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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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246 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Resolved and Asymmetric Ring of PAHs within the Young Circumstellar Disk of IRS 48

For one decade, the spectral type and age of the ρ Oph object IRS-48 were subject to debate and mystery. Modeling its disk with mid-infrared to millimeter observations led to various explanations to account for the complex intricacy of dust holes and gas-depleted regions. We present multi-epoch high-angular-resolution interferometric near-infrared data of spatially resolved emissions in the first 15 au of IRS-48, known to have very strong polycyclic aromatic hydrocarbon (PAH) emissions within this dust-depleted region. We make use of new Sparse-Aperture-Masking data to instruct a revised radiative-transfer model, where spectral energy distribution fluxes and interferometry are jointly fitted. Neutral and ionized PAH, very small grains (VSG), and classical silicates are incorporated into the model; new stellar parameters and extinction laws are explored. A bright (42 L {sub ⊙}) and hence large (2.5 R {sub ⊙}) central star with A {sub v} = 12.5 mag and R {sub v} = 6.5 requires less near-infrared excess: the inner-most disk at ≈1 au is incompatible with the interferometric data. The revised stellar parameters place this system on a 4 Myr evolutionary track, four times younger than the previous estimations, which is in better agreement with the surrounding ρ Oph region and disk-lifetime observations. The disk-structure solution converges to a classical-grain outer disk from 55 au combined with an unsettled and fully resolved VSG and PAH ring, between 11 and 26 au. We find two overluminosities in the PAH ring at color-temperatures consistent with the radiative transfer simulations; one follows a Keplerian circular orbit at 14 au. We show a depletion of a factor of ≈5 of classical dust grains up to 0.3 mm compared to very small particles: the IRS-48 disk is nearly void of dust grains in the first 55 au. A 3.5 M {sub Jup} planet on a 40 au orbit can qualitatively explain the new disk structure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Remotely sensed physical property estimation from powder contaminated surfaces

It is the intent of this paper to demonstrate the veracity of a method to estimate the total solid contaminant mass present on a sparsely coated surface encompassing a pixel from longwave infrared spectra. Sparsely coated surfaces create complex radiometry due to the interactions of electromagnetic energy between intimate materials. Current algorithms can be used on intimate mixtures to identify the abundances of materials in a pixel, but they cannot provide additional property information. Radiative transfer models can create mixture signatures, but only with a set of well-characterized physical parameters that are typically not known or are difficult to retrieve. Finally, the approach described here creates a parameter inversion model from a radiative transfer model and uses empirically measured mixture data to retrieve physical characteristics of the contaminated surface and derive a total contaminant mass present.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

The Clouds and the Earth’s Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (R n ) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W·m -2 , Root Mean Square Error (RMSE) of 33.56 W·m -2 , and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W·m -2 , RMSE of 25.57 W·m -2 , and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R n , which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by -0.33 W·m -2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of -0.63 W·m -2 per year, the major driving factor of which was surface green vegetation

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A comparative study of discrete fracture network and equivalent continuum models for simulating flow and transport in the far field of a hypothetical nuclear waste repository in crystalline host rock

One of the major challenges of simulating flow and transport in the far field of a geologic repository in crystalline host rock is related to reproducing the properties of the fracture network over the large volume of rock with sparse fracture characterization data. Various approaches have been developed to simulate flow and transport through the fractured rock. The approaches can be broadly divided into Discrete Fracture Network (DFN) and Equivalent Continuum Model (ECM). The DFN explicitly represents individual fractures, while the ECM uses fracture properties to determine equivalent continuum parameters. In this paper, we compare DFN and ECM in terms of upscaled observed transport properties through generic fracture networks. The major effort was directed on making the DFN and ECM approaches similar in their conceptual representations. This allows for separating differences related to the interpretation of the test conditions and parameters from the differences between the DFN and ECM approaches. The two models are compared using a benchmark test problem that is constructed to represent the far field (1 × 1 × 1 km 3 ) of a hypothetical repository in fractured crystalline rock. The test problem setting uses generic fracture properties that can be expected in crystalline rocks. The models are compared in terms of the: 1) effective permeability of the domain, and 2) nonreactive solute breakthrough curves through the domain. The principal differences between the models are mesh size, network connectivity, matrix diffusion and anisotropy. We demonstrate how these differences affect the flow and transport. Finally, we identify the factors that should be taken in consideration when selecting an approach most suitable for the site-specific conditions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Variability in soil redox response to seasonal flooding in a vernal pond

Vernal ponds are ephemeral landscape features that experience intermittent flooding and drying, leading to variable saturation in underlying soils. Redox potential (E h ) is an important indicator of biogeochemical processes that changes in response to these hydrological shifts; however, high-resolution measurements of E h in variably inundated environments remain sparse. In this study, the responses of soil E h to ponding, drying, and rewetting of a vernal pond were investigated over a 5-month period from late spring through early autumn. Soil E h was measured at 10-min frequencies and at multiple soil depths (2–48 cm below the soil surface) in shallow and deep sections within the seasonally ponded lowland and in unsaturated soils of the surrounding upland. Over the study period, average E h in surface soils (0–8 cm) was oxidizing in the upland (753 ± 79 mV) but relatively reducing in the shallow lowland (369 ± 49 mV) and deep lowland (198 ± 37 mV). Reducing conditions (E h <300 mV) in surface soils prevailed for up to 6 days in the shallow lowland and up to 24 days in the deep lowland after surface water dried out. Intermittent reflooding resulted in multiple shifts between reducing and oxidizing conditions in the shallow lowland while the deep lowland remained reducing following reflooding. Soil E h in the uplands was consistently oxidizing over the study period with transient increases in response to rain events. Reducing conditions in the lowland resulted in greater Fe-oxide dissolution and release of dissolved Fe and P into porewater than in the surrounding uplands. We determined that change in water depth alone was not a good indicator of soil E h , and additional factors such as soil saturation and clay composition should be considered when predicting how E h responds to surface flooding and drying. These findings highlight the spatial and temporal variability of E h within ponds and have implications for how soil processes and ecosystem function are impacted by shifts in hydrology at terrestrial-aquatic interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Deflation as a method of variance reduction for estimating the trace of a matrix inverse

Many fields require computing the trace of the inverse of a large, sparse matrix. The typical method used for such computations is the Hutchinson method which is a Monte Carlo (MC) averaging over matrix quadratures. To improve its convergence, several variance reductions techniques have been proposed. In this paper, we study the effects of deflating the near null singular value space. We make two main contributions. First, we analyze the variance of the Hutchinson method as a function of the deflated singular values and vectors. Although this provides good intuition in general, by assuming additionally that the singular vectors are random unitary matrices, we arrive at concise formulas for the deflated variance that include only the variance and mean of the singular values. We make the remarkable observation that deflation may increase variance for Hermitian matrices but not for non-Hermitian ones. This is a rare, if not unique, property where non-Hermitian matrices outperform Hermitian ones. The theory can be used as a model for predicting the benefits of deflation. Second, we use deflation in the context of a large scale application of "disconnected diagrams" in Lattice QCD. On lattices, Hierarchical Probing (HP) has previously provided an order of magnitude of variance reduction over MC by removing "error" from neighboring nodes of increasing distance in the lattice. Although deflation used directly on MC yields a limited improvement of 30% in our problem, when combined with HP they reduce variance by a factor of over 150 compared to MC. For this, we pre-computated 1000 smallest singular values of an ill-conditioned matrix of size 25 million. Furthermore, using PRIMME and a domain-specific Algebraic Multigrid preconditioner, we perform one of the largest eigenvalue computations in Lattice QCD at a fraction of the cost of our trace computation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An Algebraic Multilevel Preconditioner with Low-Rank Corrections for Sparse Symmetric Matrices

In this work, we describe a multilevel preconditioning technique for solving sparse symmetric linear systems of equations. This “Multilevel Schur Low-Rank” (MSLR) preconditioner first builds a tree structure $\mathcal{T}$ based on a hierarchical decomposition of the matrix and then computes an approximate inverse of the original matrix level by level. Unlike classical direct solvers, the construction of the MSLR preconditioner follows a top-down traversal of $\mathcal{T}$ and exploits a low-rank property that is satisfied by the difference between the inverses of the local Schur complements and specific blocks of the original matrix. A few steps of the generalized Lanczos tridiagonalization procedure are applied to capture most of this difference. Numerical results are reported to illustrate the efficiency and robustness of the MSLR preconditioner with both two- and three-dimensional discretized PDE problems and with publicly available test problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software (Extended Analysis)

Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A Multiple Lines of Evidence Approach for Conceptual Site Models in Performance Assessments - 19070

Demonstration of the long-term performance of remediated radioactive waste sites relies on models that evaluate vadose zone and groundwater flow and contaminant transport for thousands of years. Model qualification, verification, and comparisons with data are essential elements to demonstrating the accuracy of long-term performance assessments (PAs). However, comparisons with data are generally limited due to incomplete characterization data and long-time scales. Since PAs support decision making to assure long-term protection of both human health and the environment, a conceptual site model (CSM) is needed to explain the hydrodynamic behavior and account for subsurface heterogeneities in sufficient detail to support confident decisions. In the case of the Waste Management Area (WMA) C PA, where the focus of the effort is on characterizing flow and transport through a thick vadose zone, site-specific data are sparse and larger-scale geologic descriptions are adopted with the risk of losing small scale features that may affect contaminant transport. Features such as horizontal bedding and sloping interfaces between contrasting material types, as well as vertically oriented features such as clastic dikes, may be difficult to delineate from sparse characterization data, but may affect the extent to which contaminants are transported. The PA conducted to identify long-term impacts associated with the closure of the single-shell tank (SST) farm at WMA C adopted an equivalent homogenous medium approach to represent the major stratigraphic units at the site. This approach explicitly represents major stratigraphic units but assigns hydraulic properties that implicitly account for the presence of small-scale heterogeneities and their impact on flow and contaminant transport to the groundwater. The objective of this study is to evaluate the available site-specific and analogue data that can be used to characterize heterogeneity and to assess its impact on fate and transport of contaminants that may lead to different conclusions when compared to the results of the equivalent homogeneous media approach. To this end, a multiple lines of evidence approach was used to construct alternative CSMs based on an evaluation of several types of data. These data include 1) general characteristics and features of the major stratigraphic units from historical descriptions; 2) borehole geologic logs; 3) particle size distribution measurements; 4) moisture content measurements; 5) spectral gamma data; and 6) concentrations of key constituents (i.e., Tc-99) in both the vadose zone and groundwater impacted by past releases. Based on an analysis of the available data, lines of evidence are evaluated for consistency and integrated in a consistent manner into scientifically defensible CSMs. Numerical models are then used to evaluate the impact that the explicit representation of heterogeneities has on contaminant transport. Together, the field data and the numerical modeling results indicate that heterogeneity caused by the small-scale features do not have a significant (i.e. greater than a factor of 2) impact on transport below WMA C relative to peak concentrations and arrival times for potential releases from waste tanks. The results of this study underscore the value of using multiple lines of evidence based on the quantity and quality of available data in developing alternative CSMs and evaluating their relative impact on key transport metrics such as arrival times and peak concentration. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

ASTEROID LIGHT CURVES FROM THE PALOMAR TRANSIENT FACTORY SURVEY: ROTATION PERIODS AND PHASE FUNCTIONS FROM SPARSE PHOTOMETRY

We fit 54,296 sparsely sampled asteroid light curves in the Palomar Transient Factory survey to a combined rotation plus phase-function model. Each light curve consists of 20 or more observations acquired in a single opposition. Using 805 asteroids in our sample that have reference periods in the literature, we find that the reliability of our fitted periods is a complicated function of the period, amplitude, apparent magnitude, and other light-curve attributes. Using the 805-asteroid ground-truth sample, we train an automated classifier to estimate (along with manual inspection) the validity of the remaining ∼53,000 fitted periods. By this method we find that 9033 of our light curves (of ∼8300 unique asteroids) have “reliable” periods. Subsequent consideration of asteroids with multiple light-curve fits indicates a 4% contamination in these “reliable” periods. For 3902 light curves with sufficient phase-angle coverage and either a reliable fit period or low amplitude, we examine the distribution of several phase-function parameters, none of which are bimodal though all correlate with the bond albedo and with visible-band colors. Comparing the theoretical maximal spin rate of a fluid body with our amplitude versus spin-rate distribution suggests that, if held together only by self-gravity, most asteroids are in general less dense than ∼2 g cm{sup −3}, while C types have a lower limit of between 1 and 2 g cm{sup −3}. These results are in agreement with previous density estimates. For 5–20 km diameters, S types rotate faster and have lower amplitudes than C types. If both populations share the same angular momentum, this may indicate the two types’ differing ability to deform under rotational stress. Lastly, we compare our absolute magnitudes (and apparent-magnitude residuals) to those of the Minor Planet Center’s nominal (G = 0.15, rotation-neglecting) model; our phase-function plus Fourier-series fitting reduces asteroid photometric rms scatter by a factor of ∼3.

79 ASTRONOMY AND ASTROPHYSICS↗