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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Projected changes in African easterly wave activity due to climate change

African easterly waves significantly influence regional hydroclimate, making it crucial to understand how global warming will impact their activity. Here, we investigate future changes in wave activity and assess the underlying mechanisms using an ensemble of Earth system models. We find a robust increase in wave activity over the Sahel–Sahara region by the end of the 21st century under two emission scenarios. This intensification is linked to increased baroclinicity associated with a strengthening of the meridional temperature gradient between the Guinea Coast and the Sahara. Our results also indicate that low-level warming enhances the waves by reinforcing monsoon flow, leading to increased convergence and vertical motion along the intertropical discontinuity. These energetic alterations significantly modify the conditions that currently produce these waves. Overall, our findings suggest that changes in wave activity could impact the transport of Saharan dust and mesoscale convective activity over the Sahel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Dark matter clumps as sources of gravitational-wave glitches in LIGO-Virgo-KAGRA data

We consider the hypothetical possibility that nonstationary glitch features in the noise of ground-based gravitational-wave detectors could be produced by small dark matter clumps that pass through the earth in the vicinity of gravitational-wave detectors. Here, we first derive the gravitational-wave strain that would be generated by the passage of such a dark matter clump. We find that the strain is primarily sourced by the Newtonian gravitational acceleration of the mirrors toward the clump and by the Shapiro time delay of the photons in the laser beams as they pass through the gravitational potential created by the dark matter clump. We also find that the Newtonian acceleration effect dominates the gravitational-wave strain for both ground and space-based interferometers. We then compare our dark matter clump, gravitational-wave strain model to 84 Koi-Fish glitches detected during the second observing run of the LIGO/Virgo/KAGRA Collaboration through a Markov chain Monte Carlo Bayesian analysis. We find that all glitches but nine can be confidently rejected as having originated from dark matter clumps. For the remaining glitches, the dark matter hypothesis cannot be excluded, and the maximum a posteriori parameters yield minimum densities of about 10 −7 g⁡/cm 3 , within the model. These results allow us to place the first direct upper limits with gravitational-wave detectors on the local over-density of dark matter in the form of clumps in the local neighborhood of Earth, namely 𝜌 DM ⁢clumps ≲ 10 −15 g⁡/cm −3 .

Astronomy and AstroPhysics↗
DOE OSTI2025

Surface acoustic waves and lattice vibrations in two-dimensional Ti 3 ⁢C 2 ⁢𝑇 𝑥 (𝑇 =O, F) MXene films

We investigated surface acoustic wave (SAW) propagation and lattice vibrations in two-dimensional (2D) titanium carbide (Ti 3 ⁢C 2 ⁢𝑇 x ) MXene films as a function of surface termination and layer stacking, using atomistic simulations. We found that SAW propagation velocity is highly sensitive to both single-layer properties and interlayer bonding. Surface terminations significantly modulate wave behavior, with oxygen and fluorine terminations producing distinct effects on wave propagation, with oxygen-terminated monolayers exhibiting 20% higher wave speeds than fluorine counterparts due to strengthened intralayer bonds. Key observations include the transition from one to two layers causing wave speed variations, and the development of interlayer modes that generate more dispersed lattice vibrations. As the film layer thickness increases, SAW propagation becomes predominantly confined to the upper surface, with coherence of vibrational modes diminishing in multilayer structures. These findings suggest MXene terminations and layer stacking are crucial parameters for controlling SAW behavior, offering promising avenues for novel acoustic wave device applications.

2-dimensional systems↗
DOE OSTI2024

System and method for wave prediction

A method and system for prediction of wave properties include collecting time series data streams from one or more wave measurement devices and processing the data using a wave-prediction algorithm to identify the frequency components of the data and compute wave parameters. The wave-field is propagated in space and time to predict wave height, speed, and velocity at a target location. A sliding window approach is used to continuously update the prediction in real-time.

Previsic, Mirko↗
DOE OSTI2026

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗
DOE OSTI2024

Experimental Study of Alfvén Wave Reflection from an Alfvén-speed Gradient Relevant to the Solar Coronal Holes

Abstract We report the first experimental detection of a reflected Alfvén wave from an Alfvén-speed gradient under conditions similar to those in coronal holes. The experiments were conducted in the Large Plasma Device at the University of California, Los Angeles. We present the experimentally measured dependence of the coefficient of reflection versus the wave inhomogeneity parameter, i.e., the ratio of the wavelength of the incident wave to the length scale of the gradient. Two-fluid simulations using the Gkeyll code qualitatively agree with and support the experimental findings. Our experimental results support models of wave heating that rely on wave reflection at low heights from a smooth Alfvén-speed gradient to drive turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Report on Coastal Structure Integrated Wave Energy Converters (CSI-WECs)

This report investigates a largely underexplored, and underexploited, application for wave energy: coastal structure integrated wave energy converters (CSI-WECs). These WEC types have significant potential to rapidly advance the wave energy industry while addressing the challenges of coastal community resiliency and marine energy integration and application, and the urgent need for increased coastal protection. To further support the value proposition investigation of this work, three wave energy developers agreed to conduct an energy production analysis of their technology for the four sites identified. Additionally, a geographic information system-based tool was developed that supports efficient and comprehensive site assessment for optimal and high-value deployments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

The Effects of Material-Filled Voids on Detonation Wave Shape in Rubberized RDX Explosives

The sensitivity of explosives is affected by inhomogeneities within the material. This is evident in the increased shock sensitivity of explosives with slightly lower densities resulting from an increased number of hotspots. The influence of hotspots on explosive initiation has been well studied; however, few studies have been conducted on the effect of intermediate-sized voids (0.1–10 mm) on a propagating detonation wave. Cylindrical voids filled with air have been studied for diameters ranging from 0.3 mm to 0.8 mm for both 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) and 1,3,5-trinitro-1,3,5-triazinane (RDX)-based rubberized explosives. Continuing the investigation into single cylindrical voids, this study examined the effects of 0.5 mm diameter voids filled with different inert cylindrical metals on the detonation wave shape for an RDX-based rubberized explosive. The metals selected for experiments were 1066 aluminum, brass, copper, and tungsten. The propagation of the detonation wave was captured using a digital streak camera. Experimental results showed that the extent of detonation wave shaping was closely tied to the density differential between the bulk explosive and metal insert. Forty-four different filler materials, including non-metals, were simulated using a hydrodynamic code to further analyze material inclusion effects. The main factors hypothesized to be of interest were bulk sound speed, shock impedance, and filler material density. We found that the local detonation delay could be correlated fairly well to a ratio of bulk sound speed and density. Understanding the influence of material inclusions on detonation performance and wave shape allows for tailoring of detonations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Non-propagating structures and propagating waves in solar wind turbulence revealed by simulations and observations

Structures and waves are common features of solar wind turbulence at various scales. The interplay between structures and waves is important for processes such as the turbulent energy cascade, plasma heating, and particle scattering. Our understanding of turbulence has been advanced by not only new space missions and numerical simulations, but also techniques that have been developed to interpret the rapidly growing turbulence data. We review basic models of turbulence with a specific focus on the analysis methods for understanding magnetic structures and waves. MHD and kinetic waves in single-spacecraft time series measurements can be identified through mode decomposition or their characteristic polarization signatures. The structures in this paper are considered as zero-frequency, non-propagating or convected modes embedded in the solar wind. The synergy between observations and simulations is most evident in the application of spatial-temporal analysis to multi-spacecraft observation and turbulence simulations. The spatial-temporal analysis has greatly improved our understanding of structures and waves in turbulence. We conclude by discussing prospects for future research.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2024

Characterization of blast waves induced by femtosecond laser irradiation in solid targets

Blast waves have been produced in solid target by irradiation with short-pulse high-intensity lasers. The mechanism of production relies on energy deposition from the hot electrons produced by laser–matter interaction, producing a steep temperature gradient inside the target. Hot electrons also produce preheating of the material ahead of the blast wave and expansion of the target rear side, which results in a complex blast wave propagation dynamic. Several diagnostics have been used to characterize the hot electron source, the induced preheating and the velocity of the blast wave. Results are compared to numerical simulations. These show how blast wave pressure is initially very large (more than 100 Mbar), but it decreases very rapidly during propagation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Offshore wind and wave energy can reduce total installed capacity required in zero-emissions grids

Abstract As the world races to decarbonize power systems to mitigate climate change, the body of research analyzing paths to zero emissions electricity grids has substantially grown. Although studies typically include commercially available technologies, few of them consider offshore wind and wave energy as contenders in future zero-emissions grids. Here, we model with high geographic resolution both offshore wind and wave energy as independent technologies with the possibility of collocation in a power system capacity expansion model of the Western Interconnection with zero emissions by 2050. In this work, we identify cost targets for offshore wind and wave energy to become cost effective, calculate a 17% reduction in total installed capacity by 2050 when offshore wind and wave energy are fully deployed, and show how curtailment, generation, and transmission change as offshore wind and wave energy deployment increase.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Electrical switching of a p-wave magnet

Magnetic states with zero magnetization but non-relativistic spin splitting are outstanding candidates for the next generation of spintronic devices. Their electronvolt (eV)-scale spin splitting, ultrafast spin dynamics and nearly vanishing stray fields make them particularly promising for several applications. A variety of such magnetic states with non-trivial spin textures have been identified recently, including even-parity d-wave, g-wave or i-wave altermagnets and odd-parity p-wave magnets. Achieving voltage-based control of the non-uniform spin polarization of these magnetic states is of great interest for realizing energy-efficient and compact devices for information storage and processing. Spin-spiral type II multiferroics are optimal candidates for such voltage-based control, as they exhibit an inversion-symmetry-breaking magnetic order that directly induces ferroelectric polarization, allowing for symmetry-protected cross-control between spin chirality and polar order. Here we combine photocurrent measurements, first-principles calculations and group-theory analysis to provide direct evidence that the spin polarization of the spin-spiral type II multiferroic NiI 2 exhibits odd-parity character connected to the spiral chirality. The symmetry-protected coupling between chirality and polar order enables electrical control of a primarily non-relativistic spin polarization. Our findings represent an observation of p-wave magnetism in a spin-spiral type II multiferroic, which may lead to the development of voltage-based switching of non-relativistic spin polarization in compensated magnets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes ($\ell$, |m|) = {(2,2), (2,1), (3,3), (3,2), (4,4)}, and mode mixing effects in the $\ell$ = 3, |m| = 2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Validating sequential Monte Carlo for gravitational-wave inference

Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalescences. It can handle the complex nature of the gravitational-wave likelihood surface and provides an estimate of the Bayesian model evidence. However, there is another class of algorithms that meets the same requirements, but has not been used for gravitational-wave analyses: sequential Monte Carlo (SMC), an extension of importance sampling that maps samples from an initial density to a target density via a series of intermediate densities. In this work, we validate a type of SMC algorithm, called persistent sampling (PS), for gravitational-wave inference. We consider a range of different scenarios including binary black holes and binary neutron stars and real and simulated data and show that PS produces results that are consistent with NS whilst being, on average, 2 times more efficient and 2.74 times faster. This demonstrates that PS is a viable alternative to NS that should be considered for future gravitational-wave analyses.

black hole mergers↗
DOE OSTI2025

TEAMER Technical Support on Biofouling and Corrosion for E-Wave Technologies LLC Novel (Abstract)

National Technology & Engineering Solutions of Sandia (NTESS) in collaboration with the Pacific Northwest National Laboratory (PNNL), and with guidance from E-Wave, will assess corrosion and biofouling challenges specific to E-Wave’s innovative WEC designed to retrofit into and power existing aquaculture farm infrastructure from ocean waves. The teams will examine coatings via a static raw seawater test and determine the efficacy of coatings for E-Wave’s applications. Samples will include panels of materials provided by E-Wave and coated with three different paint treatments. Fouling will be determined using a staining technique followed by photographic analysis to evaluate fouling intensity on the surface. Materials will also be weighed before and after submersion to facilitate weight change analysis resulting from fouling organisms aggregating on the materials.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

GIS Supported Optimal Site Selection for Coastal Structure Integrated Wave Energy Converters: Preprint

There is an urgent need for adaptative engineering towards more resilient coastal communities, and Coastal Structure Integrated Wave Energy Converters (CSI-WECs) are a promising solution. CSI-WECs are wave energy converters (WECs) that are built into coastal protection structures, such as breakwaters. These devices provide the dual benefits of coastal protection and local energy production, and unlike other WECs, maximizing energy production is not always the main objective. CSI-WECs are located near the shore, where the wave resource is lower, thus site selection for these devices differs from the typical offshore WECs. Other attributes of a site that may be more important than wave power include existing coastal structures, port proximity, electric transmission line proximity, and location of disadvantaged communities. Geospatial information systems (GIS) interfaces can be used to easily visualize geospatial data that represents these difference kinds of criteria important for the determination of optimal marine energy sites. Multi-Criteria Decision Analysis (MCDA) is a geospatial analysis method that allows for the evaluation of multiple, usually overlapping, criteria. This project applies GIS-based MCDA methods to two distinct case studies in Puerto Rico and California for CSI-WEC site selection. The two study sites contrast in terms of wave resource, coastal hazards, and local energy needs. This research demonstrates the utility of applying an MCDA framework within GIS to facilitate efficient site selection for devices with unique characteristics in different use cases.

coastal protection↗
DOE OSTI2024

Hall MHD waves: A fundamental departure from their MHD counterparts

It is demonstrated through a succinct derivation as to how the linear waves in Hall magnetohydrodynamics (HMHD) constitute a fundamental departure from the standard MHD waves. Apart from modifying the conventional MHD spectrum, the Hall current induces a distinct and new branch consisting of purely circularly polarized waves that may become the representative shear waves.

Astrophysics↗