may also be more susceptible to errors in cloud-clearing algorithms, and to uncertainty in surface emissivity. Since ET is greatest in the tropics, and tropical measurement networks are particularly sparse, this work motivates new approaches for measuring ET in the tropics.

Evapotranspiration↗
DOE OSTI2024

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

Abstract A major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks

Abstract We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ( κ ) from photometric measurements of galaxies along a given line of sight (LOS). The method is of particular interest in strong gravitational time-delay cosmography (TDC), where characterizing the “external convergence” ( κ ext ) from the lens environment and LOS is necessary for precise Hubble constant ( H 0 ) inference. Starting from a large-scale simulation with a κ resolution of ∼1′, we introduce fluctuations on galaxy–galaxy lensing scales of ∼1″ and extract random sight lines to train our BGNN. We then evaluate the model on test sets with varying degrees of overlap with the training distribution. For each test set of 1000 sight lines, the BGNN infers the individual κ posteriors, which we combine in a hierarchical Bayesian model to yield constraints on the hyperparameters governing the population. For a test field well sampled by the training set, the BGNN recovers the population mean of κ precisely and without bias (within the 2 σ credible interval), resulting in a contribution to the H 0 error budget well under 1%. In the tails of the training set with sparse samples, the BGNN, which can ingest all available information about each sight line, extracts a stronger κ signal compared to a simplified version of the traditional method based on matching galaxy number counts, which is limited by sample variance. Our hierarchical inference pipeline using BGNNs promises to improve the κ ext characterization for precision TDC. The code is available as a public Python package, Node to Joy ⏬ .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2007

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

Memarsadeghi, Nargess↗
NASA NTRS2004

NASA GEOS-3/TRMM Re-analysis: Capturing Observed Tropical Rainfall Variability in Global Analysis for Climate Research

Understanding climate variability over a wide range of space-time scales requires a comprehensive description of the earth system. Global analyses produced by a fixed assimilation system (i.e., re-analyses) - as their quality continues to improve - have the potential of providing a vital tool for meeting this challenge. But at the present time, the usefulness of re-analyses is limited by uncertainties in such basic fields as clouds, precipitation, and evaporation - especially in the tropics, where observations are relatively sparse. Analyses of the tropics have long been shown to be sensitive to. the treatment of cloud precipitation processes, which remains a major source of uncertainty in current models. Yet, for many climate studies it is crucial that analyses can accurately reproduce the observed rainfall intensity and variability since a small error of 1 mm/d in surface rain translates into an error of approx. 30 W/sq m in energy (latent heat) flux. Currently, discrepancies between the observed and analyzed monthly-mean rain rates averaged to 100 km x 100 km resolution can exceed 4 mm/d (or 120 W/sq m ), compared to uncertainties in surface radiative fluxes of approx. 10-20 W/sq m . Improving precipitation in analyses would reduce a major source of uncertainty in the global energy budget. Uncertainties in tropical precipitation have also been a major impediment in understanding how the tropics interact with other regions, including the remote response to El Nino/Southern Oscillation (ENSO) variability on interannual time scales, the influence of Madden-Julian Oscillation (MJO) and monsoons on intraseasonal time scales. A global analysis that can replicate the observed precipitation variability together with physically consistent estimates of other atmospheric variables provides the key to breaking this roadblock. NASA Goddard Space Flight Center has been exploring the use of satellite-based microwave rainfall measurements in improving global analyses and has recently produced a multi-year, 1 x 1 TRMM re-analysis , which assimilates 6-hourly TMI and SSM/I surface rain rates over tropical oceans using a ID variational continuous assimilation (VCA) procedure in the GEOS-3 global data assimilation system. The analysis period extends from 1 November 1997 through 3 1 December 2002. The goal is to produce a multi-year global analysis that is dynamically consistent with available tropical precipitation observations for the community to assess its utility in climate applications and identify areas for further improvements. A distinct feature of the GEOS-3RRMh4 re-analysis is that its precipitation analysis is not derived from a short-term forecast (as done in most operational systems) but is given by a time- continuous model integration constrained by precipitation observations within a 6-h analysis window, while the wind, temperature, and pressure fields are allowed to directly respond to the improved precipitation and associated latent heating structures within the same analysis window. In this talk, I will assess the impact VCA precipitation assimilation on analyses of climate signals ranging from a few weeks to interannual time scales and compare results against other operational and reanalysis products.

Hou, Arthur Y.↗
NASA NTRS2006

NASA Tech Briefs, May 2006

T-Shaped Emitter Metal Structures for HBTs Rigorous Estimation of SNR of a PSK Communication Link Advanced Ka-Band Transceiver With Monopulse Tracking EMI Filters for Low-Temperature Applications Lightweight Electronic Camera for Research on Clouds Pilot Weather Advisor System Waveguide Power-Amplifier Module for 80 to 150 GHz Better Back Contacts for Solar Cells on Flexible Substrates Topics covered include:Tunable, Highly Stable Lasers for Coherent Lidar; Improved Photon-Emission-Microscope System; Program Synthesizes UML Sequence Diagrams; Aspect-Oriented Subprogram Synthesizes UML Sequence Diagrams; Updated Computational Model of Cosmic Rays Near Earth; Software for Alignment of Segments of a Telescope Mirror; Simulation of Dropping of Cargo With Parachutes; DAVE-ML Utility Program; Robust Control for the Mercury Laser Altimeter; Thermally Stable Piezoelectric and Pyroelectric Polymers; Combustion Synthesis of Ca3(PO4)2 Net-Shape Surgical Implants; Stochastic Representation of Chaos Using Terminal Attractors; Two High-Temperature Foil Journal Bearings; Using Plates To Represent Fillets in Finite-Element Modeling; Repairing Chipped Silicide Coatings on Refractory Metal Substrates; Simplified Fabrication of Helical Copper Antennas; Graded-Index "Whispering-Gallery" Optical Microresonators; Optical Profilometers Using Adaptive Signal Processing; Manufacture of Sparse-Spectrum Optical Microresonators; Exact Tuning of High-Q Optical Microresonators by Use of UV; Automation for "Direct-to" Clearances in Air-Traffic Control; Improved Traps for Removing Gases From Coolant Liquids; and Lunar Constellation of Frozen Elliptical Inclined Orbits.

Source record↗
NASA NTRS2008

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Hong, Yang↗
DOE OSTI2023

Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making

One of the great challenges with decision making tasks on real world systems is the fact that data is sparse and acquiring additional data is expensive. In these cases, it is often crucial to make a model of the environment to assist in making decisions. At the same time, limited data means that learned models are erroneous, making it just as important to equip the model with good predictive uncertainties. In the context of learning sequential decision making policies, these uncertainties can prove useful for informing which data to collect for the greatest improvement in policy performance \citep{mehta2021experimental, mehta2022exploration} or informing the policy about unsure regions of state and action space to avoid during test time \citep{yu2020mopo}. Additionally, assuming that realistic samples of the environment can be drawn, an adaptable policy can be trained that attempts to make optimal decisions for any given possible instance of the environment \citep{ghosh2022offline, chen2021offline}. In this work, we examine the so-called ``probabilistic neural network'' (PNN) model that is ubiquitous in model-based reinforcement learning (MBRL) works. We argue that while PNN models may have good marginal uncertainties, they form a distribution of non-smooth transition functions. Not only are these samples unrealistic and may hamper adaptability, but we also assert that this leads to poor uncertainty estimates when predicting multiple step trajectory estimates. To address this issue, we propose a simple sampling method that can be implemented on top of pre-existing models.We evaluate our sampling technique on a number of environments, including a realistic nuclear fusion task, and find that, not only do smooth transition function samples produce more calibrated uncertainties, but they also lead to better downstream performance for an adaptive policy.

Offline Reinforcement Learning↗
DOE OSTI2022

Biorefinery upgrading of herbaceous biomass to renewable hydrocarbon fuels, Part 1: Process modeling and mass balance analysis

Process simulation has long been a well-established tool to track key operational, design, and mass and energy balance metrics for pre-commercial technologies such as advanced lignocellulosic biofuels. While tools such as this are well-documented in the public literature around 2nd-generation cellulosic ethanol technologies (which have been scaled up to commercial deployment to some degree over the past decade), such models and analysis information remain more sparse for more complex biorefinery pathways focused on producing drop-in hydrocarbon fuels and blend-stocks, particularly regarding information required to support air emissions or other environmental analysis. In this work, we summarize key details for an established "design case" modeling the conversion of herbaceous lignocellulosic biomass into a renewable diesel hydrocarbon blend-stock based on a representative lipid pathway from oleaginous yeast. The process is based on a biochemical deconstruction and upgrading approach utilizing deacetylation and dilute acid pretreatment, followed by enzymatic hydrolysis, fermentation, and catalytic upgrading of hydrolysate sugars to fuels. We provide key mass and energy balance outputs from the process models, with accompanying stream tables and component-level flowrates. A total of 12 model scenarios are presented spanning two feedstocks, three biorefinery scales, and two processing approaches for the lignin/residual solids waste streams. This "Part 1" manuscript presents the resulting impacts across the 12 cases on fuel yields and key output streams, focused here on direct biorefinery air emissions for selected components including CO 2 as well as sulfur (SOx) and nitrogen oxides (NOx). In the context of cleaner production, the latter focus on selected biorefinery air emission outputs establishes an initial baseline estimate and accompanying framework of the model cases, upon which an accompanying "Part 2" study will build to refine the values for these and other air pollutants across these scenarios, also considering mitigation options to comply with applicable regulatory standards. We also highlight further optimization opportunities based on potential tradeoffs identified here between air emissions versus life-cycle greenhouse gas profiles attributed to the disposition of lignin/residual solids.

09 BIOMASS FUELS↗
NASA NTRS2011

Experiments in Reconstructing Twentieth-Century Sea Levels

One approach to reconstructing historical sea level from the relatively sparse tide-gauge network is to employ Empirical Orthogonal Functions (EOFs) as interpolatory spatial basis functions. The EOFs are determined from independent global data, generally sea-surface heights from either satellite altimetry or a numerical ocean model. The problem is revisited here for sea level since 1900. A new approach to handling the tide-gauge datum problem by direct solution offers possible advantages over the method of integrating sea-level differences, with the potential of eventually adjusting datums into the global terrestrial reference frame. The resulting time series of global mean sea levels appears fairly insensitive to the adopted set of EOFs. In contrast, charts of regional sea level anomalies and trends are very sensitive to the adopted set of EOFs, especially for the sparser network of gauges in the early 20th century. The reconstructions appear especially suspect before 1950 in the tropical Pacific. While this limits some applications of the sea-level reconstructions, the sensitivity does appear adequately captured by formal uncertainties. All our solutions show regional trends over the past five decades to be fairly uniform throughout the global ocean, in contrast to trends observed over the shorter altimeter era. Consistent with several previous estimates, the global sea-level rise since 1900 is 1.70 +/- 0.26 mm/yr. The global trend since 1995 exceeds 3 mm/yr which is consistent with altimeter measurements, but this large trend was possibly also reached between 1935 and 1950.

Ray, Richard D.↗
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
DOE OSTI2023

Machine learning-based inversion for acoustic impedance with large synthetic training data: Workflow and data characterization

Where wells are sparse or training data are difficult to label with high-quality wireline-derived impedance logs, machine learning (ML)-based inversion of acoustic impedance typically depends on small training data sets, leading to biased prediction. We have advanced a novel workflow that applies large synthetic seismic training data to reduce facies-related bias. Using a geologically realistic model as the truth model, we randomly select sparse seed wells to perform sequential Gaussian simulation (SGS) for impedance models of the same geometry and simulate facies variability. We implement random forest regression on 30 features extracted from the synthetic volume. We observe that more seed wells tend to reduce facies-induced bias by sampling more types of facies, resulting in a better prediction. We then focus on the responses of SGS models to facies changes, the number of seed wells necessary for a useful synthetic model, and how much a synthetic model can help ML-based inversion. Here, we observe that the SGS synthetic training model outperforms well-direct training in general. For modeled clastic shore-zone systems in Miocene Gulf of Mexico, two or more seed wells are necessary for a significant reduction of root-mean-square error and outliners, and improvement of facies imaging. In a field-data test, we apply a similar workflow to quantitatively predict acoustic impedance, which is then converted to a sand-volume map at a high-frequency sequence (10–100 m), revealing detailed facies and sandstone patterns. Such results are valuable in many geologic and engineering applications, such as hydrocarbon and CO 2 reservoir prospecting, reserve estimation, simulation, etc.

3D seismic↗
NASA NTRS1971

Determination of stratospheric temperature and height gradients from nimbus 3 radiation data

To improve the specification of stratospheric horizontal temperature and geopotential height fields from satellite radiation data, needed for high flying aircraft, a technique was derived to estimate data between satellite tracks using interpolated IRIS 15-micron data from Nimbus III. The interpolation is based on the observed gradients of the MRIR 15-micron radiances between subsatellite tracks. The technique was verified with radiosonde data taken within 6 hours of the satellite data. The sample varied from 1126 pairs at low levels to 383 pairs at 10 mb using northern hemisphere data for June 15 to July 20, 1969. The data were separated into five latitude bands. The Rms temperature differences were generally from 2 to 5 C for all levels above 300 mb. From 500 to 300 mb RMS differences vary from 4 to 9C except at high latitudes which show values near 3C. The RMS differences between radiosonde heights and those calculated hydrostatically from the surface were from 30 to 280 meters increasing from the surface to 10 mb. Integration starting at 100 mb reduced the RMS difference in the stratosphere to 20 to 120 meters from 70 to 10 mb. From a comparison with actual operational maps at 50 and 10 mb, it appears the techniques developed produce analyses in general agreement with those from radiosonde data. In addition, they are able to indicate details over areas of sparse data not shown by conventional techniques.

Nicholas, G. W.↗
NASA NTRS2017

OGLE-2013-BLG-1761Lb: A Massive Planet around an MK Dwarf

We report the discovery and the analysis of the planetary microlensing event, OGLE-2013-BLG-1761. There are some degenerate solutions in this event because the planetary anomaly is only sparsely sampled. However, the detailed light curve analysis ruled out all stellar binary models and shows the lens to be a planetary system. There is the so-called close wide degeneracy in the solutions with the planet host mass ratio of q approx.(7.0+/-2.0) x 10(exp -3) and q approx.(8.1+/-2.6) x 10(exp -3) with the projected separation in Einstein radius units of s = 0.95 (close) and s = 1.18(wide), respectively. The microlens parallax effect is not detected, but the finite source effect is detected. Our Bayesian analysis indicates that the lens system is located -D(sub L) = 6.9(+ 1.0 -1.2)kpc away from us and the host star is an M/K dwarf with amass of M(sub L) = 0.33(+ 0.32- 1.9)Stellar Mass orbited by a super-Jupiter mass planet with a mass of m(sub p) = 2.7(+ 2.5 - 1.5) M(sub Jup) at the projected separation of a(sub l) = 1.8(+ 0.5 -0.5)au. The preference of the large lens distance in the Bayesian analysis is due to the relatively large observed source star radius. The distance and other physical parameters may be constrained by the future high-resolution imaging by large ground telescopes or HST. If the estimated lens distance is correct, then this planet provides another sample for testing the claimed deficit of planets in the Galactic bulge.

Hirao, Y.↗
NASA NTRSDate not supplied

Lessons from Earth Aerobiology for Venus Astrobiology

Venus’s clouds have often suggested as a possible habitat. The constraints governing putative airborne life in such a habitat in turn inform priorities and strategies for remote and in situ exploration, methods by which resulting biosignatures might be detected, exoplanet habitability assessment, and planetary protection concerns. Lessons drawn from studying Earth’s aerobiosphere can help improve this understanding. There are altitude ranges within Venus’s clouds in which temperature, pressure, particle size, and radiation appear to be within the limits of microbial life on Earth, and life cycles involving S- and Fe-based redox metabolism have been proposed. However, given the lack of a habitable surface reservoir, a long-term stable Venus aerobiosphere would require that the reproduction rate of the airborne microbes be faster than the settling rate of airborne microbes due to gravity, or eventually the population would be depleted; put another way, the mean generation time would need to exceed the mean residence time. This creates a joint constraint of aerosol dynamics, potential nutrient availability and energy influx, and bioenergetic costs such as desiccation and radiation damage. Even at an optimistic estimate of 75% H2SO4, Venus aerosol water activity (aw) is still ~0.02, far below the observed microbial growth limit of ~0.6. Long-term desiccation with brief spurts of repair and growth in response to transient water influx, such as from volcanism, is the most likely model for Earth-like life on Venus – a ‘desert bloom’ scenario. Several high-priority science goals – cloud aerosol composition, internal radiative flux, and aerosol residence time and circulation models – thus will also improve our understanding of Venus in an astrobiology context. In Earth’s troposphere, warm water clouds can carry 103 – 105 cells/mL, some metabolically active. However, Earth’s stratospheric sulfate aerosol layer may be a better analogue: supercooled sulfuric acidaerosols (acid weight fraction 0.6 – 0.85, 0.1 – 1 μm diameter, 0.1 – 1 cm-3) with little water activity, long residence times, high UV radiation, and only sporadic influx from surface particle sources. Though ‘hot spots’ can occur associated with tropospheric mixing, viable cells in stratospheric samples are rare (~102 cells/m3), and primarily inactive forms such as spores. It is not yet clear whether such bioaerosols are associated with sulfate aerosols or simply co-located, and reproduction in situ has not yet been observed. In this model of a sparse, largely dormant Venus ecosystem, a single transect on descent is likely to pass through a low-water, inactive region, missing potential signs of habitability or biosignatures. A targeted strategy would sample through an aerial region with some upwelling from surface sources, andtake multiple transects separated in time and space. This is compatible with other in situ science goals seeking to understand the dynamics and heterogeneities of Venus’s clouds.

Lower atmosphere↗