Search NASA⌕ Search

SEARCH · Search NASA

Results for “assist”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

Quantum dynamics of positron-hydrogen scattering and three-body bound state formation with an assisting laser field: predictions of a reduced-dimensionality model

Abstract We investigate the quantum dynamics of target excitation and positronium formation in the positron-hydrogen atom scattering without and with an external assisting laser field within a reduced-dimensional quantum model. Strong interference fringes between the incident and reflected positron wave packets are observed in the reaction region. We further investigate the critical behavior of transition probabilities near the channel-opening thresholds for hydrogen excitation and positronium formation and find a strong competition between channels with similar threshold energies, but different parities. The transmission ratios of the incident positron in different reaction channels are calculated, and it is shown that only positronium formation in the ground state prefers forward scattering. Our simulation of the positron-hydrogen scattering with an assisting laser field indicates that the three-particle bound states can be formed during the collisions due to the photon emission induced by the external laser field.

Optics↗

Dimensionality effects on trap-assisted recombination: the Sommerfeld parameter

Abstract In the context of condensed matter physics, the Sommerfeld parameter describes the enhancement or suppression of free-carrier charge density in the vicinity of a charged center. The Sommerfeld parameter is known for three-dimensional systems and is integral to the description of trap-assisted recombination in solids. Here we derive the Sommerfeld parameter in one and two dimensions and compare with the results in three dimensions. We provide an approximate analytical expression for the Sommerfeld parameter in two dimensions. Our results indicate that the effect of the Sommerfeld parameter is to suppress trap-assisted recombination in decreased dimensionality.

Physics↗

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗

Microwave-assisted synthesis of iron sulfide motifs for electrochemical applications

Abstract The syntheses of FeS 2 and Fe 3 S 4 nanomaterials were optimized using a novel facile, surfactant-free, and microwave-assisted, one-pot synthesis method, run under ambient and reasonably mild reaction conditions. Synthetic parameters, such as metal precursor salt identity, reaction time, reaction temperature, metal:sulfur molar ratios, and solvent combinations, were all systematically investigated and optimized. A series of FeS 2 (pyrite) samples was initially fabricated using thioacetamide (TAA) as the sulfur precursor to generate a distinctive, uniform octahedra-based morphology. Switching the sulfur precursor from TAA to L-cysteine resulted in a corresponding transformation in not only chemical composition from FeS 2 to an iron thiospinel structure, Fe 3 S 4 (otherwise known as greigite), but also an associated morphological evolution from octahedra to nanosheet aggregates. The study of these materials has enabled crucial insights into the formation mechanisms of these materials under a relatively non-conventional microwave-assisted setting. Furthermore, in separate experiments, multi-walled carbon nanotubes (MWNTs) and graphene were added in with underlying metal sulfide species to create conductive Fe–S/MWNT composites and Fe–S/graphene composites, respectively. The method of addition of either MWNTs or graphene was also explored, wherein an ‘ ex-situ ’ synthetic procedure was found to be the least disruptive means of attachment and immobilization onto iron sulfide co-reagents as a means of preserving the latter’s inherent composition and morphology. The redox acidity for the parent material and associated composites demonstrates the utility of our as-developed synthetic methods for creating motifs relevant for electrochemical applications, such as energy storage.

25 ENERGY STORAGE↗

AI-assisted transport of radioactive ion beams

Beams of radioactive heavy ions allow researchers to study rare and unstable atomic nuclei, shedding light into the internal structure of exotic nuclei and on how chemical elements are formed in stars. However, the extraction and transport of radioactive beams rely on time-consuming expert-driven tuning methods, where hundreds of parameters are manually optimized. Here, in this study, we introduce a system that employs Artificial Intelligence (AI), specifically utilizing Bayesian Optimization, to assist in the transport process of radioactive beams. We apply our methodology to real-life scenarios showing advantages when compared with standard tuning methods. This AI-assisted approach can be extended to other radioactive beam facilities around the world to improve operational efficiency and enhance scientific output.

43 PARTICLE ACCELERATORS↗

Trap-assisted Auger-Meitner recombination in GaN p-i-n diodes

Most properties of semiconductor devices are dominated by shallow impurities. However, deep defects often play an important role, for instance, in recombination processes or high field transport. While a variety of techniques are available to assess the density and energy levels of impurities, other properties, such as the recombination mechanisms of the defects, escape observation. We report on the direct measurement of hot electrons generated by trap-assisted Auger-Meitner recombination (TAAR) in GaN p-i-n diodes. By performing electron emission spectroscopy (EES) on diodes with surfaces activated to negative electron affinity by cesium, we observe the expected overflow electrons of p-i-n diodes under low current injection. However, when operating the devices at higher current densities, as low as ∼25 A/c⁢m 2 , we measure the emission of high-energy electrons. At variance with the observed hot electrons in light-emitting diodes (LEDs) using EES, the hot electrons generated in p-i-n diodes at our tested currents cannot be from eeh Auger-Meitner recombination due to the diodes' significantly lower carrier densities compared to those in LEDs. During our measurements, we observe the emission of accumulated electrons with energies ∼0.42 eV, ∼0.99 eV, ∼1.43 eV, and ∼2.32 eV above the conduction-band minimum (CBM) at various bias conditions, suggesting the existence of conduction-band features in GaN at these energies where electrons can be long-lived, such as satellite-valley minima and inflection points. We also measure incompletely relaxed hot electrons approaching energies 1.97 ± 0.13 eV and 2.94 ± 0.13 eV above the CBM, as the diodes are biased to high currents, suggesting at least some of the TAAR partaking defects have an energy level ≳1.97 eV and ≳2.94 eV away from either the conduction or valence band edges. Additionally, at our highest operating currents, we measure hot electrons with energies 3.28 ± 0.13 eV above the CBM, providing direct evidence of TAAR processes involving shallow impurities. This unexpected observation of TAAR in GaN p-i-n diodes spotlights the importance of further studies of defects in GaN and the necessity to incorporate the multi-phonon emission, radiative, and TAAR capture steps of defect-assisted recombination cycles into device modeling. Furthermore, this experiment demonstrates the applicability of the simplest semiconductor structures, p-i-n diodes, as a test bed to study the rich recombination physics of semiconductor materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Trap-Assisted Auger-Meitner Recombination from First Principles

Trap-assisted nonradiative recombination is known to limit the efficiency of optoelectronic devices, but the conventional multiphonon emission (MPE) process fails to explain the observed loss in wide-band-gap materials. Here, we highlight the role of trap-assisted Auger-Meitner (TAAM) recombination and present a first-principles methodology to determine TAAM rates due to defects or impurities in semiconductors or insulators. We assess the impact on efficiency of light emitters in a recombination cycle that may include both TAAM and carrier capture via MPE. We apply the formalism to the technologically relevant case study of a calcium impurity in InGaN, where a Shockley-Read-Hall recombination cycle involving MPE alone cannot explain the experimentally observed nonradiative loss. We find that, for band gaps larger than 2.5 eV, the inclusion of TAAM results in recombination rates that are orders of magnitude larger than recombination rates based on MPE alone, demonstrating that TAAM can be a dominant nonradiative process in wide-band-gap materials. Furthermore, our computational formalism is general and can be applied to the calculation of TAAM rates in any semiconducting or insulating material.

36 MATERIALS SCIENCE↗

Phonon-Assisted Auger-Meitner Recombination in Silicon from First Principles

Here, we present a consistent first-principles methodology to study both direct and phonon-assisted Auger-Meitner recombination (AMR) in indirect-gap semiconductors that we apply to investigate the microscopic origin of AMR processes in silicon. Our results are in excellent agreement with experimental measurements and show that phonon-assisted contributions dominate the recombination rate in both n-type and p-type silicon, demonstrating the critical role of phonons in enabling AMR. We also decompose the overall rates into contributions from specific phonons and electronic valleys to further elucidate the microscopic origins of AMR. Our results highlight potential pathways to modify the AMR rate in silicon via strain engineering.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Machine Learning-Assisted Stability Boundary Determination of Multiport Autonomous Reconfigurable Solar Power Plants

The multiport autonomous reconfigurable solar (MARS) power plant is a promising solution to integrate renewable resources and energy storage systems into the alternating current (ac) power grid and an high-voltage direct current (HVdc) link. In the MARS system, various input power sources are connected to the individual submodules (SMs) through direct current (dc)–dc converters. However, the presence of external power sources can result in unbalanced capacitor voltages of SMs, thereby violating stability constraints under multiple/diverse operating conditions. This article aims to address the gap by accurately determining the stability boundary of the MARS system. As such, a novel machine learning (ML)-assisted energy balancing control (EBC) criterion is proposed. Further, in conjunction with a refined EBC, this approach ensures balanced capacitor voltages across various types of SMs, significantly enhancing the overall system efficiency. The proposed EBC criterion effectively controls EBC activation and deactivation, achieving remarkable accuracy. Both power systems computer aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulations and control hardware-in-the-loop (cHIL) tests are conducted to validate the feasibility and efficiency of the proposed method. By combining the EBC and ML-assisted EBC criterion, efficient energy management is achieved for systems featuring multiple input power sources, such as MARS. This approach enables the system to fully exploit its potential across an expanded operational range while upholding high-efficiency standards.

14 SOLAR ENERGY↗

Forced Oscillation Source Location in Power Systems Using MVMD-assisted DEF in TF Plane

Fast and accurate forced oscillation source location (FOSL) is critical for mitigating forced oscillations in power systems. Here, to overcome the shortcomings of traditional dissipating energy flow (DEF) in decomposing the forced oscillation components from the measurement responses, a multivariate variational mode decomposition (MVMD)-assisted FOSL is proposed in this paper to locate the forced oscillation sources from measurements in time-frequency (TF) plane. The multi-channel measurement matrix of each generator is formed, then the multi-channel modes are decomposed from the formed multi-channel measurement matrix using the MVMD. Further, the intrinsic mode functions (IMFs) associated with the forced oscillation mode are distinguished from the decomposed modes according to the relative energy weights. With the distinguished IMFs, the MVMD-assisted DEF calculation method is developed to locate the forced oscillation source. Finally, the performance of the proposed method is evaluated using the simulation data of the WECC 179-bus test system and IEEE-NASPI Oscillation Source Location Contest as well as the actual PMU data of ISO New England. The results validated the effectiveness and accuracy of the proposed method in the FOSL.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Comprehensive Studies on Hot Compaction and Vibration-Assisted Compaction Tests of Aluminum Powder

Abstract Aluminum powder compaction was studied using both test and simulation. Cold compaction, hot compaction, and vibration-assisted (cold) compaction tests were conducted to achieve different density ratios. First, the hot compaction test (at 300 °C, compression pressure 140 MPa) improved about 6% compared with cold compaction under the same compression pressure. Second, although the relative density ratio does not obviously improve at a vibration-assisted (cold) compaction, the strength of the specimens made under vibration loading is much better than those of cold compaction. Additionally, finite element models with well-calibrated Drucker–Prager Cap (DPC) material constitutive model were built in abaqus/standard to simulate the powder compaction process. The results of the finite element model have very good correlations with test results up to the tested range, and this finite element model further predicts the loading conditions needed to achieve the higher density ratios. Two exponential equations of the predicted density ratio were obtained by combining the test data and the simulation results. A new analytical solution was developed to predict the axial pressure versus the density ratio for the powder compaction according to DPC material model. The results between the analytical solution and the simulation model have a very good match.

Engineering↗

Direct Numerical Simulation of Partial Fuel Stratification Assisted Lean Premixed Combustion for Assessment of Hybrid G-Equation/Well-Stirred Reactor Model

Partial fuel stratification (PFS) is a promising fuel injection strategy to stabilize lean premixed combustion in spark-ignition (SI) engines. PFS creates a locally stratified mixture by injecting a fraction of the fuel, just before spark timing, into the engine cylinder containing homogeneous lean fuel/air mixture. Further, this locally stratified mixture, when ignited, results in complex flame structure and propagation modes similar to partially premixed flames and allows for faster and more stable flame propagation than a homogeneous lean mixture. This study focuses on understanding the detailed flame structures associated with PFS-assisted lean premixed combustion. First, a two-dimensional direct numerical simulation (DNS) is performed using detailed fuel chemistry, experimental pressure trace, and realistic initial conditions mapped from a prior engine large-eddy simulation (LES), replicating practical lean SI operating conditions. DNS results suggest that the conventional triple flame structure is prevalent during the initial stage of flame kernel growth. Both premixed and nonpremixed combustion modes are present with the premixed mode contributing dominantly to the total heat release. Detailed analysis further reveals the effects of flame stretch and fuel pyrolysis on flame displacement speed. Based on the DNS findings, the accuracy of a hybrid G-equation/well-stirred reactor (WSR) combustion model is assessed for the PFS-assisted lean operation in the LES context. It is found that the G-equation model qualitatively captures the premixed branches of the triple flame, while the WSR model predicts the nonpremixed branch of the triple flame. Finally, potential needs for improvements to the hybrid G-equation/WSR modeling approach are discussed.

33 ADVANCED PROPULSION SYSTEMS↗

Diffusion-assisted molecular beam epitaxy of CuCrO 2 thin films

Using molecular beam epitaxy (MBE) to grow multielemental oxides (MEOs) is generally challenging, partly due to difficulty in stoichiometry control. Occasionally, if one of the elements is volatile at the growth temperature, stoichiometry control can be greatly simplified using adsorption-controlled growth mode. Otherwise, stoichiometry control remains one of the main hurdles to achieving high-quality MEO film growths. Here, in this paper, we report another kind of self-limited growth mode, dubbed diffusion-assisted epitaxy, in which excess species diffuses into the substrate and leads to the desired stoichiometry, in a manner similar to the conventional adsorption-controlled epitaxy. Specifically, we demonstrate that using diffusion-assisted epitaxy, high-quality epitaxial CuCrO 2 films can be grown over a wide growth window without precise flux control using MBE.

36 MATERIALS SCIENCE↗

Multiscale Control of Generic Second Order Traffic Models by Driver-Assist Vehicles

We study the derivation of generic high order macroscopic traffic models from a follow-the-leader particle description via a kinetic approach. First, we recover a third order traffic model as the hydrodynamic limit of an Enskog-type kinetic equation. Next, we introduce in the vehicle interactions a binary control modeling the automatic feedback provided by driver-assist vehicles and we upscale such a new particle description by means of another Enskog-based hydrodynamic limit. The resulting macroscopic model is now a generic second order model (GSOM), which contains in turn a control term inherited from the microscopic interactions. We show that such a control may be chosen so as to optimize global traffic trends, such as the vehicle flux or the road congestion, constrained by the GSOM dynamics. By means of numerical simulations, we investigate the effect of this control hierarchy in some specific case studies, which exemplify the multiscale path from the vehiclewise implementation of a driver-assist control to its optimal hydrodynamic design.

GSOM↗

ChatHPC: Building the Foundations for a Productive and Trustworthy AI-Assisted HPC Ecosystem

ChatHPC democratizes large language models for the high-performance computing (HPC) community by providing the infrastructure, ecosystem, and knowledge needed to apply modern generative AI technologies to rapidly create specific capabilities for critical HPC components while using relatively modest computational resources. Our divide-and-conquer approach focuses on creating a collection of reliable, highly specialized, and optimized AI assistants for HPC based on the cost-effective and fast Code Llama fine-tuning processes and expert supervision. We target major components of the HPC software stack, including programming models, runtimes, I/O, tooling, and math libraries. Thanks to AI, ChatHPC provides a more productive HPC ecosystem by boosting important tasks related to portability, parallelization, optimization, scalability, and instrumentation, among others. With relatively small datasets (on the order of KB), the AI assistants, which are created in a few minutes by using one node with two NVIDIA H100 GPUs and the ChatHPC library, can create new capabilities with Meta’s 7-billion parameter Code Llama base model to produce high-quality software with a level of trustworthiness of up to 90% higher than the 1.8-trillion parameter OpenAI ChatGPT-4o model for critical programming tasks in the HPC software stack.

Young, Aaron [ORNL] (ORCID:0000000254484667)↗

Machine-Learning Assisted Identification of Accurate Battery Lifetime Models with Uncertainty

Reduced-order battery lifetime models, which consist of algebraic expressions for various aging modes, are widely utilized for extrapolating degradation trends from accelerated aging tests to real-world aging scenarios. Identifying models with high accuracy and low uncertainty is crucial for ensuring that model extrapolations are believable, however, it is difficult to compose expressions that accurately predict multivariate data trends; a review of cycling degradation models from literature reveals a wide variety of functional relationships. Here, a machine-learning assisted model identification method is utilized to fit degradation in a stand-out LFP-Gr aging data set, with uncertainty quantified by bootstrap resampling. The model identified in this work results in approximately half the mean absolute error of a human expert model. Models are validated by converting to a state-equation form and comparing predictions against cells aging under varying loads. Parameter uncertainty is carried forward into an energy storage system simulation to estimate the impact of aging model uncertainty on system lifetime. The new model identification method used here reduces life-prediction uncertainty by more than a factor of three (86% ± 5% relative capacity at 10 years for human-expert model, 88.5% ± 1.5% for machine-learning assisted model), empowering more confident estimates of energy storage system lifetime.

25 ENERGY STORAGE↗

Machine-learning-assisted analysis of transition metal dichalcogenide thin-film growth

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. The quantitative analysis of RHEED data allows us to characterize and categorize the growth modes step by step, and extract hidden knowledge of the epitaxial film growth process. In this study, we employed the ML-assisted RHEED analysis method to investigate the growth of 2D thin films of transition metal dichalcogenides (ReSe2) on graphene substrates by MBE. Principal component analysis (PCA) and K-means clustering were used to separate statistically important patterns and visualize the trend of pattern evolution without any notable loss of information. Using the modified PCA, we could monitor the diffraction intensity of solely the ReSe2 layers by filtering out the substrate contribution. These findings demonstrate that ML analysis can be successfully employed to examine and understand the film-growth dynamics of 2D materials. Further, the ML-based method can pave the way for the development of advanced real-time monitoring and autonomous material synthesis techniques.

36 MATERIALS SCIENCE↗

Insights into Designing an Efficient and Reliable Microwave-Assisted Methane Dehydroaromatization Process: Effect of Microwave Absorber on Catalyst Performance

Microwave-assisted methane dehydroaromatization has the potential to address challenges of traditional dehydroaromatization reactions. However, catalysts for microwave-enhanced reaction systems require effective coupling of fields with the catalyst to produce heat and reach reaction temperatures. Here, this work presents an in-depth understanding of the effect of the addition of silicon carbide as a microwave absorber on catalyst performance among other variables, the viability of the microwave reactor configuration, and insights into designing an effective and reliable microwave-based methane dehydroaromatization process. The effect of other parameters including temperature, weight hourly space velocity, role of microwave absorber, and methane concentration during microwave-assisted methane dehydroaromatization reaction are studied. Mo/ZSM-5 was found to suffer from low permittivity and nonuniform heating under microwave conditions. Mixing silicon carbide powder as a microwave absorber with the catalyst was found to provide more uniform heating. When assessing the catalytic performance of the mixture, it was found that higher methane partial pressures at 2000 cc/g cat .h and a temperature range of 500-600°C produced the highest amount of benzene. The formation of graphitic carbon on the spent catalyst increased with temperature, gas-solid contact period, and methane concentration, which resulted in higher methane conversion and benzene selectivity. The study indicates that under microwave heating the presence of localized carbon enhanced catalyst life by coupling with microwave energy, leading to localized heating, and improving benzene selectivity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗