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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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A solid–gas reaction rate modeling framework is applied to characterize degradation in thermochemical materials induced by thermal cycling. Time constants and a kinetic conversion ratio are established for a representative carbonation-calcination reaction, which provides insight into degradation mechanisms and reveals a mitigation strategy by tuning reaction duration.
Non-equilibrium interactions between plasmonic metals and adsorbed molecules lie at the heart of emerging applications such as plasmonic photocatalysis and sensing, though the ultrafast charge and energy transfer mechanisms arising from these interactions are not well understood. Herein, we investigate the ultrafast dynamics of Au nano-islands tethered with a self-assembled monolayer (SAM) of electron-withdrawing 4-mercaptobenzoic acid (4MBA) molecules. Ultrafast UV-visible transient absorption spectroscopy following excitation of the interband transition in Au reveals three well-known, characteristic time constants that quantify electron–electron (el–el), electron–phonon (el–ph) and phonon–phonon (ph–ph) scattering lifetimes. When comparing the dynamics of bare Au and 4MBA-Au, we find that the el–ph and ph–ph scattering lifetimes are notably longer in 4MBA-Au. Density functional perturbation theory calculations ascribe the elongation in el–ph lifetimes in 4MBA-Au to the significant coupling of acoustic phonon modes of Au with certain molecular vibrations of 4MBA, leading to decreased spatial overlap between carrier electronic states and the acoustic modes. We speculate that the elongation of ph–ph scattering lifetimes in 4MBA-Au arises due to poor thermal conductivity of the SAM which disrupts efficient energy dissipation from Au to the environment, thus slowing down the thermalization of phonons. This work provides a glimpse into how molecular adsorbates modify the charge carrier and phonon dynamics of Au and sets the stage for further systematic exploration of plasmonic metal–molecule interactions.
Norrish type I reactions selectively cleave carbon–carbon bonds directly adjacent to carbonyl groups. Despite their broad use in combination with aromatic carbonyls for additive manufacturing and dental UV curing applications, the nature of the photochemically active state and its population mechanism remain insufficiently understood. Detailed mechanistic insight requires mapping of the photoexcited population flow involving internal conversion and intersystem crossing. We present a time-domain study of gas phase acetophenone as a prototypical aromatic carbonyl combining soft X-ray time-resolved near-edge X-ray absorption fine structure (TR-NEXAFS) spectroscopy at the oxygen K-edge with ab initio multiple spawning (AIMS) simulations. Exploiting the specific sensitivity of TR-NEXAFS spectroscopy to states with nπ* character, we observe population transfer from the initially excited 1 ππ* state to the 1 nπ* state with a time constant of (0.13 ± 0.02) ps after an initial induction period of (0.12 ± 0.02) ps without population transfer, in quantitative agreement with the AIMS simulations. The population in the 1 nπ* state subsequently decays via intersystem crossing, likely mediated by a 3 ππ* state, within (3.17 ± 0.66) ps to a long-lived 3 nπ* state, which is presumed to be active towards Norrish type I chemistry.
We have built and commissioned a compact in situ 3He polarizer based on spin-exchange optical pumping for the engineering materials diffractometer, named VULCAN, at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. The system achieved 78% 3He polarization with a short pump-up time constant of 2.2 h. Its first deployment in an in situ half-polarized neutron diffraction experiment on a magnetic shape memory alloy, Ni–Mn–Ga, revealed changes in magnetization coupled to deformation twinning under compression. With this new capability, VULCAN has become the first diffractometer at SNS capable of performing polarized neutron experiments, opening new opportunities to study a wider range of magnetism-related material behaviors under varied thermal, mechanical, electrical, and/or magnetic fields.
One of the challenges of electron density measurements of an atmospheric pressure plasma (APP) with a laser interferometer is the significant and unwanted phase shift caused by changes in the neutral gas density. These unwanted phase shifts can be mitigated and plasma density measured using an interferometer architecture called a dispersion interferometer (DI). A DI is composed of two nonlinear orientation patterned GaAs crystals for frequency doubling and measures the phase shift induced by a plasma in the interference signal between two second-harmonic beams. Measurement of plasma dynamics or a short plasma pulse in less than a millisecond is enabled with heterodyne detection in a DI using an acousto-optic cell with a frequency of 40 MHz. This heterodyne DI (HDI) is targeted to measure APPs in an electron density range of 10 20 –10 24 m −3 . Finally, the HDI achieves a line-integrated density resolution of 2 × 10 15 m −2 (a phase resolution of 0.005°) with a time constant of 1 μs using ensemble averaging techniques.
In this paper, we present a first quantitative test of detected light signals produced in a pulsed neutron source run in a small vertical drift LArTPC at the CERN Neutrino Platform ColdBox test facility. The ColdBox cryostat, detectors, neutron sources, and particle interactions are modeled and simulated using Fluka. We demonstrate the ability to identify the contribution from neutron interactions using X-ARAPUCA photodetectors, and show first comparisons of data to simulation, which indicate reasonable agreement. A time constant is also fitted from the neutron-beam-off light signal spectrum and found consistent between data and simulation. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.
Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shallow-depth circuits. Matrix product states (MPS) comprise a significant class of many-body entangled states, efficiently describing the ground states of one-dimensional gapped local Hamiltonians and finding applications in a number of recent quantum algorithms. Recently, it has been shown that the Affleck-Kennedy-Lieb-Tasaki state—a paradigmatic example of an MPS—can be exactly prepared with an adaptive quantum circuit of constant depth, an impossible feat with local unitary gates alone due to its nonzero correlation length [Smith , PRX Quantum 4, 020315 (2023)]. In this work, we broaden the scope of this approach and demonstrate that a diverse class of MPS can be exactly prepared using constant-depth adaptive quantum circuits, outperforming theoretically optimal preparation with unitary circuits. We show that this class includes short- and long-ranged entangled MPS, symmetry-protected topological (SPT) and symmetry-broken states, MPS with finite Abelian, non-Abelian, and continuous symmetries, resource states for MBQC, and families of states with tunable correlation length. Moreover, we illustrate the utility of our framework for designing constant-depth sampling protocols, such as for random MPS or for generating MPS in a particular SPT phase. We present sufficient conditions for particular MPS to be preparable in constant time, with global on-site symmetry playing a pivotal role. Altogether, this work demonstrates the immense promise of adaptive quantum circuits for efficiently preparing many-body entangled states and provides explicit algorithms that outperform known protocols to prepare an essential class of states. Published by the American Physical Society 2024
The parameters of electric machine thermal equivalent circuit networks are difficult to predict due to material and manufacturing uncertainties. In this paper, a Greybox system identification approach is used to identify parameters of electric machine stator lumped parameter thermal networks (LPTNs). LPTNs provide a low order, computationally efficient, dynamic model of temperatures at specific locations. Second and third order LPTN model structures are defined as state space equations with stator thermal parameters to be identified. To test the Greybox electric machine stator thermal system identification, five stator motorette prototypes were constructed with controlled variations in slot fill and slot liner thickness. The variation in the motorette thermal parameters and thermal time constants are detected using the Greybox identification. Special attention is given to the impact of sampling rate and Greybox data record length on parameter estimation accuracy.
The output capacitance (C OSS ) loss, a loss produced when the device’s output capacitor is charged and discharged, has become a concern for GaN high electron mobility transistors (HEMTs) in high-frequency applications. This work presents a new, easy-to-implement method for the C OSS loss characterization based on the unclamped inductive switching (UIS) setup. As compared to prior approaches, this method involves the device’s ON-state conduction and could measure the C OSS loss in a single pulse and the steady-state switching. The C OSS loss of three types of mainstream commercial GaN HEMTs is characterized, which exhibit some common dependencies including a non-monotonic relation with the dv/dt (or resonance frequency), a linear relation with the ON-state current, a power-law relation with the peak blocking voltage, and little temperature dependence. In addition, their C OSS losses all show minimal distinctions in a single pulse and the steady-state switching, despite the increased on-resistance in the steady-state switching. Furthermore, this suggests that the traps accounting for the C OSS loss possess different de-trapping time constants as compared to the traps governing the dynamic on-resistance. Finally, a unified model is established to describe the C OSS loss of all three types of GaN HEMTs. These results provide important references for the high-frequency application of GaN HEMTs and new insights into the physical origin of their C OSS loss.1
We demonstrate a mid-infrared detector using a phononic crystal resonator fabricated on 128° Y-cut bulk lithium niobate substrate, featuring an absorbed noise equivalent power of 30 pW√ Hz and a response time constant of 0.3 ms.
Here, we demonstrate an ultrafast parametric four-wave mixing method for the collisional-quenching-free detection of atomic hydrogen. Two photons of 243 nm light resonantly excite the n = 1 to n = 2 transition of atomic hydrogen, and a third probe photon scatters from the electronic coherence to generate the parametric emission of a degenerate signal photon. Laser-induced photofragmentation fluorescence from water, also excited with the same 243 nm pulses, is used as a convenient method to visualize beam overlap in space and time. The femtosecond time resolution enables the suppression of unwanted nonresonant contributions to the signal and separation from resonant mixing in water molecules because of the significantly different coherence lifetimes. A folded BOXCARS phase-matching configuration yields a spatially isolated, laser-like, signal beam, amenable to measurements in highly luminous backgrounds. Measurements in an H 2 /CH 4 /air flame demonstrate quantitative agreement with simulated H-atom concentration profiles. A coherence decay time constant of 42 ps is recorded in the atmospheric pressure flame.
Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.
This paper presents a control co-design (CCD) framework tailored to the hydraulic and mechanical subsystems of hydropower systems, aimed at enhancing their dynamic performance and responsiveness to grid demands. Conventional sequential design approaches often fall short in capturing the coupled interactions between plant dynamics and control objectives. The proposed CCD methodology enables simultaneous optimization of key hydro plant parameters - such as governor settings, penstock characteristics, and valve control - with the control system architecture. Results indicate that both CCD and sequential design approaches achieve comparable transient and steady-state performance, with minor discrepancies arising from differences in their optimized parameters. However, CCD demonstrates a distinct advantage by tripling the water time constant (Tw) while maintaining similar dynamic performance to the sequential approach. This increase in Tw enables optimization of penstock dimensions, as it is directly influenced by penstock design, thereby contributing to overall cost optimization.
The neutron sensor qualification device was developed to provide a temperature-controlled environment for neutron sensors and dosimetry, enabling irradiation in a neutron field at the Armed Forces Radiobiology Research Institute (AFRRI) TRIGA reactor facility. The device, constructed from low-activation and low neutron cross-section materials, features a modular tube furnace design with three independently controlled heating zones for controlling axial temperature distribution. Laboratory testing validated the device's thermal performance, including uniform temperature distribution with less than 6°C variation across the central region, a steady-state operational temperature of 350°C achieved in approximately 3 hours, and a cooling time constant of 3.5 hours. External surface temperatures remained safe for handling, with the surrounding aluminum structure remaining at ambient conditions. The results confirm the device’s suitability for neutron sensor qualification experiments, with potential for future operation at higher temperatures and further optimization of performance.
Recent years have seen an increased importance of neural network inference in edge-based scenarios, which impose size and power constraints requiring novel computing devices. These same edge scenarios may require operating over long periods of time, or exposure to extreme environments, resulting in a drift of neural network weights that cause degraded performance. In searching for ways to develop neural network approaches that perform robustly under these conditions, we propose a biologically-inspired mechanism for the dynamic adaptation of within-neuron parameters that is guided by a global context signal carrying information about perturbations and variability in incoming stimuli. Specifically, we demonstrate that adaptive voltage thresholds or neuronal time constants, when informed by a global context signal, can enable network-level mechanisms to recover from perturbed synaptic weights. Consistent with prior literature, the context-modulated approach is effective for recurrent, but not feedforward networks, by modulating network level dynamics. We demonstrate this approach successfully recovers performance in image classification tasks and spatiotemporal tracking tasks under idealized and Gaussian noise as well as for realistic perturbations from a memristive device when exposed to ionizing radiation. Finally, we discuss how this approach enables the design of robust and energy-efficient neuromorphic systems that perform well, even in resource-constrained scenarios with extreme environments such as edge processing.
In recent years, the frequency of forced oscillation events due to control system malfunctions or improper parameter settings has increased. Tuning the parameters of exciters and governor models is crucial for maintaining power system stability. Traditional simulation studies typically involve small transient disturbances or step changes to find optimal parameter sets, but existing optimization algorithms often fall short in fine-tuning for forced oscillations. Identifying the sensitive parameters within these control models is essential for ensuring stability during large, sustained disturbances. This study focuses on identifying these critical exciter and governor model parameters by analyzing their influence on sustained forced oscillations. Using Kundur’s two-area system, we analyze common exciter models such as SCRX, ESST1A, and AC7B, along with governor models like GAST, HYGOV, and GGOV1, utilizing PSS®E software version 34. Sustained forced oscillations are injected at generator-1 of area-1, with individual parameter changes dynamically simulated. By considering a local oscillation frequency of 1.4 Hz and an inter-area oscillation mode of 0.25 Hz, we analyze the impact of each parameter change on the magnitude and frequency of forced oscillations as well as on active and reactive power outputs. This novel approach highlights the most influential parameters of each tested model—such as exciter, governor, and turbine gains, as well as time constant parameters—on the impact of forced oscillations. Based on our findings, the sensitive parameters of each tested model are ranked. These would provide valuable insights for industry operators to fine-tune control settings during oscillation events, ultimately enhancing system stability.
This study aimed to determine the structure–function relationship (SFR) for ChCl–glycerol mixtures, a deep eutectic solvent (DES), by investigating their microscopic solvation dynamics and how it relates to their macroscopic properties across varying concentrations of ChCl. Femtosecond transient absorption (fs-TA) spectroscopy revealed two distinct solvation dynamics time constants: τ 1 , governed by glycerol–glycerol interactions, and τ 2 , dominated by the choline response. The τ 2 minimum at 25–30 mol % ChCl closely aligned with the eutectic composition (~33.33 mol % ChCl), where the glycerol network was the most organized and the choline ions exhibited the fastest relaxation. The viscosity decreased sharply up to ~25 mol % ChCl and then plateaued, while the conductivity increased monotonically with ChCl concentration, reflecting enhanced ionic mobility. The density decreased with both increasing ChCl concentration and temperature, indicating disrupted hydrogen bonding and reduced molecular packing. The polarity, measured using betaine-30 (B30) and the E T (30) polarity scale, increased steeply up to approximately 25 mol % ChCl before reaching a plateau. These findings identified the eutectic composition as the optimal concentration range for balancing stability, fluidity, conductivity, and enhanced dynamics within the glycerol system.
We consider the problem of controlling the frequency response of weakly-coupled multi-machine multi-inverter low-inertia power systems via grid-forming inverter-based resources (IBRs). In contrast to existing methods, our approach relies on dividing the larger system into multiple strongly-coupled subsystems, without ignoring either the underlying network or approximating the subsystem response as an aggregate harmonic mean model. Rather, through a structured clustering and recursive dynamic shaping approach, the frequency response of the overall system to load perturbations is shaped appropriately. We demonstrate the proposed approach for a three-node triangular configuration and a small-scale radial network. Furthermore, for small-scale radial microgrids, we demonstrate the ability of IBRs to tune the effective transfer functions of synchronous machines. This enables us to relax the uniform turbine time-constant assumptions and widen the scope of existing synchronization results for proportionally heterogeneous machines.