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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 19 records

A circumstellar molecular gas structure associated with the massive young star Cepheus A-HW 2

We report the detection via VLA-D observations of ammonia of a circumstellar high-density molecular gas structure toward the massive young star related to the object Cepheus A-HW 2, a firm candidate for the powering source of the high-velocity molecular outflow in the region. We suggest that the circumstellar molecular gas structure could be related to the circumstellar disk previously suggested from infrared, H2O, and OH maser observations. We consider as a plausible scenario that the double radio continuum source of HW 2 could represent the ionized inner part of the circumstellar disk, in the same way as proposed to explain the double radio source in L1551. The observed motions in the circumstellar molecular gas can be produced by bound motions (e.g., infall or rotation) around a central mass of about 10-20 solar masses (B0.5 V star or earlier).

Torrelles, Jose M.↗

HW Virginis - A short period eclipsing binary containing an sdB star

Simultaneous photometry of the binary star HW Vir, a detached system with an orbital period of 2 h 48 min is described. The new UBVR light curves obtained using the Wilson-Devinney code are found to constrain the orbital inclination of the system within the narrow range of 80.6 +/-0.2 deg. The temperature of the primary star is between 29,000 and 36,000 K, and the temperature of the secondary star is near 3,700 K. The possible masses and radii of the two stars are calculated using the published amplitude of the radial velocity curve of the primary star, 87.9 +/-4.8 km/s. It is found that, for the primary star log g1 is greater than 4.8 and less than or equal to 5.8. From the temperature and gravity of the primary star, its distance is found to be in the range between 42 and 151 pc. It is suggested that the system will begin mass transfer when the orbital period has decreased enough to bring the secondary star into contact with its Roche lobe.

Wood, Janet H.↗

Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operationson Spatial Accelerators

To meet the extreme compute demands for deep learning across commercial and scientific applications, dataflow accelerators are becoming increasingly popular. While these“domain-specific” accelerators are not fully programmable like CPUs and GPUs, they retain varying levels of flexibility with respect to data orchestration, i.e., dataflow and tiling optimizations to enhance efficiency. There are several challenges when designing new algorithms and mapping approaches to execute the algorithms for a target problem on new hardware. Previous works have addressed these challenges individually. To address this challenge as a whole, in this work, we present an HW-SW co-design ecosystem for spatial accelerators called Union within the popular MLIR compiler infrastructure. Our framework allows exploring different algorithms and their mappings on several accelerator cost models. Union also includes a plug-and-play library of accelerator cost models and mappers which can easily be extended. The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper. We demonstrate the value of Union for the community with several case studies which examine offloading different tensor operations (CONV/GEMM/Tensor Contraction) on diverse accelerator architectures using different mapping schemes.

Jeong, Geonhwa↗

Materials Data on HW by Materials Project

HW1 is Molybdenum Carbide MAX Phase-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. W is bonded in a 6-coordinate geometry to six equivalent H atoms. All W–H bond lengths are 2.08 Å. H is bonded to six equivalent W atoms to form a mixture of face, edge, and corner-sharing HW6 octahedra. The corner-sharing octahedral tilt angles are 48°.

36 MATERIALS SCIENCE↗

Near-term heatwave risk in HighResMIP models across different temperature zones of West Africa

This study projects near-future (2031–2050) changes in heatwave (HW) risk across West Africa (WA) using an ensemble of eight high-resolution global climate models from the High-Resolution Model Intercomparison Project under a high-emission scenario. Using K-means clustering, we divided WA into four unique temperature zones and examined projected changes in extreme temperatures, HW occurrence and magnitude. Our results indicate a statistically significant increase in future HW events across most parts of WA, although considerable spread exists over the region and among individual models. The most pronounced increases are evident in the Sahel/Sahara and the Guinea Highlands subregions, with an ensemble mean increase of ∼10 HW events per year. In contrast, the lowest increase in HW events is projected in central WA, with increases ranging between 1 and 5 events per year. Similarly, the magnitude of HW events is projected to increase in most models, with Sahel/Sahara exhibiting the largest increases. Additionally, projections suggest that the strongest HWs will become more frequent, particularly in northern and southwestern WA. These findings highlight significant spatial heterogeneity in future HW risk across WA, emphasizing the need for targeted adaptation strategies.

HighResMIP↗

Healthwatch-2 System Overview

Healthwatch-2 (HW-2) is a research tool designed to facilitate the development and testing of in-flight health monitoring algorithms. HW-2 software is written in C/C++ and executes on an x86-based computer running the Linux operating system. The executive module has interfaces for collecting various signal data, such as vibration, torque, tachometer, and GPS. It is designed to perform in-flight time or frequency averaging based on specifications defined in a user-supplied configuration file. Averaged data are then passed to a user-supplied algorithm written as a Matlab function. This allows researchers a convenient method for testing in-flight algorithms. In addition to its in-flight capabilities, HW-2 software is also capable of reading archived flight data and processing it as if collected in-flight. This allows algorithms to be developed and tested in the laboratory before being flown. Currently HW-2 has passed its checkout phase and is collecting data on a Bell OH-58C helicopter operated by the U.S. Army at NASA Ames Research Center.

Barszcz, Eric↗

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗

Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware

In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.

Mott insulators↗

Galaxy formation by dust

It has been known since the early 1940's that radiation can cause an instability in the interstellar medium. Absorbing dust particles in an isotropic radiation field shadow each other by a solid angle which is inversely proportional to the square of the distance between the two particles, leading to an inverse-square attractive force - mock gravity. The effect is largest in an optically thin medium. Recently Hogan and White (HW, hereafter) proposed that if the pre-galactic universe contained suitable sources of radiation and dust, instability in the dust distribution caused by mock gravity may have led to the formation of galaxies and galaxy clusters. In their picture of a well-coupled dust-gas medium, HW show that mock gravity begins to dominate gravitational instability when the perturbation becomes optically thin, provided that the radiation field at the time is strong enough. The recent rocket observation of the microwave background at submillimeter wavelengths by Matsumoto et al. might be from pre-galactic stars, the consequence of the absorption of ultraviolet radiation by dust, and infrared reemission which is subsequently redshifted. HW's analysis omits radiative drag, incomplete collisional coupling of gas and dust, finite dust albedo, and finite matter pressure. These effects could be important. In a preliminary calculation including them, the authors have confirmed that mock gravitational instability is effective if there is a strong ultraviolet radiation at the time, but any galaxies that form would be substantially enriched in heavy elements because the contraction of the dust is more rapid than that of the gas. Moreover, since the dust moves with supersonic velocity through the gas soon after the perturbation becomes optically thin, the sputtering of dust particles by gas is significant, so the dust could disappear before the instability develops significantly. They conclude that the mock gravity by dust is not important in galaxy formations.

Wang, Boqi↗

Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints

Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a ”physiological temporal fingerprint”, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states.

Napoli, Nicholas J.↗

Probing exotic charged Higgs decays in the Type-II 2HDM through top rich signal at a future 100 TeV pp collider

The exotic decay modes of non-Standard Model Higgs bosons are efficient in probing the hierarchical Two Higgs Doublet Models (2HDM). In particular, the decay mode H ± → HW ± serves as a powerful channel in searching for charged Higgses. In this paper, we analyze the reach for H ± → HW ± → $t\bar{t}W$ at a 100 TeV pp collider, and show that it extends the reach of the previously studied ττW final states once above the top threshold. Top tagging technique is used, in combination with a boosted decision tree classifier. At the low tan β region, almost the entire hierarchical Type-II 2HDM parameter space can be probed via the combination of all exotic decay channels.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

What Technical Choices Matter to Characterize Heat Wave and Cold Snap Events in Support of Bulk Power Grid Reliability Studies?

Extreme weather events, such as Heat Waves (HW) and Cold Snaps (CS), pose significant risks to the power grid. The United States (U.S.) Federal Energy Regulatory Commission Order No. 896 mandates regional coordination standards that account for extreme thermal events. However, the lack of a universal definition for extreme thermal events may lead to inconsistent compliance efforts among neighboring entities, undermining the reliability of the transmission system. This study directly addresses this challenge by systematically evaluating how varying technical choices in defining HW and CS fundamentally impact the characterization and ranking of extreme events for power grid reliability studies. We used 12 event definitions and multiple temperature spatial aggregation approaches to construct historical (1980–2024) regional extreme thermal event libraries across North American Electric Reliability Corporation (NERC) subregions in the conterminous U.S. We examined the sensitivity of event characteristics (e.g., duration, frequency, intensity, and spatial coverage) to different definitions. While some definitions produced similar libraries and top event rankings, definitions based on moving-window-averaged temperatures yielded markedly different characteristics. Spatial aggregation methods had minimal impact on heat wave or cold snap intensity, frequency and duration but significantly influenced spatial coverage. The top events identified across different aggregation methods were consistent, but their ranking order varied. These findings offer critical insights for characterizing and selecting extreme thermal events and for supporting local and cross-regional coordination as required by reliability standards.

Wan, Heng [Pacific Northwest National Laboratory (↗

A generative artificial intelligence framework for long-time plasma turbulence simulations

Generative deep learning techniques are employed in a novel framework for the construction of surrogate models capturing the spatiotemporal dynamics of 2D plasma turbulence. The proposed Generative Artificial Intelligence Turbulence (GAIT) framework enables the acceleration of turbulence simulations for long-time transport studies. GAIT leverages a convolutional variational auto-encoder and a recurrent neural network to generate new turbulence data from existing simulations, extending the time horizon of transport studies with minimal computational cost. The application of the GAIT framework to plasma turbulence using the Hasegawa–Wakatani (HW) model is presented, evaluating its performance via various analyses. Very good agreement is found between the GAIT and the HW models in the spatiotemporal Fourier and Proper Orthogonal Decomposition spectra, the flow topology characterized by the Okubo–Weiss parameter, and the time autocorrelation function of turbulent fluctuations. Excellent agreement has also been obtained in the probability distribution function of particle displacements and the effective turbulent diffusivity. In-depth analyses of the latent space of turbulent states, choice of hyperparameters and alternative deep learning models for the time prediction are presented. Our results highlight the potential of Artificial Intelligence-based surrogate models to overcome the computational challenges in turbulence simulation, which can be extended to other situations such as geophysical fluid dynamics.

Artificial intelligence↗

Noise-induced stabilization of dynamical states with broken time-reversal symmetry

Under a high frequency drive, Josephson junctions demonstrate Shapiro steps of quantized voltage. These are dynamically stabilized states in which the phase across the junction locks to the external drive. We explore the stochastic switching between two symmetric steps at $\frac{hw}{2e}$ and –$\frac{hw}{2e}$. Surprisingly, the switching rate exhibits a pronounced nonmonotonicity as a function of temperature, violating the general expectation that transitions should become faster with temperature. As a result, we explain this behavior by realizing that the system retains memory of the dynamic state from which it is switching, thereby breaking the conventional simplifying assumptions about separations of timescales.

36 MATERIALS SCIENCE↗