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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Gravitational waves from binary neutron star mergers with a spectral equation of state

In numerical simulations of binary neutron star systems, the equation of state of the dense neutron star matter is an important factor in determining both the physical realism and the numerical accuracy of the simulations. Some equations of state used in simulations are C 2 or smoother in the pressure/density relationship function, such as a polytropic equation of state, but may not have the flexibility to model stars or remnants of different masses while keeping their radii within known astrophysical constraints. Other equations of state, such as tabular or piece-wise polytropic, may be flexible enough to model additional physics and multiple stars' masses and radii within known constraints, but are not as smooth, resulting in additional numerical error. We will study in this paper a recently developed family of equation of state, using a spectral expansion with sufficient free parameters to allow for a larger flexibility than current polytropic equations of state, and with sufficient smoothness to reduce numerical errors compared to tabulated or piece-wise polytropic equations of state. We perform simulations at three mass ratios with a common chirp mass, using two distinct spectral equations of state, and at multiple numerical resolutions. We evaluate the gravitational waves produced from these simulations, comparing the phase error between resolutions and equations of state, as well as with respect to analytical models. From our simulations we estimate that the phase difference at merger for binaries with a dimensionless weighted tidal deformability difference greater than Δ$\tilde{Λ}$=55 can be captured by the SpEC code for these equations of state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Analyzing at-scale distribution grid response to extreme temperatures

Threats against power grids continue to increase, as extreme weather conditions and natural disasters (extreme events) become more frequent. Hence, there is a need for the simulation and modeling of power grids to reflect realistic conditions during extreme events conditions, especially distribution systems. Herein, this paper presents a modeling and simulation platform for electric distribution grids which can estimate overall power demand during extreme weather conditions. The presented platform's efficacy is shown by demonstrating estimation of electrical demand for 1) Electricity Reliability Council of Texas (ERCOT) during winter storm Uri in 2021, and 2) alternative hypothetical scenarios of integrating Distributed Energy Resources (DERs), weatherization, and load electrification. In comparing to the actual demand served by ERCOT during the winter storm Uri of 2021, the proposed platform estimates approximately 34 GW of peak capacity deficit. These numbers are consistent with state-of-the-art prediction results published in the literature. For the case of the future electrification of heating loads, peak capacity of 78 GW (124% increase) is estimated, which would be reduced to 47 GW (38% increase) with the adoption of efficient heating appliances and improved thermal insulation. Integrating distributed solar PV and storage into the grid causes improvement in the local energy utilization and hence reduces the potential unmet energy by 31% and 40%, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Candidate spin-liquid ground state in CsNdSe 2 with an effective spin-1/2 triangular lattice

Rare-earth-based triangular lattice materials are extremely attractive for studying unconventional magnetism. Here, we report the magnetic properties of layered CsNdSe 2 based on direct current (DC) and alternating current (AC) susceptibility measurements down to 0.04 K. While the AC susceptibility at the zero DC field shows a broad hump below 0.5 K, there is no sign of any long-range magnetic ordering. Quantitative analysis of the DC magnetic susceptibility gives the negative Curie-Weiss (CW) temperature θ CW < 0 in all directions, indicating antiferromagnetic interaction between Nd ions. Of particular interest is the low temperature magnetic susceptibility, which reflects the effective spin-1/2 state with $\theta^a_{\text {cw}}/\theta^c_{\text {cw}}$ > 3. The estimated exchange interactions are J a /k B = 1.42 K (in-plane) and J c /k B = 0.44 K (out-of-plane), pointing to the anisotropic magnetism. First-principles calculations that include spin-orbit coupling and Coulomb correlations reveal multiple states with zero net magnetization for CsNdSe 2 . Both experiment and simulation strongly suggest CsNdSe 2 has the spin liquid ground state with effective spin-1/2. Application of a magnetic field can induce long-range antiferromagnetic ordering with the maximum transition temperature around 0.3 K, in further support of the zero-field spin liquid state.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Raccoon density estimation from camera traps for raccoon rabies management

Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.

Davis, Amy J.↗
DOE OSTI2026

Advancing Asset Management in Water Infrastructure Systems

Aging water system infrastructure, including drinking water, wastewater, and stormwater, poses a growing challenge for utilities and municipalities. These water systems have well documented challenges with respect to their age, condition, and level of service. ASCE annual report cards consistently rate these infrastructure systems in the United States as underfunded, overcapacity, or past service life (ASCE 2025 Report Card). For example, Chini and Stillwell (2017) estimated that the mean water loss in drinking water systems, i.e., non-revenue water, is approximately 16% across the United States. These concerns are not just relegated to the United States, with Courtenay, British Columbia, identifying 17% of their water main pipes as in a ‘poor’ condition state, defined as a category condition 5 out of 5 (City of Courtenay, 2024). These cases illustrate the challenges utilities are facing to manage extensive networks of infrastructure to deliver a consistent and high level of service. For buried infrastructure such as water systems, studies suggest that preventative interventions can lead to lower maintenance costs and fewer service disruptions (Mazumder et al, 2018; Li et al, 2014). The demonstrated need and benefit of appropriately applied asset management is juxtaposed against the relatively sparse literature that evaluates water systems within an asset management construct. Since 2020, just 37 papers specifically reference asset management in the Journal of Water Resources Planning and Management. Of those, only a few specifically look to develop strategies for improved asset management. Therefore, we highlight four key research areas that represent opportunities for advancement of asset management research for water systems. First, advances in condition assessment and forecasting are needed to better estimate asset deterioration using diverse datasets. Second, machine learning (ML) and artificial intelligence (AI) hold promise for predictive maintenance and investment prioritization, though questions of generalizability and model transparency remain. Third, applying a value of information framework can guide utilities in making cost-effective sensor deployment and data collection decisions, to direct monitoring strategies towards data-informed asset management decisions. Finally, integrated infrastructure management is critical, requiring coordinated planning with other infrastructure systems and stakeholder engagement to reduce costs and enhance service delivery.

Chini, Christopher M.↗
DOE OSTI2025

Unification of finite symmetries in the simulation of many-body systems on quantum computers

Symmetry is fundamental in the description and simulation of quantum systems. Leveraging symmetries in classical simulations of many-body quantum systems can result in significant overhead due to the exponentially growing size of some symmetry groups as the number of particles increases. Quantum computers hold the promise of achieving exponential speedup in simulating quantum many-body systems; however, a general method for utilizing symmetries in quantum simulations has not yet been established. In this work, we present a unified framework for incorporating symmetry group transforms on quantum computers to simulate many-body systems. The core of our approach lies in the development of efficient quantum circuits for symmetry-adapted projection onto irreducible representations of a group or pairs of commuting groups. We provide resource estimations for common groups, including the cyclic and permutation groups. Our algorithms demonstrate the capability to prepare coherent superpositions of symmetry-adapted states and to perform quantum evolution across a wide range of models in condensed-matter physics and ab initio electronic structure in quantum chemistry. Specifically, we execute a symmetry-adapted quantum subroutine for small molecules in first-quantization on noisy hardware and demonstrate the emulation of symmetry-adapted quantum phase estimation for preparing coherent superpositions of quantum states in various irreducible representations of a symmetry group. In addition, we present a discussion of open problems regarding treating symmetries in digital quantum simulations of many-body systems, paving the way for future systematic investigations into leveraging symmetries quantumly for practical quantum advantage. The broad applicability and rigorous resource estimation for symmetry transformations make our framework appealing for achieving provable quantum advantage on fault-tolerant quantum computers, especially for symmetry-related properties.

quantum algorithms↗
DOE OSTI2025

Equation of state measurement of detonation carbon condensates using optical microscopy and interferometry

Thermochemical models of detonation that estimate performance (e.g., detonation velocity, energy delivery, etc.), are based on assumptions that carbon condensates (soot) formed during detonation is largely similar to bulk carbon. However, soot constituents can range from amorphous carbon to nanodiamond and include other material phases. Since thermodynamic properties of the soot such as compressibility are imperative for accurate thermochemical modeling of detonation reaction chemistry, experimental measurements of the equation of state (EOS) which determine the compressibility are vital. Due to the mixed-phase nature of detonation soot, typical methods to measure the EOS (e.g., x-ray diffraction) are untenable. In this study, the high-pressure EOS up to 20 GPa was determined for detonation soot collected from PBX 9502, Composition B (Comp B), Hexanitrostilbene (HNS), and LX-21 high explosives by employing a direct volume technique using optical microscopy and interferometry in a diamond anvil cell. Comp B soot was determined to be the least compressible [K 0 = 57.9(17) GPa] with HNS soot [K 0 = 53.7(15) GPa], LX-21 soot [K 0 = 45.8(51) GPa], and PBX 9502 soot [K 0 = 28.2(27) GPa] being more compressible, likely due to differences in nanodiamond content as compared to amorphous carbon and graphite content.

Amorphous materials↗
DOE OSTI2021

Estimation of Possible Primary Biological Particle Emissions and Rupture Events at the Southern Great Plains ARM Site

We use 10 years of data from the Department of Energy (DoE) Atmospheric Radiation Measurements (ARM) United States Southern Great Plains (SGP) site with nearby regional pollen and fungal spore measurements to indirectly estimate the seasonal influence of these two primary biological aerosol particles (PBAP). We estimate possible primary emissions of larger PBAP and PBAP rupture events, which form submicron organic aerosol during precipitation or high relative humidity. High pollen counts at two urban stations near SGP occur during late winter/early spring (day of year (DOY) 50–120) and late summer (DOY 240–310). Around 4–19 days per year show possible pollen events (PPE) when near-surface lidar observations of daily linear particle depolarization ratio >0.1 are coincident with high organic aerosol fraction. For PPE days with rainfall, aerosol size distribution observations show enhanced submicron particle concentrations consistent with pollen rupture events. For fungal spores, high fungal spore counts occur during late spring/early summer (DOY 110–195) and late summer/autumn (DOY 220–340). Based on size distribution observations, up to 7% of days have possible fungal spore rupture events (PFE) with higher aerosol number count specifically over the range expected for fungal spore fragment mobility diameter (20–50 nm). These short-lived PFE correlate with rainfall or occur after prolonged exposure to rainfall (e.g., >10 h). While the SGP site lacks direct measurements of bioaerosol and large particle sizes, this analysis suggests that PBAP primary emissions and rupture events could occur about 32 days per year, representing an important component of the aerosol budget during seasonal emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Preliminary proof-of-concept of real-time divertor heat flux control from infrared cameras with nitrogen injection in the DIII-D tokamak

In future tokamak reactors like ITER and the Fusion Pilot Plant (FPP), real-time feedback control of heat flux to the plasma-facing components (PFC) will be critical for steady-state operation. This work presents the first experimental demonstration of real-time divertor heat flux estimation with infrared thermography and feedback control with impurity seeding on the DIII-D tokamak. The flexible infrastructure of the Plasma Control System (PCS) on DIII-D makes this new capability possible. The PCS software runs on a gateway computer system, and five real-time compute nodes. An array of low latency streaming digitizers from D-TACQ Solutions connects to these real-time computers to collect and process data, and send commands to actuators during plasma discharges. This system handles the signal IO from the tokamak and allows the PCS to utilize the diagnostic data necessary to perform control in real-time. Feedback control on heat flux was accomplished by feeding infrared camera data from the “Infrared TV” (IRTV) camera to a custom-developed User Datagram Protocol (UDP) server. This server transmits infrared data to a newly developed PCS algorithm that estimates the heat flux to PFC. Here, a proportional integral derivative (PID) controller minimizes the error between a heat flux reference and the real-time estimate by injecting nitrogen gas into the divertor.

Algorithm development↗
DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗
DOE OSTI2024

Artificial-intelligence-driven shot reduction in quantum measurement

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL)-based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction Ansätze. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Multimodal CO2 Transportation Cost Model

This model provides a cost estimate for transporting CO2 via truck or rail in the United States using commercially available equipment and technologies. The model includes an analysis of direct and indirect CO2 emissions to determine costs per net tonne of CO2 transported. Publicly available data and methods published in the peer-reviewed literature are used to the extent possible; references are available at the bottom of the "Calculations" sheet. Upstream (i.e., liquefaction, buffer storage) and downstream (i.e., buffer storage, reconditioning) activities are included in the model of emissions and costs. All capital and operating expenditures are estimated in the "Calculations" sheet. The cost of financing the project is determined in the "FINEX" sheet. All user inputs are done via drop down menus in on the "User Interface" sheet. Summary results are also provided on the "User Interface" sheet.

Myers, CoreyA↗
DOE OSTI2020

Radar-based Snow Property Profile Retrievals for NSA

The product contains estimated snow microphysical properties retrieved primarily from vertically-profiling radar observations using a priori constraints that are dependent on atmospheric state. The product provides data at 1-minute time resolution, including snowfall rate at the surface and vertical profiles at 300-meter resolution of snow particle size distribution parameters and snow water contents. Estimated uncertainties for the snowfall rates and snow water contents are also provided. The product is obtained using an optimal estimation retrieval in which radar forward model and a priori state characteristics are based on expected snow particle microphysical and radar scattering properties evaluated from field experiment data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Starting-point-independent quantum Monte Carlo calculations of iron oxide

Quantum Monte Carlo (QMC) methods are useful for studies of strongly correlated materials because they are many body in nature and use the physical Hamiltonian. Typical calculations assume as a starting point a wave function constructed from single-particle orbitals obtained from one-body methods, e.g., density functional theory. However, mean-field-derived wave functions can sometimes lead to systematic QMC biases if the mean-field result poorly describes the true ground state. In this study, we examine the accuracy and flexibility of QMC trial wave functions using variational and fixed-node diffusion QMC estimates of the total spin density and lattice distortion of antiferromagnetic iron oxide (FeO) in the ground state B1 crystal structure. We found that for relatively simple wave functions the predicted lattice distortion was controlled by the choice of single-particle orbitals used to construct the wave function, rather than by subsequent wave function optimization techniques within QMC. By optimizing the orbitals with QMC, we then demonstrate starting-point independence of the trial wave function with respect to the method by which the orbitals were constructed by demonstrating convergence of the energy, spin density, and predicted lattice distortion for two qualitatively different sets of orbitals. The results suggest that orbital optimization is a promising method for accurate many-body calculations of strongly correlated condensed phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contain private information, could be inferred by an eavesdropper. In this article, a privacy-preserving distributed average observer is proposed that is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. Here, the proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Geothermal District Heating in the United States: 2021 Update

As of 2021, there are 23 geothermal district heating (GDH) systems in the United States. Most are over 30 years old. This paper presents an overview of GDH development in the United States and the performance of GDH systems over time. Calculations of the estimated levelized cost of heat (LCOH) for existing U.S. GDH systems were made using NREL's GEOPHIRES tool. Estimated LCOH for existing U.S. GDH systems ranges from $15 to $105/MWhth. This paper explores other factors in GDH development such as resource and system size, capacity factor, and the role of policy. U.S. GDH utilization and deployment are compared to worldwide trends. Results show that the market for GDH in the United States has been weak over the past 40 years due to the combination of inexpensive fossil fuel alternatives (mostly natural gas), lack of incentives focused on heating/cooling, and other factors. Future opportunities for increased GDH deployment in the United States related to increased demand for low-carbon heating and cooling solutions (driven by decarbonization goals in the residential, commercial and industrial heating/cooling sectors, and particularly aggressive ones on college campuses) are outlined. Lastly, this paper identifies policy mechanisms that have been implemented in other countries to incentivize GDH (such as financial incentives targeting GDH, geothermal risk reduction mechanisms, carbon prices benefiting low-carbon heat production, and others).

geothermal district heating↗
DOE OSTI2019

Quantum simulations of excited states with active-space downfolded Hamiltonians

Many-body techniques based on the double unitary coupled cluster ansatz (DUCC) can be used to downfold electronic Hamiltonians into low-dimensional active spaces. It can be shown that the resulting dimensionality reduced Hamiltonians are amenable for quantum computing. Recent studies performed for several benchmark systems using quantum phase estimation (QPE) algorithms demonstrated that these formulations can recover a significant portion of ground-state dynamical correlation effects that stem from the electron excitations outside of the active space. These results have also been confirmed in studies of ground-state potential energy surfaces using quantum simulators. In this letter, we study the effectiveness of the DUCC formalism in describing excited states. We also emphasize the role of the QPE formalism and its stochastic nature in discovering/identifying excited states or excited-state processes in situations when the knowledge about the true configurational structure of a sought after excited state is limited or postulated (due to the specific physics driving excited-state processes of interest). In this context, we can view the QPE algorithm as an engine for verifying various hypotheses for excited-state processes and providing statistically meaningful results that correspond to the electronic state(s) with the largest overlap with a postulated configurational structure. We illustrate these ideas on examples of strongly cor- related molecular systems, characterized by small energy gaps and high density of quasi-degenerate states around the Fermi level.

quantum chemistry, quantum computing, computationa↗