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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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We study the spatiotemporal patterns of density fluctuations in 16,24O and 48Ca using nuclear interactions from chiral effective field theory and the time-dependent coupled-cluster method. We find that two-particle–two-hole excitations generate small-amplitude fluctuations that are fast, short-ranged, and of stochastic character.
Nonlinear frequency conversion processes, such as optical rectification, difference-frequency generation, and sum-frequency generation, are fundamental for producing electromagnetic radiation at diverse frequencies. In this work, we demonstrate that coherently excited infrared-active phonons can act as transducers for generating nonlinear magnetizations through phonon-magnon interactions, analogous to nonlinear optical frequency conversion. We derive analytical solutions for the time-dependent magnetizations arising from the second-order response to the electric field component of an ultrashort laser pulse. These solutions enable us to define second-order nonlinear magneto-electric susceptibilities, which describe rectification, impulsive excitation, and sum-frequency excitation of coherent magnons. Our theoretical framework naturally incorporates the conventional magneto-optic and phonon inverse Faraday effects and predicts a hybrid magneto-opto-phononic inverse Faraday effect involving photon-phonon-magnon scattering. This work highlights nonlinear phononics as a pathway for controlling magnetization in solids.
In this paper, we present a design of a high-efficiency high-gain free-electron laser oscillator based on the use of a strongly tapered undulator for extracting energy from high-brightness electron beams. We provide an analytical model of the setup followed by numerical simulations for lasing at the wavelength of 13.5 nm. We discuss the optimization of the system in steady state and the conditions necessary for the pass-per-pass buildup of the power from shot noise level. We propose the use of fast phase shifters as a way to accelerate the buildup. Finally, we present time-dependent simulations of the oscillator and discuss the role of spectral filtering. The optimized working point yields a total energy conversion efficiency from the electron beam to output radiation above 1% at the wavelength of 13.5 nm.
Shock-augmented ignition (SAI) [R. H. H. Scott et al., Phys. Rev. Lett. 129, 195001 (2022)] is an alternative inertial confinement fusion concept that is designed to achieve high energy gain by combining improved resilience to instabilities with enhanced fuel compression. In SAI, lower implosion velocities can improve hydrodynamic stability and limited laser intensities reduce the excitation of detrimental laser-plasma instabilities, enabling greater areal density accumulation. Here we report an experimental investigation of SAI using a series of warm D 2 implosions. By timing the augmenting shock to arrive at the implosion center immediately prior to peak compression, the areal density and hot-spot pressure increase by 50% and 112%, respectively, relative to unoptimized designs. Ignition-scale simulations reproduce the observed timing dependence and demonstrate the potential of SAI for inertial fusion energy.
Lamellar phases are essential in various soft matter systems, with topological defects significantly influencing their mechanical properties. In this report, we present a machine-learning approach for quantitatively analyzing the structure and dynamics of distorted lamellar phases using scattering techniques. By leveraging the mathematical framework of Kolmogorov–Arnold networks, we demonstrate that the conformations of these distorted phases – expressed as superpositions of complex waves – can be reconstructed from small-angle scattering intensities. Through the contour analysis of wave field phase singularities, we obtain the statistics of the spatial distribution of topological defects. Furthermore, we establish that the temporal evolution of these defects can be derived from the time-dependent traveling wave field, informed by the dispersion relation of spectral components. This method opens new avenues for investigating the dynamics of distorted lamellar phases using various dynamic scattering techniques such as neutron spin echo and X-ray photon correlation spectroscopy. These findings enhance our microscopic understanding of how defects influence the physical properties of lamellar materials, with implications for both equilibrium and non-equilibrium states in general lamellar systems.
X-ray free-electron lasers (XFELs) open a new era of X-ray based research by generating extremely intense X-ray flashes. To further improve the spectrum brightness, a self-seeding FEL scheme has been developed and demonstrated experimentally. As the next step, new-generation FELs with high repetition rates are being designed, built and commissioned around the world. A high repetition rate would significantly speed up the scientific research; however, alongside this improvement comes new challenges surrounding thermal management of the self-seeding monochromator. Here, a new configuration for self-seeding FELs is proposed, operated under a high repetition rate which can strongly suppress the thermal effects on the monochromator and provides a narrow-bandwidth FEL pulse. Three-dimension time-dependent simulations have been performed to demonstrate this idea. Finally, with this proposed configuration, high-repetition-rate XFEL facilities are able to generate narrow-bandwidth X-ray pulses without obvious thermal concern on the monochromators.
In multi-timescale multi-agent reinforcement learning (MARL), agents interact across different timescales. In general, policies for time-dependent behaviors, such as those induced by multiple timescales, are non-stationary. Learning non-stationary policies is challenging and typically requires sophisticated or inefficient algorithms. Motivated by the prevalence of this control problem in real-world complex systems, we introduce a simple framework for learning non-stationary policies for multi-timescale MARL. Our approach uses available information about agent timescales to define and learn periodic multi-agent policies. In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy. To learn such policies, we propose a policy gradient algorithm that parameterizes the actor and critic with phase-functioned neural networks, which provide an inductive bias for periodicity. The framework's ability to effectively learn multi-timescale policies is validated on a gridworld and building energy management environment.
Multi-spike models of synaptic plasticity, such as the triplet and suppression spike-timing-dependent plasticity (STDP) rules, exhibit better alignment with neurophysiological data in the brain compared to the pair-based STDP rule. Previous studies have empirically shown that the pair-based STDP rule can detect spatiotemporal spike patterns hidden in equally dense distractor spike trains in an unsupervised manner. However, it fails to detect spike patterns influenced by jitter noise. Given that spiking neural networks (SNNs) exhibit variability in generated spike trains in response to the same inputs, it becomes imperative to have learning rules capable of detecting spike patterns even in the presence of jitter noise. In this study, we introduce a simplified suppression-based STDP rule that demonstrates significantly enhanced tolerance to jitter in spike patterns compared to the pair-based STDP rule. Unlike the ideal suppression STDP rule, characterized by an exponential learning window and requiring high-resolution synapses, the simplified rule limits the synaptic efficacy update to a single bit at any given instant. Moreover, it employs 4-bit fixed-point synapses, facilitating straightforward implementation in neuromorphic hardware.
We present two datasets that contain quantum chemical electronic structure calculations for organic molecules from the GDB-9-Ex dataset. The “GDB-9-Ex_TD-DFT-PBE0” dataset contains calculations performed using the time-dependent density functional theory (TD-DFT) first principles method, and the “GDB-9-Ex_EOMCCSD” dataset contains calculations performed using the equation-of-motion coupled cluster (EOM-CCSD) method. Both types of calculations were performed using the ORCA software and provided ultraviolet-visible spectra with a high level of accuracy.
Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.
Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.
Allan deviation provides a means to characterize the time-dependence of noise in oscillators and potentially identify the source characteristics. Measurements on a 130 nm, 7-stage ring oscillator show that the Allan deviation declines from 300 K to 150 K as expected, but surprisingly increases from 150 K to 11 K. At low temperatures, the measured Allan deviation can be well fit using a few random telegraph noise (RTN) sources over the range of a few kilohertz to a few gigahertz. Further, the RTN characteristics evolve to reveal an enhanced role in low-frequency noise at lower temperatures.
The plasma charge injection technology reported earlier can be a low-cost alternative to the Al 2 O 3 passivation technology. The charge stability under sunlight exposure is a key concern. We investigated the light-induced loss of injected charge for different nitrogen-to-silicon atomic ratios (x=N/Si) of SiN x , and found drastic improvements in charge loss with increasing x-value and logarithmic time dependent charge decays. The best result indicated a sufficient charge amount can sustain over the module lifetime (25 years). Here, the charge loss is likely related to photo-generated free carriers and atomic hydrogens in SiN x .
Including invariance of global properties of a phys-ical system as an intrinsic feature in graph neural networks (GNNs) enhances the model's robustness and generalizability and reduces the amount of training data required to obtain a desired accuracy for predictions of these properties. Existing open source GNN libraries construct invariant features only for specific GNN architectures. This precludes the generalization of invariant features to arbitrary message passing neural network (MPNN) layers which, in turn, precludes the use of these libraries for new, user-specified predictive tasks. To address this limitation, we implement invariant MPNNs into the flexible and scalable HydraGNN architecture. HydraGNN enables a seamless switch between various MPNNs in a unified layer sequence and allows for a fair comparison between the predictive performance of different MPNNs. We trained this enhanced HydraGNN archi-tecture on the ultraviolet-visible (UV-vis) spectrum of GDB-9 molecules, a feature that describes the molecule's electronic exci-tation modes, computed with time-dependent density functional tight binding (TD-DFTB) and available open source through the GDB-9-Ex dataset. We assess the robustness (i.e., accuracy and generalizability) of the predictions obtained using different invariant MPNNs with respect to different values of the full width at half maximum (FWHM) for the Gaussian smearing of the theoretical peaks. Our numerical results show that incorporating invariance in the HydraGNN architecture significantly enhances both accuracy and generalizability in predicting UV-vis spectra of organic molecules.
This work presents an open-source code, FEgen, that generates initial field emission distributions based on the time-dependent Fowler–Nordheim equation. Current in-demand and open-source beam tracking software—such as ASTRA, IMPACT-T, and GPT—do not contain native distribution generator functions suitable for field emitted electron tracking and phase space analysis. Furthermore, the intuitive graphical user interface of FEgen generates input files shown to be fully compatible with both ASTRA and GPT. Here, detailed application examples demonstrating a subset of FEgen’s capabilities are given by creating a customized emitter pattern with application to transverse beam shaping and longitudinal beam dynamic experiments.
Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.
The increasing penetration of distributed energy resources (DERs) is significantly reshaping the role of distribution systems under active energy management. To aggregate the active-reactive power flexibility of DERs dispersed at the feeder and provide capacity support to the transmission system, it is essential to efficiently identify feasible substation power injection trajectories. This paper introduces a novel ahead-of-time flexibility characterization method to address it. First, a polyhedral non-feeder-level power flexibility region (PFR) is constructed, accounting for various time-dependent, power-coupled, and forecast error uncertainties. Then, a polyhedral feeder-level PFR is analytically derived through a coordinate transformation, which can reveal the uncertainty propagation path, i.e., how uncertainty applies to the feeder-level PFR. To facilitate the high-level application, a tractable chance-constrained Chebyshev centering optimization model is further developed to find a ball-shaped inner approximation of the feeder-level PFR. Finally, the proposed method is validated on a modified IEEE 123-bus test system. Here, both theoretical and experimental results show that, with appropriate robustness parameter settings, the proposed method can make the approximated PFR less conservative with abundant robustness against forecast error uncertainty.
Here, we report first-time results for in-operando flash sintering synchrotron experiments carried out in current rate mode where the specimen, held at a constant temperature, is fed current that is increased at a constant rate. These experiments are unique because the time dependence of the sintering behavior can be stretched out over a longer period (by changing the current rate) than in voltage-to-current experiments in which sintering occurs in a burst at the onset of the flash. Two results are presented: (i) A comparison of temperatures measured with the platinum standard to those predicted by the black body radiation model leading to estimates of the emissivity as a function of porosity whereby emissivity increases from 0.65 to 0.9 as the sample sinters from its green state to full density, and (ii) measurements of the excess lattice expansion as a function of density as the sample sinters continuously while the current is increased. The present work highlights the promise of current rate experiments to obtain results while the sample sinters gradually from its green density to full density (somewhat akin to conventional sintering) for gaining further insights into the mechanisms of flash sintering.