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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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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Tunable tunnel barriers in a semiconductor via ionization of individual atoms

individual adatoms deposited on an InSb(110) surface. The adatoms can be reproducibly dropped off from the STM tip by voltage pulses, and impact tunneling into the surface by up to ~100×. The spatial extent and magnitude of the tunneling effect are widely tunable by imaging conditions such as bias voltage, set current and photoillumination. We attribute the effect to occupation of a (+/0) charge transition level, and switching of the associated adatom-induced band bending. The effect in STM topographic images is well reproduced by transport modeling of filling and emptying rates as a function of the tip position. STM atomic contrast and tunneling spectra are in good agreement with density functional theory calculations for In adatoms. The adatom ionization effect can extend to distances greater than 50 nm away, which we attribute to the low concentration and low binding energy of the residual donors in the undoped InSb crystal. These studies demonstrate how individual atoms can be used to sensitively control current flow in nanoscale devices.

Physics↗
DOE OSTI2021

Indexed improvements for real-time trotter evolution of a (1 + 1) field theory using NISQ quantum computers

Today's quantum computers offer the possibility of performing real-time calculations for quantum field theory scattering processes motivated by high energy physics. In order to follow the successful roadmap which has been established for the calculation of static properties at Euclidean time, it is crucial to develop new algorithmic methods to deal with the limitations of current noisy intermediate-scale quantum (NISQ) devices and to establish quantitative measures of the progress made with different devices. In this paper, we report recent progress in these directions. We show that nonlinear aspects of the trotter errors allow us to take much larger step then suggested by low-order analysis. This is crucial to reach physically relevant time scales with today's NISQ technology. We propose to use an index averaging absolute values of the difference between the accurately calculated trotter evolution of site occupations and their actual measurements on NISQ machines (G index) as a measure to compare results that have been obtained from different hardware platforms. Using the transverse Ising model in one spatial dimension with four sites we apply this metric across several hardware platforms. We study the results including readout mitigation and Richardson extrapolations and show that the mitigated measurements are very effective based on the analysis of the trotter step size modifications. Furthermore, we discuss how this advance in the trotter step size procedures can improve quantum computing physics scattering results and how this technical advance can be applied to other machines and noise mitigation methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Machine Learning-based Approach to Predict the Aggregate Flexibility of HVAC Systems

Use of renewable energy resources can play a significant role in mitigation of climate change since they reduce the dependency on fossil fuels. However, high penetration of renewable energy resources into the power grid may cause some reliability problems. Ancillary services are key to realize the potential of renewable resources without disturbing the grid reliability. In recent years, many control approaches focused on exploiting the aggregate flexible heating, ventilation, and air-conditioning (HVAC) loads of buildings. However, there are still research gaps in assessing in advance the resulting indoor temperature responses for given total power and weather profiles before implementing the controls in the buildings. Such an assessment is critical to guarantee satisfying both the grid related objective (reliability) and the building related objective (occupants' comfort). Towards addressing this research gap, this paper presents a machine-learning approach for predicting the ratings of indoor temperature responses. In this approach, a prediction model was developed using a dataset generated by a set of model-free control simulations. The developed model achieved high prediction accuracy and showed the potential of the proposed approach.

Amasyali, Kadir↗
DOE OSTI2021

Stochastic Pricing Game for Aggregated Demand Response Considering Comfort Level

In recent years, demand response (DR) has been explored as a fundamental strategy for demand-side management due to its advantages in mediating intermittency of renewable energy generation, load shifting, etc. To engage customers in DR programs, several deterministic price-based DR strategies have been developed and implemented. However, the stochastic weather conditions and occupants' consumption behaviors often make the deterministic solution less robust to uncertainties. In this paper, with the consideration of the uncertainties, a stochastic Stackelberg game is proposed to model the price-demand negotiation between a distributed system operator and load aggregators, where the virtual battery constraints are extracted from the building thermostatically controlled loads (TCLs)‘ characteristics to guarantee comfortable TCLs' levels. Following the negotiation, a priority-based control method is used to allocate the optimal aggregated power DR profile at the building level and track the power signal. Several groups of experiments have demonstrated the effectiveness and robustness of the stochastic solutions.

Chen, Yang↗
NASA NTRS1975

Results of investigations on a 0.010-scale 140A/B configuration space shuttle vehicle orbiter model 72-0 in the NASA/Langley Research Center continuous flow hypersonic tunnel (OA90)

Data are documented which were obtained during wind tunnel tests. The test was conducted beginning 4 March and ending 6 March 1974 for a total of 24 occupancy hours. all test runs were conducted at a Mach number of 10.3 and at Reynolds numbers of 0.65, 1.0 and 1.33 million per foot. Only the complete 140A/B was tested with various elevon, speedbrake, and bodyflap settings at angles of attack from 12 to 37 degrees at 0 and -5 degrees of beta, and from 0 to -9 degrees of beta at 20 and 30 degrees angle of attack. The purpose was to obtain hypersonic longitudinal and lateral-directional stability and control characteristics of the updated space shuttle vehicle configuration.

Hawthorne, P. J.↗
NASA NTRS1979

Adaptive allocation of decisionmaking responsibility between human and computer in multitask situations

As human and computer come to have overlapping decisionmaking abilities, a dynamic or adaptive allocation of responsibilities may be the best mode of human-computer interaction. It is suggested that the computer serve as a backup decisionmaker, accepting responsibility when human workload becomes excessive and relinquishing responsibility when workload becomes acceptable. A queueing theory formulation of multitask decisionmaking is used and a threshold policy for turning the computer on/off is proposed. This policy minimizes event-waiting cost subject to human workload constraints. An experiment was conducted with a balanced design of several subject runs within a computer-aided multitask flight management situation with different task demand levels. It was found that computer aiding enhanced subsystem performance as well as subjective ratings. The queueing model appears to be an adequate representation of the multitask decisionmaking situation, and to be capable of predicting system performance in terms of average waiting time and server occupancy. Server occupancy was further found to correlate highly with the subjective effort ratings.

Chu, Y.-Y.↗
DOE OSTI2023

Probing proton cross-shell excitations through the two-neutron removal from 38 Ca

Bound states of the neutron-deficient, near-dripline nucleus 36 Ca were populated in two-neutron removal from the ground state of 38 Ca, a direct reaction sensitive to the single-particle configurations and couplings of the removed neutrons in the projectile wave function. Final-state exclusive cross sections for the formation of 36 Ca and the corresponding longitudinal momentum distributions, both determined through the combination of particle and γ-ray spectroscopy, are compared to predictions combining eikonal reaction theory and shell-model two-nucleon amplitudes from the USDB, USDC, and ZBM2 effective interactions. The final-state cross-section ratio σ($2$$^{+}_{1}$) / σ (0 + ) shows particular sensitivity and is approximately reproduced only with the two-nucleon amplitudes from the ZBM2 effective interaction that includes proton cross-shell excitations into the pf shell. Characterizing the proton pf-shell occupancy locally and schematically, an increase of the sd – pf shell gap by 250 keV yields an improved description of this cross-section ratio and simultaneously enables a reproduction of the B(E2;$0$$^{+}_{1}$ → $2$$^{+}_{1}$) excitation strength of 36 Ca. Furthermore, this highlights an important aspect if a new shell-model effective interaction for the region was to be developed on the quest to model the neutron-deficient Ca isotopes and surrounding nuclei whose structure is impacted by proton cross-shell excitations.

20 ≤ A ≤ 38↗
DOE OSTI2022

An all optical approach for comprehensive in-operando analysis of radiative and nonradiative recombination processes in GaAs double heterostructures

We demonstrate an all optical approach that can surprisingly offer the possibility of yielding much more information than one would expect, pertinent to the carrier recombination dynamics via both radiative and nonradiative processes when only one dominant deep defect level is present in a semiconductor material. By applying a band-defect state coupling model that explicitly treats the inter-band radiative recombination and Shockley–Read–Hall (SRH) recombination via the deep defect states on an equal footing for any defect center occupation fraction, and analyzing photoluminescence (PL) as a function of excitation density over a wide range of the excitation density (e.g., 5–6 orders in magnitude), in conjunction with Raman measurements of the LO-phonon plasmon (LOPP) coupled mode, nearly all of the key parameters relevant to the recombination processes can be obtained. They include internal quantum efficiency (IQE), minority and majority carrier density, inter-band radiative recombination rate (W r ), minority carrier nonradiative recombination rate (W nr ), defect center occupation fraction (f), defect center density (N t ), and minority and majority carrier capture cross-sections (σ t and σ tM ). While some of this information is thought to be obtainable optically, such as IQE and the W r /W nr ratio, most of the other parameters are generally considered to be attainable only through electrical techniques, such as current-voltage (I-V) characteristics and deep level transient spectroscopy (DLTS). Following a procedure developed herein, this approach has been successfully applied to three GaAs double-heterostructures that exhibit two distinctly different nonradiative recombination characteristics. The method greatly enhances the usefulness of the simple PL technique to an unprecedented level, facilitating comprehensive material and device characterization without the need for any device processing.

14 SOLAR ENERGY↗
DOE OSTI2023

Predicting Building Envelope Construction from In-Situ Thermal Testing

When embarking on a retrofit of a building envelope, it is critical to understand the composition of its assembly. This practice is currently done by destructive and invasive material testing or demolition, which is sometimes not possible when in historic or protected buildings. To address this problem, in-situ thermal testing can be utilized along with machine learning classification algorithms to infer the composition of an assembly. In this paper, a proof-of-concept K-nearest neighbors classification model is developed to classify assembly composition from effective thermal resistance, effective thermal mass, and assembly cladding. This model was trained and tested utilizing a synthetic dataset producing an F1-score of 94.6%. This model was also validated with experimental data from a 100-year old wall assembly, confirming the model’s real-world validity. The paper presents a framework for inferring as-built envelope assemblies, all without having to damage or disturb the building and its occupants.

Pilet, Tyler J.↗
NASA NTRS1991

Analysis of an initial lunar outpost life support system preliminary design

A preliminary design of a life-support system (LSS) was developed as part of an ongoing comprehensive trade study of advanced processor technologies and system architectures for an initial lunar outpost. The design is based on a mission scenario requiring intermittent occupation of a lunar-surface habitat by a crew of four. It incorporates physiochemical process technologies that were considered for Space Station Freedom. A system-level simulation model of the design was developed to obtain steady-state material balances for each LSS processor. The mass-flow rate predictions were used to obtain estimates of the LSS mass, volume, and power consumption by means of processor-sizing correlations that were extrapolated from Space Station Freedom processor designs. The results were used to analyze the impacts of varying crew size, mission duration, processor-operation strategy, and crew-cabin loads on the LSS mass, average power consumption, volume, periodic resupply mass, and waste-accumulation rates. The merits of the design were quantified relative to an open-loop LSS, and the implications of this assessment for future LSS research and technology development were identified.

Ballin, Mark G.↗
NASA NTRS2013

A Binary-Encounter-Bethe Approach to Simulate DNA Damage by the Direct Effect

The DNA damage is of crucial importance in the understanding of the effects of ionizing radiation. The main mechanisms of DNA damage are by the direct effect of radiation (e.g. direct ionization) and by indirect effect (e.g. damage by.OH radicals created by the radiolysis of water). Despite years of research in this area, many questions on the formation of DNA damage remains. To refine existing DNA damage models, an approach based on the Binary‐Encounter‐Bethe (BEB) model was developed[1]. This model calculates differential cross sections for ionization of the molecular orbitals of the DNA bases, sugars and phosphates using the electron binding energy, the mean kinetic energy and the occupancy number of the orbital. This cross section has an analytic form which is quite convenient to use and allows the sampling of the energy loss occurring during an ionization event. To simulate the radiation track structure, the code RITRACKS developed at the NASA Johnson Space Center is used[2]. This code calculates all the energy deposition events and the formation of the radiolytic species by the ion and the secondary electrons as well. We have also developed a technique to use the integrated BEB cross section for the bases, sugar and phosphates in the radiation transport code RITRACKS. These techniques should allow the simulation of DNA damage by ionizing radiation, and understanding of the formation of double‐strand breaks caused by clustered damage in different conditions.

Plante, Ianik↗
DOE OSTI2021

Mechanistic insights into carbon–carbon coupling on NiAu and PdAu single-atom alloys

Carbon–carbon coupling is an important step in many catalytic reactions, and performing sp 3 –sp 3 carbon–carbon coupling heterogeneously is particularly challenging. It has been reported that PdAu single-atom alloy (SAA) model catalytic surfaces are able to selectively couple methyl groups, producing ethane from methyl iodide. In this work, we extend this study to NiAu SAAs and find that Ni atoms in Au are active for C–I cleavage and selective sp 3 –sp 3 carbon–carbon coupling to produce ethane. Furthermore, we perform ab initio kinetic Monte Carlo simulations that include the effect of the iodine atom, which was previously considered a bystander species. We find that model NiAu surfaces exhibit a similar chemistry to PdAu, but the reason for the similarity is due to the role the iodine atoms play in terms of blocking the Ni atom active sites. Specifically, on NiAu SAAs, the iodine atoms outcompete the methyl groups for occupancy of the Ni sites leaving the Me groups on Au, while on PdAu SAAs, the binding strengths of methyl groups and iodine atoms at the Pd atom active site are more similar. These simulations shed light on the mechanism of this important sp 3 –sp 3 carbon–carbon coupling chemistry on SAAs. Furthermore, we discuss the effect of the iodine atoms on the reaction energetics and make an analogy between the effect of iodine as an active site blocker on this model heterogeneous catalyst and homogeneous catalysts in which ligands must detach in order for the active site to be accessed by the reactants.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

New Approach Methodology for Assessing Inhalation Risks of a Contact Respiratory Cytotoxicant: Computational Fluid Dynamics-Based Aerosol Dosimetry Modeling for Cross-Species and In Vitro Comparisons

Regulatory agencies are considering alternative approaches to assessing inhalation toxicity that utilizes in vitro studies with human cells and in silico modeling in lieu of additional animal studies. In support of this goal, computational fluid-particle dynamics models were developed to estimate site-specific deposition of inhaled aerosols containing the fungicide, chlorothalonil, in the rat and human for comparisons to prior rat inhalation studies and new human in vitro studies. Under bioassay conditions, the deposition was predicted to be greatest at the front of the rat nose followed by the anterior transitional epithelium and larynx corresponding to regions most sensitive to local contact irritation and cytotoxicity. For humans, simulations of aerosol deposition covering potential occupational or residential exposures (1–50 µm diameter) were conducted using nasal and oral breathing. Aerosols in the 1–5 µm range readily penetrated the deep region of the human lung following both oral and nasal breathing. Under actual use conditions (aerosol formulations >10 µm), the majority of deposited doses were in the upper conducting airways. Beyond the nose or mouth, the greatest deposition in the pharynx, larynx, trachea, and bronchi was predicted for aerosols in the 10–20 µm size range. Only small amounts of aerosols >20 µm penetrated past the pharyngeal region. Using the ICRP clearance model, local retained tissue dose metrics including maximal concentrations and areas under the curve were calculated for each airway region following repeated occupational exposures. These results are directly comparable with benchmark doses from in vitro toxicity studies in human cells leading to estimated human equivalent concentrations that reduce the reliance on animals for risk assessments.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2021

Development of Advanced Smart Ventilation Controls for Residential Applications

This study examined the use of zoned ventilation systems using a coupled CONTAM/EnergyPlus model for new California dwellings. Several smart control strategies were developed with a target of halving ventilation-related energy use, largely through reducing dwelling ventilation rates based on zone occupancy. The controls were evaluated based on the annual energy consumption relative to continuously operating non-zoned, code-compliant mechanical ventilation systems. The systems were also evaluated from an indoor air quality perspective using the equivalency approach, where the annual personal concentration of a contaminant for a control strategy is compared to the personal concentration that would have occurred using a continuously operating, non-zoned system. Individual occupant personal concentrations were calculated for the following contaminants of concern: moisture, CO2, particles, and a generic contaminant. Zonal controls that saved energy by reducing outside airflow achieved typical reductions in ventilation-related energy of 10% to 30%, compared to the 7% savings from the unzoned control. However, this was at the expense of increased personal concentrations for some contaminants in most cases. In addition, care is required in the design and evaluation of zonal controls, because control strategies may reduce exposure to some contaminants, while increasing exposure to others.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2010

Modeling Respiratory Toxicity of Authentic Lunar Dust

The lunar expeditions of the Apollo operations from the 60 s and early 70 s have generated awareness about lunar dust exposures and their implication towards future lunar explorations. Critical analyses on the reports from the Apollo crew members suggest that lunar dust is a mild respiratory and ocular irritant. Currently, NASA s space toxicology group is functioning with the Lunar Airborne Dust Toxicity Assessment Group (LADTAG) and the National Institute for Occupational Safety and Health (NIOSH) to investigate and examine toxic effects to the respiratory system of rats in order to establish permissible exposure levels (PELs) for human exposure to lunar dust. In collaboration with the space toxicology group, LADTAG and NIOSH the goal of the present research is to analyze dose-response curves from rat exposures seven and twenty-eight days after intrapharyngeal instillations, and model the response using BenchMark Dose Software (BMDS) from the Environmental Protection Agency (EPA). Via this analysis, the relative toxicities of three types of Apollo 14 lunar dust samples and two control dust samples, titanium dioxide (TiO2) and quartz will be determined. This will be executed for several toxicity endpoints such as cell counts and biochemical markers in bronchoaveolar lavage fluid (BALF) harvested from the rats.

Santana, Patricia A.↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Valence 1⁢𝑠−0⁢𝑑 proton vacancy of the 32 Si ground state

The 32 Si( 3 He,d) 33 P reaction was studied in inverse kinematics at 6.3 MeV/u. States in 33 P corresponding to the proton 1s-0d single-particle orbitals were identified up to ≈ 4.5 MeV in excitation energy. The ( 3 He,d) spectroscopic factors were determined from distorted-wave Born approximation calculations. When combined with complementary neutron-adding data, the 1s-0d proton vacancies in the 32 Si ground state were extracted. In conjunction with a reanalysis of data from previous single-particle measurements, the trends in proton and neutron vacancy were explored across the 28,30,32,34 Si isotopes. Both proton and neutron vacancy data show gradual changes in their occupancies. The proton 1s 1/2 orbitals in 32 Si and 34 Si are both consistent with being empty. In conclusion, the ground-state nucleon distributions are described by shell-model calculations constrained to the 1s-0d model space.

Watwood, N. [Argonne National Laboratory (ANL), Ar↗