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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

GW-PINN: A deep learning algorithm for solving groundwater flow equations

Machine learning methods provide new perspective for more convenient and efficient prediction of groundwater flow. In this study, a deep learning method “GW-PINN” without labeled data for solving groundwater flow equations with wells was proposed. GW-PINN takes the physics inform neural network (PINN) as the backbone and uses either the hard or soft constraint in the loss function for training. A locally refined sampling strategy (LRS) is adopted to generate the consistent spatial sampling points for problems with strong hydraulic head change, and then combined with an appropriate temporal sampling scheme to obtain the final spatial-temporal sampling points. A snowball-style two-stage training strategy by dividing the temporal domain into two subdomains is designed to decrease the sampling points. Five cases were designed to test the training performance of GW-PINN under different sampling strategies and two constraints. The predicted results of GW-PINN were compared with MODFLOW and the analytical solution. The results demonstrate that GW-PINN possesses strong ability in capturing the hydraulic head change for both confined and un-confined aquifers. The hard constraint owns more robust learning ability than the soft constraint. The LRS strategy can generate more accurate results with much fewer sampling points than traditional sampling strategies, and the snowball-style two-stage training strategy is significantly efficient for problems with the drastic change of hydraulic head. Additionally, the application of GW-PINN as a surrogate model for parameterized groundwater flow equations is illustrated. This study provides an option tool for efficient groundwater flow simulation, especially for those with local refinements are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Meso-scale Instability Triggered by Dust Feedback in Dusty Rings: Origin and Observational Implications

High spatial resolution observations of protoplanetary disks by ALMA have revealed many substructures that are providing interesting constraints on disk physics as well as dust dynamics, both of which are essential for understanding planet formation. We carry out high-resolution, 2D global hydrodynamic simulations, including the effects of dust feedback, to study the stability of dusty rings. When the ring edges are relatively sharp and the dust surface density becomes comparable to the gas surface density, we find that dust feedback enhances the radial gradients of both the azimuthal velocity profile and the potential vorticity profile at the ring edges. This eventually leads to instabilities on meso-scales (spatial scales of several disk scale heights), causing dusty rings to be populated with many compact regions with highly concentrated dust densities. We also produce synthetic dust emission images using our simulation results and discuss the comparison between simulations and observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Seismic Observations of the OSIRIS-REx Sample Return Capsule Reentry: Deployment, Signal Characteristics, and Wavefield Phenomenology

Controlled spacecraft reentries from interplanetary trajectories provide rare, well-characterized hypersonic sources for advancing seismoacoustic observation techniques. Here we present seismic observations of the OSIRIS-REx sample return capsule (SRC) reentry on 24 September 2023, recorded by 16 three-component nodal seismometers deployed near Eureka, Nevada, at ground distances of 7–20 km from the capsule trajectory. Air-to-ground coupled signals are detected at all stations, exhibiting impulsive onsets consistent with ballistic shock arrivals from the descending Mach cone. We characterize the seismic wavefield through signal amplitude, period, waveform cross-correlation, and array processing. Signal periods decrease systematically with increasing distance from the trajectory within the airport array, indicating that higher-frequency content becomes more prominent at greater offsets, opposite to expectations from geometric spreading and atmospheric absorption. Seismic array processing identifies frequency-dependent back-azimuth variations whose origin remains unresolved; possible contributing factors include source geometry, scattering by fine-scale layered structure in the stratosphere, and near-surface effects. These observations document a spatially complex seismic wavefield from a well-characterized hypersonic line source and provide constraints for future modeling of atmospheric propagation and air-to-ground coupling.

OSIRIS-REx↗
DOE OSTI2024

First Sagittarius A* Event Horizon Telescope Results. VIII. Physical Interpretation of the Polarized Ring

Abstract In a companion paper, we present the first spatially resolved polarized image of Sagittarius A* on event horizon scales, captured using the Event Horizon Telescope, a global very long baseline interferometric array operating at a wavelength of 1.3 mm. Here we interpret this image using both simple analytic models and numerical general relativistic magnetohydrodynamic (GRMHD) simulations. The large spatially resolved linear polarization fraction (24%–28%, peaking at ∼40%) is the most stringent constraint on parameter space, disfavoring models that are too Faraday depolarized. Similar to our studies of M87*, polarimetric constraints reinforce a preference for GRMHD models with dynamically important magnetic fields. Although the spiral morphology of the polarization pattern is known to constrain the spin and inclination angle, the time-variable rotation measure (RM) of Sgr A* (equivalent to ≈46° ± 12° rotation at 228 GHz) limits its present utility as a constraint. If we attribute the RM to internal Faraday rotation, then the motion of accreting material is inferred to be counterclockwise, contrary to inferences based on historical polarized flares, and no model satisfies all polarimetric and total intensity constraints. On the other hand, if we attribute the mean RM to an external Faraday screen, then the motion of accreting material is inferred to be clockwise, and one model passes all applied total intensity and polarimetric constraints: a model with strong magnetic fields, a spin parameter of 0.94, and an inclination of 150°. We discuss how future 345 GHz and dynamical imaging will mitigate our present uncertainties and provide additional constraints on the black hole and its accretion flow.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Towards constraining cosmological parameters with SPT-3G observations of 25% of the sky

The South Pole Telescope (SPT), using its third-generation camera, SPT-3G, is conducting observations of the cosmic microwave background (CMB) in temperature and polarization across approximately 10 000 deg$^2$ of the sky at 95, 150, and 220 GHz. This comprehensive dataset should yield stringent constraints on cosmological parameters. In this work, we explore its potential to address the Hubble tension by forecasting constraints from temperature, polarization, and CMB lensing on early dark energy (EDE) and the variation in electron mass in spatially flat and curved universes. For this purpose, we investigate first whether analyzing the distinct SPT-3G observation fields independently, as opposed to as a single, unified region, results in a loss of information relevant to cosmological parameter estimation. We develop a realistic temperature and polarization likelihood pipeline capable of analyzing these fields in these two ways, and subsequently forecast constraints on cosmological parameters. Our findings indicate that any loss of constraining power from analyzing the fields separately is primarily concentrated at low multipoles ($\ell$ < 50) and the overall impact on the relative uncertainty on standard $Λ$ cold dark matter parameters is minimal (< 3%). Our forecasts suggest that SPT-3G data should improve by more than a factor of 90 and 190 the figure of merit of the EDE and the varying electron mass models, respectively, when combined with Planck data. The likelihood pipeline developed and used in this work is made publicly available online.

Vitrier, A. [Paris, Inst. Astrophys.]↗
DOE OSTI2021

Self-consistent, high-order spatial profiles in a model for two-fluid turbulent mixing

A Reynolds-averaged Navier-Stokes model is presented with the property that it admits self-consistent, high-order spatial profiles in simulations of two-fluid turbulent mixing layers. Whereas previous models have been limited by the assumption of a linear mixing profile, the present paper relaxes this assumption and, as a result, is shown to achieve much better agreement with experimental profiles. Similarity analysis is presented to derive constraints on model coefficients to enforce desired self-similar growth rates that are fully consistent with the high-order spatial profiles. Through this similarity analysis, it is shown that care must be taken in model construction, as it is possible to construct certain terms in such a way as to leave growth rates unconstrained. This model, termed the k-Φ-L-a-V model, is then applied in simulations of Rayleigh-Taylor, Richtmyer-Meshkov, and Kelvin-Helmholtz mixing layers. Overall, these simulations confirm that the expected growth parameters are recovered and high-order spatial profiles are maintained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

02 PETROLEUM↗
DOE OSTI2023

Expanded modelling scenarios to understand the role of offshore wind in decarbonizing the United States

An assessment of decarbonization pathways in energy models reveals fundamental limitations in representing factors that are relevant for practical decision-making. Although these modelling limitations are widely acknowledged, their impact on the deployment of individual power generation types is not well understood. As a result, the societal value from such generation types could be vastly misrepresented. Here we explore a wide spectrum of factors that impact offshore wind deployment in the United States using a detailed capacity expansion model. Many factors prescribe a large future role for offshore wind, yet this diverges from what models often show. We extend the typically narrow modelling context through high spatial resolution, several cost and transmission possibilities and various energy-sector policies. Further, we estimate offshore wind to constitute 1-8% (31-256 gigawatts) of total US generation by 2050. This wide range suggests an uncertain but potentially important regional role. Our expansive scenarios demonstrate how to address many limitations of decarbonization modelling.

17 WIND ENERGY↗
DOE OSTI2024

Divergent urban land trajectories under alternative population projections within the Shared Socioeconomic Pathways

Population change is a main driver behind global environmental change, including urban land expansion. In future scenario modeling, assumptions regarding how populations will change locally, despite identical global constraints of Shared Socioeconomic Pathways (SSPs), can have dramatic effects on subsequent regional urbanization. Using a spatial modeling experiment at high resolution (1 km), this study compared how two alternative US population projections, varying in the spatially explicit nature of demographic patterns and migration, affect urban land dynamics simulated by the Spatially Explicit, Long-term, Empirical City development (SELECT) model for SSP2, SSP3, and SSP5. The population projections included: (1) newer downscaled state-specific population (SP) projections inclusive of updated international and domestic migration estimates, and (2) prevailing downscaled national-level projections (NP) agnostic to localized demographic processes. Our work shows that alternative population inputs, even those under the same SSP, can lead to dramatic and complex differences in urban land outcomes. Under the SP projection, urbanization displays more of an extensification pattern compared to the NP projection. This suggests that recent demographic information supports more extreme urban extensification and land pressures on existing rural areas in the US than previously anticipated. Urban land outcomes to population inputs were spatially variable where areas in close spatial proximity showed divergent patterns, reflective of the spatially complex urbanization processes that can be accommodated in SELECT. Although different population projections and assumptions led to divergent outcomes, urban land development is not a linear product of population change but the result of complex relationships between population, dynamic urbanization processes, stages of urban development maturity, and feedback mechanisms. These findings highlight the importance of accounting for spatial variations in the population projections, but also urbanization process to accurately project long-term urban land patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Current data are consistent with flat spatial hypersurfaces in the Λ CDM cosmological model but favor more lensing than the model predicts

Here, we study the performance of three pairs of tilted, and a pair of untilited, ΛCDM cosmological models, with three of these four pairs allowing for non-flat spatial hypersurfaces, against cosmic microwave background (CMB) temperature and polarization power spectrum data (P18), measurements of the Planck 2018 lensing potential power spectrum (lensing), and a large compilation of non-CMB data (non-CMB). For the eight models, we measure cosmological parameters and study whether or not pairs of the data sets (as well as subsets of them) are mutually consistent in these models. Half of these models allow the lensing consistency parameter A L , which re-scales the gravitational potential power spectrum, to be an additional free parameter to be determined from data, while the other three have A L = 1 which is the theoretically expected value. The pair of untilted non-flat ΛCDM models are incompatible with P18 data. The tilted spatially-flat models assume the usual primordial spatial inhomogeneity power spectrum that is a power law in wave number. The tilted non-flat models assume either the primordial power spectrum used in the Planck group anal yses [Planck P(q)], that has recently been numerically shown to be a good approximation to what is quantum-mechanically generated from a particular choice of closed inflation model initial conditions, or a recently computed power spectrum [new P(q)] that quantum-mechanically follows from a different set of non-flat inflation model initial conditions. In the tilted non-flat models with A L = 1 we find differences between P18 data and non-CMB data cosmological parameter constraints, which are large enough to rule out the Planck P(q) model at 3σ but not the new P(q) model. No significant differences are found when cosmological parameter constraints obtained with two different data sets are compared within the standard tilted flat ΛCDM model. While both P18 data and non-CMB data separately favor a closed geometry, with spatial curvature density parameter Ω k < 0, when P18+non-CMB data are jointly analyzed the evidence in favor of non-flat hypersurfaces subsides. Differences between P18 data and non-CMB data cosmological constraints subside when A L is allowed to vary. From the most restrictive P18+lensing+non-CMB data combination we get almost model-independent constraints on the cosmological parameters and find that the A L > 1 option is preferred over the Ω k < 0 one, with the A L parameter, for all models, being larger than unity by ~ 2.5σ. According to the deviance information criterion, in the P18+lensing+non-CMB analysis, the varying A L option is on the verge of being strongly favored over the A L = 1 one, which could indicate a problem for the standard tilted flat ΛCDM model. These data are consistent with flat spatial hypersurfaces but more and better data could improve the constraints on Ω k and might alter this conclusion. Error bars on some cosmological parameters are significantly reduced when non-CMB data are used jointly with P18+lensing data. For example, in the tilted flat ΛCDM model for P18+lensing+non-CMB data the Hubble constant H 0 = 68.09 ± 0.38 km s -1 Mpc -1 , which is consistent with that from a median statistics analysis of a large compilation of H 0 measurements as well as with a number of local measurements of the cosmological expansion rate. This H 0 error bar is 31% smaller than that from P18+lensing data alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

How fast can protons decay?

Current laboratory bounds imply that protons are extremely long-lived. However, this conclusion may not hold for all time and in all of space. We find that the proton lifetime can be ∼ 15 orders of magnitude shorter in the relatively recent past on Earth or at the present time elsewhere in the Milky Way. A number of terrestrial and astrophysical constraints are examined and potential signals are outlined. We also sketch possible models that could lead to spatial or temporal variations in the proton lifetime. A positive signal could be compelling evidence for a new long range force of nature, with important implications for the limitations of fundamental inferences based solely on laboratory measurements. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time-of-flight matrix-assisted laser desorption/ionization, and time-of-flight secondary ion mass spectrometry imaging techniques. By leveraging FT-ICR's unparalleled spectral resolution and ToF-SIMS's exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

canonical correlation analysis↗
DOE OSTI2023

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks

Gigapixel images are prevalent in scientific domains ranging from remote sensing, and satellite imagery to microscopy, etc. However, training a deep learning model at the natural resolution of those images has been a challenge in terms of both, overcoming the resource limit (e.g. HBM memory constraints), as well as scaling up to a large number of GPUs. In this paper, we trained Residual neural Networks (ResNet) on 22,528 x 22,528-pixel size images using a distributed spatial decomposition method on 2,304 GPUs on the Summit Supercomputer. We applied our method on a Whole Slide Imaging (WSI) dataset from The Cancer Genome Atlas (TCGA) database. WSI images can be in the size of 100,000 x 100,000 pixels or even larger, and in this work we studied the effect of image resolution on a classification task, while achieving state-of-the-art AUC scores. Moreover, our approach doesn't need pixel-level labels, since we're avoiding patching from the WSI images completely, while adding the capability of training arbitrary large-size images. This is achieved through a distributed spatial decomposition method, by leveraging the non-block fat-tree interconnect network of the Summit architecture, which enabled GPU-to-GPU direct communication. Finally, detailed performance analysis results are shown, as well as a comparison with a data-parallel approach when possible.

Tsaris, Aristeidis (aris)↗
DOE OSTI2021

Milky Way Satellite Census. III. Constraints on Dark Matter Properties from Observations of Milky Way Satellite Galaxies

We perform a comprehensive study of Milky Way (MW) satellite galaxies to constrain the fundamental properties of dark matter (DM). This analysis fully incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and marginalizes over uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk. Our results are consistent with the cold, collisionless DM paradigm and yield the strongest cosmological constraints to date on particle models of warm, interacting, and fuzzy dark matter. At $95\%$ confidence, we report limits on (i) the mass of thermal relic warm DM, $m_{\rm WDM} > 6.5\ \mathrm{keV}$ (free-streaming length, $\lambda_{\rm{fs}} \lesssim 10\,h^{-1}\ \mathrm{kpc}$), (ii) the velocity-independent DM-proton scattering cross section, $\sigma_{0} < 8.8\times 10^{-29}\ \mathrm{cm}^{2}$ for a $100\ \mathrm{MeV}$ DM particle mass (DM-proton coupling, $c_p \lesssim (0.3\ \mathrm{GeV})^{-2}$), and (iii) the mass of fuzzy DM, $m_{\phi}> 2.9 \times 10^{-21}\ \mathrm{eV}$ (de Broglie wavelength, $\lambda_{\rm{dB}} \lesssim 0.5\ \mathrm{kpc}$). These constraints are complementary to other observational and laboratory constraints on DM properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

GFiRe—Gauge Field integrator for Reheating

We present a new numerical algorithm and code, GFiRe, for solving the nonlinear evolution of Abelian gauge fields coupled to complex scalar fields in homogeneous and isotropic spacetimes. We adopt a hybrid approach to solving the system: the spatial derivatives are discretized using standard Lattice Gauge Field Theory techniques, whereas the time evolution of the fields and scale factor is implemented with explicit, composite, symplectic integrators. An important property of our compound algorithm is that the discretized Gauss constraint is respected exactly, regardless of the order of the symplectic integrator. This remains true even when the background expansion is computed “self-consistently”; that is, when the expansion history is computed using spatial averaged components of the energy momentum tensor in the simulation volume. Hence, our code can also be used in cases where the fields dominate the energy density of the universe, for example, during reheating after inflation. We test the algorithm in scenarios of reheating where the inflaton is a complex scalar field with a potential ∝(2|Φ| 2 - v 2 ) 2 and is coupled to an Abelian gauge field. Tracing the evolution of the system through complex dynamics (including resonant excitation of fields, backreaction, formation of solitons, and changes in the equation of state) in a self-consistently expanding universe, we find the energy conservation violation (<10 -4 ) to be very stable and the Gauss constraint violation (<10 -6 ) to be dominated by differencing noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Equation of state of neutron-rich matter in d -dimensions

Nuclear systems under constraints, with high degrees of symmetries and/or collectivities may be considered as moving effectively in spaces with reduced spatial dimensions. We first derive analytical expressions for the nucleon specific energy E 0 (ρ), pressure P 0 (ρ), incompressibility coefficient K 0 (ρ) and skewness coefficient J 0 (ρ) of symmetric nucleonic matter (SNM), the quadratic symmetry energy E sym (ρ), its slope parameter L(ρ) and curvature coefficient K sym (ρ) as well as the fourth-order symmetry energy E sym,4 (ρ) of neutron-rich matter in general d spatial dimensions (abbreviated as "dD") in terms of the isoscalar and isovector parts of the isospin-dependent single-nucleon potential according to the generalized Hugenholtz-Van Hove (HVH) theorem. The equation of state (EOS) of nuclear matter in dD can be linked to that in the conventional 3-dimensional (3D) space by the ε-expansion which is a perturbative approach successfully used previously in treating second-order phase transitions and related critical phenomena and more recently in studying the EOS of cold atoms. The ε-expansion of nuclear EOS in dD based on a reference dimension d f = d – ε is shown to be effective with –1 ≲ ε ≲ 1 starting from 1 ≲ d f ≲ 3 in comparison with the exact expressions derived using the HVH theorem. Moreover, the EOS of SNM (with/without considering its potential part) is found to be reduced (enhanced) in lower (higher) dimensions, indicating in particular that the many-nucleon system tends to be deeper bounded but saturate at higher densities in spaces with lower dimensions. Here, the links between the EOSs in 3D and dD spaces from the ε-expansion provide new perspectives to the EOS of neutron-rich matter.

79 ASTRONOMY AND ASTROPHYSICS↗