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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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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fuel/Basket Degradation Modeling Summary

This report documents the activities in a preliminary phase of development for three models: 1) waste package breach model, 2) fuel/basket degradation model, and 3) dual-purpose canister (DPC) crush model. The waste package breach model describes the coupling of mass flow, heat transport, and canister shell deformation in response to a heat-generating (criticality) event. The fuel/basket degradation model describes potential weakening and disaggregation of the DPC structure from corrosion, possibly accelerated by seismic ground motion. Progressively degraded three-dimensional (3D) configurations of the fuel, basket, and shell are generated for future analysis of reactivity (with as-loaded DPC fuel characteristics). Another important application for the fuel/basket degradation model is validation of the two stylized degradation cases currently being used by other investigators for analysis of the as-loaded DPC inventory under disposal conditions. The DPC crush model investigates stability of a typical DPC after breach of the disposal overpack allows fluids from the repository near-field environment to penetrate and externally pressurize the canister shell. Preliminary results show that large deformation of the DPC could occur for external pressure on the order of 10 to 15 MPa, or the shell could be stable (not collapse) with pressure of 20 MPa or greater if the basket plates are fully welded at the connections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

NWB Sensors Infrared Cloud Imager Data Products from SGP

NWB Sensors is a company which has developed a commercially available Infrared Cloud Imager (ICI). For more information, consult the company's webpage, https://www.nwbsensors.com/infrared-cloud-imager. To validate the radiometric accuracy of the ICI, NWB Sensors deployed it to the ARM SGP User Facility in 2023. The primary motivation of this deployment was to perform an intercomparison between the ICI and the Atmospheric Emitted Radiance Interferometer (AERI). The AERI spectral radiance data product can be integrated across the response function of the ICI and directly compared to the zenith radiance observed by the ICI. In addition, the ICI uses proprietary models of the downwelling clear-sky radiance in its cloud processing algorithms. They are based on surface meteorology and precipitable water vapor (PWV). These models were validated by comparing their predicted radiances to those derived from radiative transfer models of the ARM radiosondes. Finally, PWV observations derived from the ICI's onboard GNSS-based PWV retrieval system were compared against those from the microwave radiometer. This dataset contains the ICI radiance and cloud data products.

Atmosphere↗
DOE OSTI2024

Identification of Defects and the Origins of Surface Noise on Hydrogen–Terminated (100) Diamond

Near-surface nitrogen vacancy centres are critical to many diamond-based quantum technologies such as information processors and nanosensors. Surface defects play an important role in the design and performance of these devices. The targeted creation of defects is central to proposed bottom-up approaches to nanofabrication of quantum diamond processors, and uncontrolled surface defects may generate noise and charge trapping which degrade shallow NV device performance. Surface preparation protocols may be able to control the production of desired defects and eliminate unwanted defects, but only if their atomic structure can first be conclusively identified. This work uses a combination of scanning tunnelling microscopy (STM) imaging and first-principles simulations to identify several surface defects on H:C(100)—2 × 1 surfaces prepared using chemical vapour deposition (CVD). The atomic structure of these defects is elucidated, from which the microscopic origins of magnetic noise and charge trapping are determined based on the modeling of their paramagnetic properties and acceptor states. Rudimentary control of these deleterious properties is demonstrated through STM tip-induced manipulation of the defect structure. Furthermore, the results validate accepted models for CVD diamond growth by identifying key adsorbates responsible for the nucleation of new layers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

NO x adsorption with CO and C 2 H 4 on Pd/SSZ-13: Experiments and modeling

A transient monolith model containing microkinetic schemes for NO uptake and release over Pd/SSZ-13 without and with reductants CO or C2H4 is presented. The scheme involves three cationic Pd species (Z – [PdOH] + , Z – Pd 2+ Z – , Z – Pd + ) as the active sites during uptake of NO, CO and C 2 H 4 , and their desorption and conversion at higher temperature. Kinetic parameters are estimated through a combination of density functional theory (DFT) estimates and a fit of uptake, desorption and conversion data. The tuned model is validated at different uptake temperatures, ramp rates, and flowrates. A “degree of uptake control” parameter is defined that helps to identify the step(s) that are uptake controlling. The model helps to interpret the data features and is used to identify operating conditions to meet application-relevant performance metrics, including NO trapping efficiency and NO release temperature. Furthermore, the model demonstrates higher NO uptake on Pd/SSZ-13 compared to Pd/ZSM-5.

42 ENGINEERING↗
DOE OSTI2022

Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential

Multi-shell fullerenes ”buckyonions ” were simulated, starting from initially random configurations, using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 ~ 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into the gallery. The energies of the models were validated ex post facto using density functional codes, VASP and SIESTA , revealing an energy difference within the range of 0.02 - 0.08 eV/atom after conjugate gradient energy convergence of the models was achieved with both methods.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Energy use of piperazine with the advanced stripper from pilot plant testing

Here, piperazine with the Advanced Stripper (PZAS™) was tested at the National Carbon Capture Center (NCCC) at coal and NGCC conditions. Energy performance was measured with accounting for heat loss. The net heat duty was found to be identical at 2.45 GJ/tonne for both 4 and 12% CO 2 at 90% CO 2 removal. The average measured heat loss was 0.38 GJ/tonne and represented 30% of the gross heat rate. For PZAS, the warm rich piping before the control valve is the largest in surface area. Heat loss at this location increased heat duty by 80% of the heat loss value. Therefore, the total heat duty was decreased by 80% of the heat loss. The Independence™ model was validated using 112 steady-state pilot plant runs and predicted average net heat duty with an error of 9% and lean loading with an error of 10%. The model was used to simulate process impacts of heat loss for the PZAS and simple stripper configurations. For both strippers, heat loss was found to impact heat duty at locations other than just the steam heater. While heat loss in the steam heater was the most important for PZAS, heat loss in the column was the most important for the simple stripper due to a large top-side pinch and CO 2 reabsorption at the same location. CO 2 reabsorption in PZAS occurred only at high heat loss values (~0.14 GJ/hr), compared to low heat loss values (~ 0.03 GJ/hr) in the PZ-simple stripper. Excessive packing in the PZ-simple stripper caused a large top-side pinch at 0.23 mol/mol lean loading, making the column very sensitive to heat loss. Heat loss in the PZ-simple stripper increased heat duty by 120–150% at typical heat loss values and lean loading of 0.23 mol/mol. Heat loss in the MEA-simple stripper only increased heat duty by 1–13% in a typical lean loading range of 0.1–0.2 mol/mol. A large bottom side pinch at low lean loading for 7 m MEA made the effect of heat loss less pronounced.

42 ENGINEERING↗
DOE OSTI2020

Development of a high temperature pyrolysis mechanism for cyclopentanone, a potential biofuel derived from biomass

Cyclopentanone (CPO) is a promising biofuel for spark-ignition engines due to its ring strain and high auto-ignition resistance. The Department of Energy has identified CPO as a potential biofuel candidate because of its unique chemical properties. Understanding CPO decomposition is crucial to building a high-temperature combustion model. In this study, we present a comprehensive kinetic model for high-temperature pyrolysis of CPO with verified results from high-pressure shock tube (HPST) measurements. The time histories of carbon monoxide (CO), ethylene (C2H4), and CPO absorbances over the temperature range of 1156-1416 K and 8.53-10.06 atm were measured. A corresponding kinetic model was generated using the Reaction Mechanism Generator (RMG) with the parameters of dominant pathways from either previous studies or quantum calculation within current work. The final model containing 821 species and 79,859 reactions exhibited a good agreement with the experimental results. In this study, the C2H4/CO ratio was used as an essential factor to validate the model and to prove that radical-involved bimolecular pathways were as significant as unimolecular decomposition of CPO. The rate of production (ROP) analysis showed that H radicals play major roles in both primary reactions and product pyrolysis. The insights from this work can be used to generate a better CPO combustion model and help evaluate CPO as a biofuel. Such understanding can also be helpful when proposing pyrolysis or combustion models for other biofuels.

cyclopentanone, reaction mechanism, pyrolysis, sho↗
DOE OSTI2026

Synthetic modeling of soft x-ray emissivity for magnetic island analysis in LTX-β

We present a synthetic soft x-ray (SXR) forward-modeling framework that characterizes the emissivity structure of rotating magnetic islands in the Lithium Tokamak eXperiment-β across space and time. Magnetic islands associated with tearing modes produce modulations in line-integrated SXR brightness. Traditional tomographic methods struggle to resolve these structures in devices with limited sightlines, resulting in an under-determined inversion problem, or otherwise necessitate equilibrium reconstruction data, preventing their use in active control. Our approach removes this challenge by combining a fully three-dimensional ray-tracing model with a time-dependent emissivity prescription of a $m/n$ = 2/1 magnetic island. The model is validated through helical island geometry, rotation measured by magnetic diagnostics, and equilibrium constraints from PSI-Tri reconstructions. Synthetic brightness signals are generated for each photodiode sightline from a modeled emissivity profile and directly compared with experimental data from a tangential midplane SXR array. By fitting the synthetic diagnostic output to the observed brightness across time, we simultaneously infer key geometric parameters-such as island radial location, island width, and rotation frequency-without requiring full tomographic reconstruction, all with an average absolute deviation of less than 3%. This work demonstrates that forward modeling with a single tangential array can extract key island parameters in a spherical tokamak, providing a computationally efficient alternative to conventional SXR tomography and providing a pathway toward real-time magnetic-island characterization in future devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

A New Impedance-Based Modeling and Stability Analysis Approach for Power Oscillations Between Grid-Forming Inverters

This paper presents a new modeling and analysis approach to address the small-signal stability issues among interconnected grid-forming (GFM) inverters. A novel phasor domain “power impedance” model is developed to capture the terminal characteristics of GFM inverters. Based on the developed models, a sum-type impedance stability criterion is proposed to analyze the power oscillations between GFM Inverters. The developed models are validated with EMT simulations and the stability analysis results using the proposed stability criterion can match both EMT simulations and eigenvalue analysis. In addition, the application of the developed model can be extended to the stability analysis of large-scale power system with high penetration of GFM inverters.

Liu, Hanchao↗
DOE OSTI2022

Melting Behavior of Conventional and Hydrogen-Reduced DRI in a Simulated EAF Steel Bath

The melting behavior of carbon-free direct reduced iron (DRI) produced by hydrogen reduction has been investigated in a simulated electric arc furnace (EAF) melting environment. A testing apparatus was created to document the melting rates under various bath conditions in slag and steel. This investigation is specific to pellets melted in a steel bath. A model was developed to simulate the shell growth and melting rate of a non-reactive DRI pellet in molten steel. Data acquired from preliminary experiments are utilized to validate the model. This investigation sheds light on the potential effects of melting hydrogen-reduced DRI in the EAF.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bayesian optimal experimental design for constitutive model calibration

Computational simulation is increasingly relied upon for high/consequence engineering decisions, which necessitates a high confidence in the calibration of and predictions from complex material models. However, the calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure—Interlaced Characterization and Calibration (ICC)—is introduced and demonstrated. Further, this framework leverages Bayesian optimal experimental design (BOED), which creates a line of communication between model calibration needs and data collection capabilities in order to optimize the information content gathered from the experiments for model calibration. Eventually, the ICC framework will be used in quasi real-time to actively control experiments of complex specimens for the calibration of a high-fidelity material model. This work presents the critical first piece of algorithm development and a demonstration in determining the optimal load path of a cruciform specimen with simulated data. Calibration results, obtained via Bayesian inference, from the integrated ICC approach are compared to calibrations performed by choosing the load path a priori based on human intuition, as is traditionally done. The calibration results are communicated through parameter uncertainties which are propagated to the model output space (i.e. stress–strain). In these exemplar problems, data generated within the ICC framework resulted in calibrated model parameters with reduced measures of uncertainty compared to the traditional approaches.

42 ENGINEERING↗
DOE OSTI2023

Mapping foliar photosynthetic capacity in sub-tropical and tropical forests with UAS-based imaging spectroscopy: Scaling from leaf to canopy

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max ). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25 ) traits (n=135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. Here we further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 =0.55, nRMSE=11.79%), Jmax (R 2 =0.54, nRMSE=12.34%), and five additional foliar traits (R 2 =0.38-0.60, nRMSE=10.11-13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectratrait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗
DOE OSTI2025

A theoretical model for sheath dynamics in the two-plasma mode of a cylindrical filament discharge

We present a refined understanding of cylindrical plasma-facing emitter cathodes by examining the limitations of the conventional space charge limited model, which overlooks ion trapping dynamics within the virtual cathode. While conventional theory distinguishes between temperature-limited and space charge limited regimes based on electron current constraints, our findings reveal that trapped ions can form a quasi-neutral “second plasma,” expanding upstream and interacting with the primary plasma. In cylindrical and other non-planar geometries, an expanding trapped-ions layer produces current enhancement by converting more of the emitted electron flux to passing flux. The current enhancement leads to a complex coupling between the trapped-ions plasma and upstream plasma through ionizing collisions in the upstream region and charge exchange collisions in the virtual cathode. The coupling results in mutual density enhancement and at the same time, antagonistic expansion dynamics between the two plasma layers. The process is understood using a new “aid-compete” model. Numerical solutions, validated against simulations, reveal the model's capacity to predict system evolution and complex discharge behaviors, albeit with qualitative approximations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Fisher for complements: extracting cosmology and neutrino mass from the counts-in-cells PDF

In this work, we comprehensively analyse the cosmology dependence of counts-in-cells statistics. Furthermore, we focus on the shape of the one-point probability distribution function (PDF) of the matter density field at mildly non-linear scales. Based on large-deviation statistics, we parametrize the cosmology dependence of the matter PDF in terms of the linear power spectrum, the growth factor, the spherical collapse dynamics, and the non-linear variance. We extend our formalism to include massive neutrinos, finding that the total matter PDF is highly sensitive to the total neutrino mass Mν and can disentangle it from the clustering amplitude σ 8 . Additionally, using more than a million PDFs extracted from the Quijote simulations, we determine the response of the matter PDF to changing parameters in the νΛCDM model and successfully cross-validate the theoretical model and the simulation measurements. We present the first νΛCDM Fisher forecast for the matter PDF at multiple scales and redshifts, and its combination with the matter power spectrum. We establish that the matter PDF and the matter power spectrum are highly complementary at mildly non-linear scales. The matter PDF is particularly powerful for constraining the matter density Ωm, clustering amplitude σ 8 and the total neutrino mass Mν. Adding the mildly non-linear matter PDF to the mildly non-linear matter power spectrum improves constraints on Ω m by a factor of 5 and σ 8 by a factor of 2 when considering the three lowest redshifts. In our joint analysis of the matter PDF and matter power spectrum at three redshifts, the total neutrino mass is constrained to better than 0.01 eV with a total volume of 6 (Gpc h -1 ) 3 . We discuss how density-split statistics can be used to translate those encouraging results for the matter PDF into realistic observables in galaxy surveys.

79 ASTRONOMY AND ASTROPHYSICS↗