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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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The Illumina iSeq low-capacity sequencing platform was evaluated for use in foodborne disease surveillance and outbreak detection. The platform produced high quality sequence data comparable to that of the Illumina MiSeq and was cost-effective with fast turn-around time in low sample volume environments.
Rare-earth metals (REMs) are crucial for many important industries, such as power generation and storage, in addition to cancer treatment and medical imaging. One promising new REM refinement approach involves mimicking the highly selective and efficient binding of REMs observed in relatively recently discovered proteins. However, realizing any such bioinspired approach requires an understanding of the biological recognition mechanisms. In this report we developed a new classical polarizable force field based on the AMOEBA framework for modeling a lanthanum ion (La 3+ ) interacting with water, acetate, and acetamide, which have been found to coordinate the ion in proteins. The parameters were derived by comparing to high-level ab initio quantum mechanical (QM) calculations that include relativistic effects. The AMOEBA model, with advanced atomic multipoles and electronic polarization, is successful in capturing both the QM distance-dependent La 3+ –ligand interaction energies and experimental hydration free energy. A new scheme for pairwise polarization damping (POLPAIR) was developed to describe the polarization energy in La 3+ interactions with both charged and neutral ligands. Simulations of La3+ in water showed water coordination numbers and ion–water distances consistent with previous experimental and theoretical findings. Water residence time analysis revealed both fast and slow kinetics in water exchange around the ion. This new model will allow investigation of fully solvated lanthanum ion–protein systems using GPU-accelerated dynamics simulations to gain insights on binding selectivity, which may be applied to the design of synthetic analogues.
Despite the weak, van der Waals interlayer coupling, photoinduced charge transfer vertically across atomically thin interfaces can occur within surprisingly fast, sub-50 fs time scales. An early theoretical understanding of charge transfer is based on a noninteracting picture, neglecting excitonic effects that dominate optical properties of such materials. We employ an ab initio many-body perturbation theory approach, which explicitly accounts for the excitons and phonons in the heterostructure. Our large-scale first-principles calculations directly probe the role of exciton-phonon coupling in the charge dynamics of the WS 2 /MoS 2 heterobilayer. We find that the exciton-phonon interaction induced relaxation time of photoexcited excitons at the K valley of MoS 2 and WS 2 is 67 and 15 fs at 300 K, respectively, which sets a lower bound to the intralayer-to-interlayer exciton transfer time and is consistent with experiment reports. We further show that electron-hole correlations facilitate novel transfer pathways that are otherwise inaccessible to noninteracting electrons and holes.
8-moment plasma models using two different distribution functions are used to study the Nernst effect and heat transport in dense plasma. These models are presented in hyperbolic form in contrast to traditional parabolic systems derived from perturbing the distribution function, as in Braginskii [Rev. Plasma Phys. 1, 205 (1965)]. The hyperbolic moment formulation can be solved implicitly in time with straightforward and fast local solvers. The numerical implementation of 8-moment models with the relaxation method in the PERSEUS code is also presented. To test 8-moment PERSEUS compared to Braginskii’s transport equations, a verification test for the Nernst thermomagnetic wave by Velikovich et al. [Phys. Plasmas 26, 112702 (2019)] is performed that confirms the presence of the same physics, but with slight differences in the transport coefficients, which are tabulated in the limits of high and low magnetization.
In the present work, electrostatic drift kinetic simulations of parallel plasma transport within the tokamak scrape-off layer (SOL) are conducted using the COGENT code. The SOL configuration is represented in one-dimensional slab geometry, incorporating a heat source localized in the midplane. The heat source parameters correspond to those characterizing edge-localized modes observed in the Joint European Torus (JET) tokamak. The numerical model includes kinetic treatment of both ions and electrons, a simplified model for the gyrokinetic Poisson equation that allows one to step over short time scales associated with fast electrostatic shear Alfvèn waves, and the logical sheath boundary condition (LSBC) that enforces global system quasineutrality. A third-order accurate LSBC is derived to be consistent with the third-order accurate upwind advection scheme utilized in the code, and it was shown to noticeably impact the simulation results, especially parallel heat flux at the target plate. The findings of this study are in agreement with results from preceding fluid and kinetic simulations.
The ability to model the interplay between the core and edge of tokamak plasmas is crucial to designing both the plasma operating scenario of a fusion pilot plant and the design of the tokamak itself. Scrape-off-layer (SOL) models that are tailored to integrated scenario modeling need to have fast turn-around time and minimal computational burden to enable wide parameter-space coverage for design scoping. The SOL 0-D Box model is a reduced SOL model based on global power and particle balance that captures the essential physics of SOL transport with little computational cost. The usage of the 0-D Box model in core-edge coupled simulations has been demonstrated in both interpretive and predictive modes on a variety of devices. This paper presents a sensitivity study of the 0-D Box model to the input SOL heat-flux width for an ST40 plasma. This study demonstrates that accurate prediction of this width is crucial to predicting global performance parameters of a plasma scenario, such as energy confinement time and flux consumption. We also present an extension of the Box model to 1-D to allow for parallel variation of plasma parameters along the magnetic field lines. The 1-D Box model is then compared with SOLPS-ITER simulations of an NSTX plasma. Advantages and limitations of the Box model are discussed, and future directions are outlined.
In many strongly correlated insulators, antiferromagnetic order competes with exotic and technologically relevant phases, like superconductivity. While control of spin order is critical to stabilize different functional states, elucidating the mechanism of laser-induced demagnetization in complex oxides remains a challenge. It is unknown if the optical pulse can quench magnetization nonthermally or if it instead only acts as a heat source. In this study, we used ultrafast, broadband, optical spectroscopy to track the responses of the electronic, lattice, and spin degrees of freedom and their relation to antiferromagnetism in the strongly spin-orbit coupled insulator Sr 3 Ir 2 O 7 . We find that magnetization can be rapidly and strongly suppressed on a sub-150 fs timescale. At low excitation fluences, the magnetic recovery is fast; however, the recovery time increases dramatically with the magnitude of demagnetization. At the same time, we show that the lattice, evidenced through the A g phonon frequencies, appears to remain below T N , suggesting that the system remains nonthermal during the optical modulation of spin order. We suggest that photogenerated spin defects are responsible for the long-lived demagnetized state and discuss its implications for optical control of solids.
A dense neutrino gas, such as the one anticipated in the supernova environment, can experience fast neutrino flavor conversions on scales much shorter than those expected in vacuum probably provided that the angular distributions of ν e and $\bar{v}_e$ cross each other. We perform a detailed investigation of the neutrino angular distributions obtained by solving the Boltzmann equations for fixed matter profiles of some representative snapshots during the postbounce phase of core-collapse supernovae in multidimensional calculations of a 11.2 M ⊙ and a 27 M ⊙ progenitor model. Although the 11.2 M ⊙ model features ν e - $\bar{v}_e$ angular crossings and the associated fast modes at different time snapshots, the 27 M ⊙ model does not show any crossings within the decoupling region. We show that this can be understood by studying the multipole components of the neutrino distributions. In fact, there is a higher chance for the occurrence of ν e - $\bar{v}_e$ angular crossings for the zones where the multipole components of the neutrino distributions are strong enough. We also show that there can exist more than one crossing between the angular distributions of ν e and $\bar{v}_e$. In addition, apart from the crossings within the neutrino decoupling region, there is a class of ν e - $\bar{v}_e$ angular crossings that appears very deep inside the protoneutron star.
Laterally configured diamond photoconductive semiconductor switches (PCSS) with a buried, metallic p+ current channel are reported. Above bandgap ( λ ≤ 226 nm) optical triggering enables responsivity of over 130 mA/W. The use of low-impurity semi-insulating diamond as an active absorption layer enables fast rise and fall times (~2 ns) and on/off ratios greater than 10 11 . The PCSS excited with a laser energy of 20 nJ per pulse passes a high current density (44 A/cm) under a DC bias of 60 V, thanks to the buried metallic p+ current channel. The reported devices promise high current carrying capacity without the need for filamenting while leveraging the excellent optical, electronic, and thermal properties of diamond.
This is the final Technical Report for DOE-SETO Project Award # DE-EE0036461. The goal of this project is to advance the understanding of the grid impact of high penetration of photovoltaic (PV) generation by developing novel numerical methods to solve the differential algebraic equations (DAEs) that define power systems. This will overcome the limitations of current software packages – namely that they only consider fast dynamics over brief time periods. The work presented in this final project report covers results over the entire period of the project. This includes results on model development, code development for the PST repository, datasets in the PST repository, algorithm development and results from variable time-step simulations, development and results from multirate simulations, and sensitivity analysis of key parameter in variable time-step methods. In addition, this report discusses project outreach activities to stakeholders, and a summary of project products. Also covered in this final report is the writing of two conference papers (one of which has already been accepted) and a journal paper. In addition, the updating of two inverter models (both grid forming and grid following) to be compatible with the latest version of PST software is discussed.
Announced in 2020, the Electron Ion Collider will be constructed at Brookhaven National Lab (BNL) by modifying the existing Relativistic Heavy Ion Collider (RHIC) facility. Among the alterations include the installation of an electron storage ring (ESR) and a Rapid Cycling Synchrotron (RCS) in the existing RHIC tunnel. The RCS will accelerate polarized electrons from 400MeV to 5GeV,10GeV and 18 GeV and transfer it to the ESR. The LINAC source produces 7nC per shot meanwhile the RCS requires 28nC per bunch of charge. Currently, it is planned to inject 4 bunches into adjacent RF buckets and then merge them into a single RF bucket using fields from several RF cavities. However, this requires a kicker with an exceptionally fast rise and fall time to inject into adjacent RF bucket without disturbing the existing circulating bunch and this requires harmonic RF kickers. Our study focuses on determining the feasibility of an alternate particle injection method that will utilize slip stacking, a method that accommodates the circulation of multiple beams with different momentum in the same azimuthal space of the beampipe, thus permitting the accumulation of charge from several shots into a single RF bucket. We find the momentum aperture required for slip stacking using current RCS beam parameters exceeds the RCS’s momentum deviation limit,dp/p = 1.5%. We determine the appropriate dimensions the beam must have to enable slip stacking and conduct a literature review of slip stacking schemes in other facilities. The literature review tentatively indicates that slip stacking has not been attempted on electron beams at a major accelerator facility.. The slip stacking approach has the potential to save the cost of the harmonic RF kicker and bunch merging cavities.
Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been developed by Latimer Controls, Inc. to estimate the headroom of large PV plants for grid operation and control; however, these technologies lack comprehensive validation under real-world application scenarios. Latimer Controls, Inc. received two voucher awards for research at a national laboratory from the Department of Energy American Made Solar Prize Round 6. The National Renewable Energy Laboratory (NREL) was selected to collaborate with Latimer staff to conduct a performance evaluation of Latimer PV control software. The NREL team will develop a hardware-in-the-loop (HIL) testbed to perform testing and validation of the Latimer PV control technology in a de-risked yet realistic testbed environment. Latimer and NREL worked together to analyze the test data, draw conclusions from the results, and disseminate the resulting scientific findings. In this CRADA work, we propose to test and validate the real-world application of the Latimer Control solution in an HIL environment. We evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. In particular, a data-driven potential high limit (PHL) estimation is developed for large solar plants to accurately estimate their headroom so that they have fast and short-time regulation and control capability to participate in grid services and respond to grid signals in real time (e.g., AGC). This PHL estimation algorithm is embedded in a hardware power plant controller (PPC) and tested with an IEEE-39 bus system model developed in RTDS. To account for the varying cloud conditions and diverse inverter dispatches, we developed a 135-MW PV plant with detailed modeling of 27 individual PV modules and inverters using RTDS. The real-world communications used in such big plants, such as ModBus TCP/IP for inverter level and DNP3 for plant level, were developed to emulate the real-world applications in big PV plants. The ML-based PHL estimation method is tested under nine separate weather scenarios against the ‘reference-control’ solution, hereafter referred to as the baseline solution. The baseline method reserves a subset of inverters (reference group) to operate at their PHL at all times and dispatches only the remaining inverters (control group) at curtailed levels to fulfill the flexibility need. Despite being successfully piloted by NREL in California in 2017 and Chile in 2020, there exist two gaps in the state of the art to fully unlock the flexibility of PV plants: a. There is a trade-off between the PHL estimation accuracy and the flexibility range. b. There lacks granularity in the PHL estimation to capture the variation across inverters. The Latimer solution seeks to address these gaps by applying machine learning methods to improve PHL estimation accuracy while accounting for variability at every inverter. Performance metrics were taken from the 2023 Georgia Power CARES utility-scale RFP. The results demonstrate that the ML-based approach outperforms the traditional baseline method in PHL estimation accuracy for 7 of 9 scenarios. The average PHL error across the nine scenarios was 7.40% for the ML-based method, 2.06% less than the 9.46% PHL error average across scenarios that was exhibited by the baseline method. Additionally, the PHL error was below 5% for at least 95% of the testing interval for 3 of 9 tested intervals with the ML approach, whereas it did not achieve this metric for any of the baseline tests. Overall, simulation results indicate the superior performance of an ML-based approach compared to the conventional baseline reference-control approach, showcasing its potential to support grid stability and operational efficiency. This laboratory HIL testing using real PPC, representative power system simulation models in real-time with detailed PV plant and inverter models, and real-world communication protocols gives us confidence that this machine learning based PHL estimation algorithm works well in the hardware PPC and therefore de-risks future field commissioning. The end goal of this project is to advance grid technology to address the grid operation challenges brought by solar plant’s variability and uncertainties in power generation.
The rapid expansion of data centers, driven by the exponential growth in data-processing and storage needs, presents significant challenges and opportunities for various stakeholders, including data center developers, nuclear energy providers, and utility companies. Data centers are projected to consume 6.7–12% of United States (U.S.) electricity by 2028, driven by artificial intelligence (AI) and cloud-computing demands. Nuclear energy offers reliability and dispatchable baseload power, but data centers need power now while nuclear still needs time to address siting, fast power ramping, and regulatory hurdles. Utilities must keep pace with the unprecedented acceleration of large load interconnection requests and urgently adapt to high-density loads while maintaining grid stability, reliability, and accelerating interconnection timelines. This report dives into these challenges and proposes key collaboration strategies to streamline data center integration that aligns with recent federal initiatives like America’s AI Action Plan and related executive orders that emphasize the importance of data center growth, nuclear energy expansion, and maintaining a competitive edge in the global AI race.
Accelerator facilities produce neutrino beams from meson decays in a decay volume. Experiments measure event rates that depend on flux, cross sections, and detector response, so the flux is predicted using hadron production and beamline modeling and constrained by beam instrumentation, since near detectors alone cannot separate flux from cross section. Proton, hadron, and muon monitors can track the parent particle distributions and beam conditions, providing the inputs needed for flux predictions in long-baseline experiments such as NOvA, T2K, and DUNE. This talk reviews how beam monitors are used in practice to understand neutrino flux. Proton beam monitors tell where the beam hits the target and how stable it is. Farther downstream, hadron and muon monitors sample particles produced in meson decays. Because those muons come from the same parents as the neutrinos, their profiles reveal focusing, alignment shifts, and other changes in the beam, and they are routinely used to detect problems and guide flux predictions. The muon information can be used more quantitatively; for example, to infer the parent meson phase space, and fast radiation-hard timing detectors can add sensitivity to the momentum dependence of the focusing. These developments show both how tightly beam measurements can constrain the flux and where the current limits still lie. These approaches complement monitored-beam concepts, in which the decay region is instrumented to detect charged leptons from meson decays and to measure the neutrino flux directly.
Accelerator facilities produce neutrino beams from meson decays in a decay volume. Experiments measure event rates that depend on flux, cross sections, and detector response, so the flux is predicted using hadron production and beamline modeling and constrained by beam instrumentation, since near detectors alone cannot separate flux from cross section. Proton, hadron, and muon monitors can track the parent particle distributions and beam conditions, providing the inputs needed for flux predictions in long-baseline experiments such as NOvA, T2K, and DUNE. This talk reviews how beam monitors are used in practice to understand neutrino flux. Proton beam monitors tell where the beam hits the target and how stable it is. Farther downstream, hadron and muon monitors sample particles produced in meson decays. Because those muons come from the same parents as the neutrinos, their profiles reveal focusing, alignment shifts, and other changes in the beam, and they are routinely used to detect problems and guide flux predictions. The muon information can be used more quantitatively; for example, to infer the parent meson phase space, and fast radiation-hard timing detectors can add sensitivity to the momentum dependence of the focusing. These developments show both how tightly beam measurements can constrain the flux and where the current limits still lie. These approaches complement monitored-beam concepts, in which the decay region is instrumented to detect charged leptons from meson decays and to measure the neutrino flux directly.
(left) The Earth’s radiation belts are donut-shaped regions containing MeV electrons (color contour for density) trapped by the magnetic field (white curves). We use DREAMxD code to model their dynamics. (right) We develop a new method to accelerate a key piece of the code – diffusion coefficient calculation. Comparing the compute time in node hours required by the standard approach to the compute time required for the fast method, it shows that the new method can be 100x faster for a large problem size.