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DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗

DOE OSTI2024

$\overline{TKE}$ Parameterization and $\bar{v}$ Uncertainty Analysis for CGMF

Previous work was performed on tuning CGMF parameters for 235 U, 238 U, and Plutonium isotopes. Now work is being done to tune minor uranium isotopes. However, uranium isotopes like 232 U and 236 U have almost no experimental data. We are applying cross-isotope models to extrapolate and tune CGMF on isotopes that lack experimental data. There exist several internal CGMF physics quantities that affect the output of CGMF—multi-chance fission probability, excitation energy sharing, spin-cutoff factor, spin scaling, and fragment total kinetic energy to name a few. The mean fragment total kinetic energy, $\overline{TKE}$, is particularly interesting because of its strong anti-correlation with $\bar{v}$. We are most interested in the mean fragment total kinetic energy before neutron emissions. $\overline{TKE}$ is assumed to be pre-neutron emission unless otherwise stated. Currently in CGMF, the $\overline{TKE}$ model for 233,234,235,238 U are tuned independently to reproduce ν for the associated isotopes. In this report, we will tune a cross-isotope $\overline{TKE}$ model to experimental $\overline{TKE}$ data for 232,233,234,235,236,238 U. Because of the unreliable and sparse nature of $\overline{TKE}$ experimental data, future work will use more reliable experimental $\bar{v}$ data to infer the $\overline{TKE}$ model (and likely other internal CGMF parameters) for uranium isotopes. Such work has been performed previously using a sensitivity analysis and Kalman filter methods.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2026

PISCES two-detector covariance matrix fit for the NOvA Experiment

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA's primary physics goals are the precise measurements of neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ , determine the neutrino mass ordering, and constrain the value of $\delta_{CP}$, via the study of muon neutrino to electron neutrino oscillation. In the standard NOvA three-flavor analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data constrain the FD prediction through a ratio method. While this allows for systematic uncertainties sharing the same effects in both detectors to cancel, it remains an FD-only fit and does not fully leverage the constraining power of the high-statistics ND. This analysis proposes a simultaneous ND+FD fit using the PISCES method. PISCES (Parameter Inference with Systematic Covariance and Exact Statistics) is a framework designed to support complex configurations such as a joint ND+FD fit. This allows PISCES to take full advantage of the ND data to directly constrain systematic uncertainties across all samples. In PISCES, systematic uncertainties are encoded in a fractional covariance matrix, and statistical uncertainties are handled with a Poisson likelihood, making the approach well suited for low-statistics samples. For interpretability, we further use a Newton–Raphson + PCA method to recover per-systematic pulls from the covariance formulation. This poster presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, describes its implementation and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method.

Rajaoalisoa, Miriama [Cincinnati U.] (ORCID:000000↗

NASA NTRS2005

Planar Laser-Induced Iodine Fluorescence Measurements in Rarefied Hypersonic Flow

A planar laser-induced fluorescence (PLIF) technique is discussed and applied to measurement of time-averaged values of velocity and temperature in an I(sub 2)-seeded N(sub 2) hypersonic free jet facility. Using this technique, a low temperature, non-reacting, hypersonic flow over a simplified model of a reaction control system (RCS) was investigated. Data are presented of rarefied Mach 12 flow over a sharp leading edge flat plate at zero incidence, both with and without an interacting jet issuing from a nozzle built into the plate. The velocity profile in the boundary layer on the plate was resolved. The slip velocity along the plate, extrapolated from the velocity profile data, varied from nearly 100% down to 10% of the freestream value. These measurements are compared with results of a DSMC solution. The velocity variation along the centerline of a jet issuing from the plate was measured and found to match closely with the correlation of Ashkenas and Sherman. The velocity variation in the oblique shock terminating the jet was resolved sufficiently to measure the shock wave thickness.

Cecil, Eric↗

NASA NTRSDate not supplied

GeoNEX-SUBSETS: Cutouts of geostationary satellite data over long-term monitoring sites

Satellite remote sensing data are important tool for extrapolating the knowledge obtained at the Fluxnet towers. We introduce our GEO-NEX geostationary satellite subset products, which make it easy to compare between ground observation and Geo-NEX products. The MODIS subset has been frequently used for validating ecosystem models developed at flux towers and upscaling the observed flux data to regional scales. However, MODIS on the polar orbiting satellites can observe target regions only once a day, while the Fluxnet eddy-covariance data are compiled as sub-hourly datasets. As a results, summarizing the sub-hourly flux data into daily statistics is necessary for the comparison between MODIS and Flux data. Here, the new generation geostationary satellite sensors (GOES-16/17 ABI and Himawari-8/9 AHI) has the high-frequent observation feature (10 minutes) in addition to similar spectral band and spatial resolution with MODIS. The high frequent observation allows us to compare the flux diurnal cycle with geostationary satellite sensor data. Some studies have already shown the effective utilization of time series of geostationary satellite data for ecosystem modeling. We are producing NEX Level-1G products, which is the gridded Top-of-Atmosphere reflectance and brightness temperature data of geostationary satellite sensors. We cut out the NEX Level-1G data using the same file format with the MODIS subset except for the projection. The other data products (e.g., surface reflectance, land surface temperature, vegetation indices and climate data) will be also added upon their availability. We selected the ground observation sites from Fluxnet, Phenocam, and AERONET networks. The NEX subset data will be provided through NASA NEX data portal.

GeoNEX↗

DOE OSTI2026

Measurements of three-flavor neutrino oscillations from a PISCES two-detector fit to the NOvA Experiment data

NOvA is a long-baseline neutrino oscillation experiment with two functionally identical detectors: a Near Detector (ND) at Fermilab, placed 1 km from the neutrino source, and a Far Detector (FD) located 810 km away from the ND in Minnesota. NOvA s primary physics goals are to measure the neutrino oscillation parameters $\theta_{23}$ and $\Delta m^2_{32}$ with high precision, determine the neutrino mass hierarchy, and constrain the value of $\delta_{CP}$, primarily via the study of muon neutrino to electron neutrino oscillation. Extracting values for oscillation parameters from fits to data usually relies on treating systematic uncertainties as nuisance parameters, a strategy that suffers from poor scalability as the number of uncertainties becomes larger. This work introduces PISCES (Parameter Inference with Systematic Covariance and Exact Statistics), a novel method that circumvents this scalability problem by encoding systematic uncertainties into a covariance matrix. PISCES utilizes a nested minimization in which optimal systematic pulls are first computed using the covariance matrix in an inner minimization step, then the oscillation parameters are profiled over in the outer minimization. PISCES also uses a Poisson Likelihood term, making it ideal for the inclusion of low-statistic samples in the fits. PISCES is a flexible framework that also supports complex fits, such as a joint Near and Far detector fit. In the standard NOvA analysis, oscillation parameters are extracted using an extrapolation technique in which the ND data indirectly constrain the FD prediction via a ratio method. PISCES, on the other hand, enables a simultaneous ND+FD fit, allowing the high-statistics ND data to directly constrain systematic uncertainties across all samples. This thesis presents the full PISCES joint ND+FD fit for the NOvA three-flavor analysis, details its implementation, and evaluates its performance through extensive robustness tests and fake data studies. It also provides a comparison between the PISCES joint ND+FD results and the standard NOvA extrapolation method using the full NOvA 10-year data set. The results demonstrate that PISCES can successfully fit NOvA data while incorporating the constraints from the ND detectors consistently, using physically motivated systematic uncertainties to account for data/MC discrepancies.

Rajaoalisoa, Miriama [Cincinnati U.]↗

DOE OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗

NASA NTRS1993

An experimental investigation of a Mach 3.0 high-speed civil transport at supersonic speeds

An experimental study was conducted to determine the aerodynamic characteristics of a proposed high speed civil transport. This configuration was designed to cruise at Mach 3.0 and sized to carry 250 passengers for 6500 n.mi. The configuration consists of a highly blended wing body and features a blunt parabolic nose planform, a highly swept inboard wing panel, a moderately swept outboard wing panel, and a curved wingtip. Wind tunnel tests were conducted in the Langley Unitary Plan Wind Tunnel on a 0.0098-scale model. Force, moment, and pressure data were obtained for Mach numbers ranging from 1.6 to 3.6 and at angles of attack ranging from -4 to 10 deg. Extensive flow visualization studies (vapor screen and oil flow) were obtained in the experimental program. Both linear and advanced computational fluid dynamics (CFD) theoretical comparisons are shown to assess the ability to predict forces, moments, and pressures on configurations of this type. In addition, an extrapolation of the wind tunnel data, based on empirical principles, to full-scale conditions is compared with the theoretical aerodynamic predictions.

Hernandez, Gloria↗

DOE OSTI2019

Complex Sources and Impacts on Remediation and Monitoring Approaches - 19058

Remedy selection, implementation, and coordination with tank farm activities for adjacent waste sites need to be facilitated by a technically defensible description of sources and their relative strength and temporal contributions to groundwater contamination. Detailed data describing contaminant source releases are typically not available, so source locations are highly uncertain. Furthermore, data on sources and model parameters are not readily available through direct measurements. Instead, source data are often derived by extrapolation from a few direct and/or indirect measurements, while model parameters are commonly estimated using inverse modeling or calibration methods. Because the conceptual model of sources will influence the mass and timing of the flux of contaminants to groundwater, uncertainty in the source terms should be considered. Even though a best-fit model can often be identified, it may not be the best representation of the sources given the sparse, uncertain nature of the data used for estimating source terms. To support future feasibility studies, a systematic approach is needed for identifying remediation and monitoring activities appropriate under conditions of uncertainty. In this study, an integrated assessment of alternative conceptual model elements has been examined using the B-Complex site in the Hanford Central Plateau to demonstrate how multiple sources in a waste management complex can be described to identify their relative contributions to groundwater. The heterogeneous structure of the system is modeled based on available field information on the lithological data for both the vadose zone and aquifer. The approach includes a simulation assessment and linkages to site data, with an uncertainty analysis applied to evaluate the effects of source term uncertainty on contaminant mass flux to groundwater. An integrated assessment of alternative conceptual model elements was evaluated to identify how parameter uncertainty impacts both temporal and spatial uncertainties associated with vadose zone source terms. Modeling scenarios identify important characteristics of remediation target zones to provide context for how surface barriers, in situ remedies, and water-table treatment zones can be designed to account for source dynamics. Ramifications for remediation and monitoring are discussed within the context of the source term uncertainty. The described approach facilitates systematically addressing alternative conceptual model questions in relation to remediation and monitoring and supports the remedial investigation/ feasibility study process and the remediation strategy development for the Hanford Central Plateau. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Thermal tensor renormalization group simulations of square-lattice quantum spin models

In this work, we benchmark the well-controlled and numerically accurate exponential thermal tensor renormalization group (XTRG) in the simulation of interacting spin models in two dimensions. Finite temperature introduces a finite thermal correlation length ξ, such that for system sizes L >> ξ finite-size calculations actually simulate the thermodynamic limit. Here in this paper, we focus on the square lattice Heisenberg antiferromagnet (SLH) and quantum Ising models (QIM) on open and cylindrical geometries up to width W = 10 . We explore various one-dimensional mapping paths in the matrix product operator (MPO) representation, whose performance is clearly shown to be geometry dependent. We benchmark against quantum Monte Carlo (QMC) data, yet also the series-expansion thermal tensor network results. Thermal properties including the internal energy, specific heat, and spin structure factors, etc. are computed with high precision, obtaining excellent agreement with QMC results. XTRG also allows us to reach remarkably low temperatures. For SLH, we obtain an energy per site u$^*_g$ ≃ -0.6694 (4) and a spontaneous magnetization m$^*_S$ ≃ 0.30 ( 1 ) already consistent with the ground-state properties, which is obtained from extrapolated low-T thermal data on W ≤ 8 cylinders and W ≤ 10 open strips, respectively. We extract an exponential divergence versus T of the structure factor S (M), as well as the correlation length ξ, at the ordering wave vector M = ($π,π$) , which represents the renormalized classical behavior and can be observed over a narrow but appreciable temperature window, by analyzing the finite-size data by XTRG simulations. For the QIM with a finite-temperature phase transition, we employ several thermal quantities, including the specific heat, Binder ratio, as well as the MPO entanglement to determine the critical temperature $T_c$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1977

Investigation of real-gas and viscous effects on the aerodynamic characteristics of a 40 deg half-cone with suggested correlations for the shuttle orbiter

Parameters were evaluated that might be used to correlate shuttle orbiter aerodynamic data to be used in extrapolating from wind-tunnel to flight conditions. Preliminary calculations indicate that the lee-side forces will have an insignificant influence on the aerodynamic characteristics of the orbiter for moderate angle-of-attack entries; therefore, this work is focused on phenomena which have an overriding influence on windward forces, namely, real-gas (equilibrium and nonequilibrium) and viscous-interaction effects. Analytically determined flow fields previously obtained on 40 deg blunted cones were used as a data source to evaluate various correlation parameters. Inviscid effects were found to be the dominant contributor to the aerodynamic coefficients in the altitude range of 64 to 76.2 km. The most suitable correlation of the aerodynamic forces on these cones is based on local dynamic pressure and local Mach number.

Hunt, J. L.↗

NASA NTRS2004

Faster Processing for Inverting GPS Occultation Data

A document outlines a computational method that can be incorporated into two prior methods used to invert Global Positioning System (GPS) occultation data [signal data acquired by a low-Earth-orbiting satellite as either this or the GPS satellite rises above or falls below the horizon] to obtain information on altitude-dependent properties of the atmosphere. The two prior inversion methods, known as back propagation and canonical transform, are computationally expensive because for each occultation, they involve numerical evaluation of a large number of diffraction-like spatial integrals. The present method involves an angular-spectrum-based phase-extrapolation approximation in which each data point is associated with a plane-wave component that propagates in a unique direction from the orbit of the receiving satellite to intersect a straight line tangent to the orbit at a nearby point. This approximation enables the use of fast Fourier transforms (FFTs), which apply only to data collected along a straight-line trajectory. The computation of the diffraction-like integrals in the angular-spectrum domain by use of FFTs takes only seconds, whereas previously, it took minutes.

Ao, Chi↗

DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗

DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Machine learning guided optimal composition selection of niobium alloys for high temperature applications

Nickel- and cobalt-based superalloys are commonly used as turbine materials for high-temperature applications. However, their maximum operating temperature is limited to about 1100 °C. Therefore, to improve turbine efficiency, current research is focused on designing materials that can withstand higher temperatures. Niobium-based alloys can be considered as promising candidates because of their exceptional properties at elevated temperatures. The conventional approach to alloy design relies on phase diagrams and structure–property data of limited alloys and extrapolates this information into unexplored compositional space. In this work, we harness machine learning and provide an efficient design strategy for finding promising niobium-based alloy compositions with high yield and ultimate tensile strength. Unlike standard composition-based features, we use domain knowledge-based custom features and achieve higher prediction accuracy. We apply Bayesian optimization to screen out novel Nb-based quaternary and quinary alloy compositions and find these compositions have superior predicted strength over a range of temperatures. We develop a detailed design flow and include Python programming code, which could be helpful for accelerating alloy design in a limited alloy data regime.

Mohanty, Trupti (ORCID:0000000342701430)↗

DOE OSTI2026

Mass of 101 Sn and Bayesian extrapolations to the proton drip line

The favorable energy configurations of nuclei at magic numbers of 𝑁 neutrons and 𝑍 protons are fundamental for understanding the evolution of nuclear structure. The 𝑍 = 50 (tin) isotopic chain is a frontier for such studies, with particular interest at and around the doubly magic 100 Sn isotope, for which the mass is a topic of debate. Precise mass values for neutron-deficient isotopes provide necessary anchor points for mass models to test extrapolations near the proton drip line, where experimental studies remain out of reach. In this work, we report a Penning trap mass measurement of 101 Sn . The determined mass excess of −59889.89⁢(96) keV for 101 Sn represents a factor-of-300 improvement over the current precision and indicates that 101 Sn is less bound than previously thought. Mass predictions from a recently developed Bayesian model combination framework employing statistical machine learning and nuclear masses computed within seven global models based on nuclear density functional theory agree within 1⁢𝜎 with experimental masses from the 48 ≤ 𝑍 ≤ 52 isotopic chains. The framework's resilience to new mass data gave confidence in the extrapolation of tin masses down to 𝑁 = 46. Our calculations suggest that 96 Sn is a two-proton drip line nucleus and predict a mass excess of −58090⁢(800) keV for 100 Sn , showing a preference within 1⁢𝜎 for the mass of 100 Sn derived from the 𝛽-delayed 𝑄 value measured at GSI.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Learning to predict the cosmological structure formation

Matter evolved under the influence of gravity from minuscule density fluctuations. Nonperturbative structure formed hierarchically over all scales and developed non-Gaussian features in the Universe, known as the cosmic web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and use a large ensemble of computer simulations to compare with the observed data to extract the full information of our own Universe. However, to evolve billions of particles over billions of years, even with the simplest physics, is a daunting task. We build a deep neural network, the Deep Density Displacement Model ( D 3 M ), which learns from a set of prerun numerical simulations, to predict the nonlinear large-scale structure of the Universe with the Zel’dovich Approximation (ZA), an analytical approximation based on perturbation theory, as the input. Our extensive analysis demonstrates that D 3 M outperforms the second-order perturbation theory (2LPT), the commonly used fast-approximate simulation method, in predicting cosmic structure in the nonlinear regime. We also show that D 3 M is able to accurately extrapolate far beyond its training data and predict structure formation for significantly different cosmological parameters. Our study proves that deep learning is a practical and accurate alternative to approximate 3D simulations of the gravitational structure formation of the Universe.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1989

Low temperature measurement of the vapor pressures of planetary molecules

Interpretation of planetary observations and proper modeling of planetary atmospheres are critically upon accurate laboratory data for the chemical and physical properties of the constitutes of the atmospheres. It is important that these data are taken over the appropriate range of parameters such as temperature, pressure, and composition. Availability of accurate, laboratory data for vapor pressures and equilibrium constants of condensed species at low temperatures is essential for photochemical and cloud models of the atmospheres of the outer planets. In the absence of such data, modelers have no choice but to assume values based on an educated guess. In those cases where higher temperature data are available, a standard procedure is to extrapolate these points to the lower temperatures using the Clausius-Clapeyron equation. Last summer the vapor pressures of acetylene (C2H2) hydrogen cyanide (HCN), and cyanoacetylene (HC3N) was measured using two different methods. At the higher temperatures 1 torr and 10 torr capacitance manometers were used. To measure very low pressures, a technique was used which is based on the infrared absorption of thin film (TFIR). This summer the vapor pressure of acetylene was measured the TFIR method. The vapor pressure of hydrogen sulfide (H2S) was measured using capacitance manometers. Results for H2O agree with literature data over the common range of temperature. At the lower temperatures the data lie slightly below the values predicted by extrapolation of the Clausius-Clapeyron equation. Thin film infrared (TFIR) data for acetylene lie significantly below the values predicted by extrapolation. It is hoped to bridge the gap between the low end of the CM data and the upper end of the TFIR data in the future using a new spinning rotor gauge.

Kraus, George F.↗