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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Towards Off-policy Evaluation as a Prerequisite for Real-world Reinforcement Learning in Building Control

We present an initial study of off-policy evaluation (OPE), a problem prerequisite to real-world reinforcement learning (RL), in the context of building control. OPE is the problem of estimating a policy's performance without running it on the actual system, using historical data from the existing controller. It enables the control engineers to ensure a new, pretrained policy satisfies the performance requirements and safety constraints of a real-world system, prior to interacting with it. While many methods have been developed for OPE, no study has evaluated which ones are suitable for building operational data, which are generated by deterministic policies and have limited coverage of the state-action space. After reviewing existing works and their assumptions, we adopted the approximate model (AM) method. Furthermore, we used bootstrapping to quantify uncertainty and correct for bias. In a simulation study, we evaluated the proposed approach on 10 policies pretrained with imitation learning. On average, the AM method estimated the energy and comfort costs with 1.84% and 14.1% error, respectively.

Chen, B↗
DOE OSTI2026

Supervised Learning-Based Spatial Position Estimation with Vertical Displacement for Hovering UAV Wireless Power Transfer

This study presents a supervised learning-based spatial position estimation approach for wireless power transfer (WPT) systems supporting hovering unmanned aerial vehicle (UAV) charging. Unlike stationary charging scenarios, hovering UAVs introduce continuous lateral misalignment and vertical displacement, leading to variations in magnetic coupling and reduced power transfer efficiency. To address this challenge, the proposed method estimates the relative spatial position of the receiver coil using only electrical measurements obtained at the secondary side. A supervised learning model is trained to map output voltage and current features to spatial coordinates, enabling position awareness without requiring external sensors, vision systems, or communication links. The sensing functionality is inherently integrated into the WPT system, allowing simultaneous power transfer and localization through the same magnetic interface. Experimental validation is conducted on a laboratory-scale prototype under varying lateral offsets and air-gap conditions. In addition, spline-based interpolation is employed to increase spatial data density for training. The results demonstrate that the proposed framework can capture spatial variations associated with both lateral and vertical displacement, providing reliable position estimation under hovering conditions. This work establishes a hardware-efficient, sensorless solution for UAV wireless charging and serves as a baseline for advanced data-driven position estimation methods in dynamic WPT systems.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2026

A method for estimating light quenching in inorganic scintillator detectors for radioactive ion beam experiments

In recent experiments, inorganic scintillators have been used to study the decays of exotic nuclei, providing an alternative to silicon detectors and enabling measurements that were previously impossible. However, proper use of these materials requires us to understand and quantify the scintillation process, specifically in response to very heavy nuclei. Here, in this work, we show a simplified method based on the models of Birks (1951) and Meyer and Murray (1962) to parametrize the light output of inorganic scintillators in response to beams of energetic heavy ions over a broad range of energies. We test the accuracy of our parametrization approach by calculating light output and quenching factors for various ions and comparing them with experimental data from Lutetium Yttrium Orthosilicate (LYSO:Ce), a common inorganic scintillator. The Meyer–Murray model suggests that, for sufficiently heavy ions at high energies, the majority of the light output is associated with the creation of delta electrons, which are induced by the passage of the beam through the material. These delta electrons dramatically impact the response of detection systems when subject to ions with velocities typical of beams in modern fragmentation facilities. To illustrate this, we also present a qualitative estimate of the effects of delta rays on overall light output using the Birks–Meyer–Murray parametrization. The approach presented herein will serve as a basic framework for further, more rigorous studies of scintillator response to heavy ions. This work is a crucial first step in planning future experiments where energetic exotic nuclei are interacting with scintillator detectors.

Heavy ion↗
DOE OSTI2022

Methods for estimating X-ray machine output through measurement and simulation

We report ball grid arrays are increasingly being applied in the electronics industry and may require X-ray inspection to ensure the integrity and correct placement of solder pins. However, as the architecture of integrated circuits continues to narrow while simultaneously growing more complex, the risk of electronic failure due to radiation damage increases. While medical X-ray devices have been held to high standards and are repeatedly shown to be well characterized, devices used for electronic inspection are often lacking detailed characterization. This study presents unique methods to solve for important properties in X-ray inspection devices such as source to object distance and energy spectrum. This information can then be applied to Monte Carlo models to achieve better overall dose estimates to electronics, which will lead to superior manufactured products. Since X-ray devices can vary greatly in source characteristics, this work investigates spectral measurement and Monte Carlo representation of three X-ray devices. For a Philips SRO 33 100 medical diagnostic device, the spectral output followed expected trends given by the prediction software SpekCalc and Spektr. For the Dage XD7500NT, direct measurement showed a spectral artifact that through the use of Gafchromic films, was shown to be a contributing effect in the dose output. For the Rad Source RS1800, a high powered irradiation device, direct spectral measurement was not achieved. However, a Monte Carlo model using an assumed spectra was found to match ion chamber measurements to a high degree.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A data-driven method to estimate contamination from light ion beam transmutation at colliders

Collisions of relativistic light ions such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time-dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple mode

Accelerator Physics (physics.acc-ph)↗
DOE OSTI2026

Data-driven method to estimate contamination from light ion beam transmutation at colliders

Collisions of relativistic light ions, such as oxygen, neon, and magnesium, have been proposed as a way to examine the system-size dependence of dynamics typically associated with the quark-gluon plasma produced in collisions of heavier ions such as xenon, gold, or lead. Recent efforts at both the Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) have produced large datasets of proton-oxygen, oxygen-oxygen, and neon-neon collisions, catalyzing intense interest in experimental backgrounds associated with light-ion collisions. In particular, electromagnetic dissociation of light ions while they are circulating in a collider can result in beam contamination that is difficult to simulate precisely. Here we propose a data-driven method for evaluating the potential impact of beam contaminants on physics analyses. The method exploits the time dependence and smaller size of contaminant ion species to define control regions that can be used to quantify potential contamination effects. A simple model is used to illustrate the method and to study its robustness. Furthermore, this method can inform studies of recent LHC and RHIC data and could also be useful for future light-ion programs at the LHC and beyond.

Beam loss↗
DOE OSTI2020

Efficient method for estimation of fission fragment yields of $\textit{r}$-process nuclei

Background: More than half of all the elements heavier than iron are made by the rapid neutron capture process (or $\textit{r}$ process). For very-neutron-rich astrophysical conditions, such at those found in the tidal ejecta of neutron stars, nuclear fission determines the $\textit{r}$-process endpoint, and the fission-fragment yields shape the final abundances of 110 ≤ $\textit{A}$ ≤ 170 nuclei. The knowledge of fission-fragment yields of hundreds of nuclei inhabiting very-neutron-rich regions of the nuclear landscape is thus crucial for the modeling of heavy-element nucleosynthesis. Purpose: In this study, we propose a model for the fast calculation of fission-fragment yields based on the concept of shell-stabilized prefragments defined with help of the nucleonic localization functions. Methods: To generate realistic potential-energy surfaces and nucleonic localizations, we apply Skyrme density-functional theory. In this work, the distribution of the neck nucleons among the two prefragments is obtained by means of a statistical model. Results: We benchmark the method by studying the fission yields of 178 Pt, 240 Pu, 254 Cf, and 254,256,258 Fm and show that it satisfactorily explains the experimental data. We then make predictions for 254 Pu and 290 Fm as two representative cases of fissioning nuclei that are expected to significantly contribute during the $\textit{r}$-process nucleosynthesis occurring in neutron-star mergers. Conclusions: The proposed framework provides an efficient alternative to microscopic approaches based on the evolution of the system in a space of collective coordinates all the way to scission. It can be used to carry out global calculations of fission-fragment distributions across the $\textit{r}$-process region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Model correction and updating of a stochastic degradation model for failure prognostics of miter gates

Understanding the degradation of the quoin block is vital for failure prognostics in miter gates. Due to the complicated degradation mechanism, degradation models based on simplifications and assumptions cannot accurately describe the damage evolution. It is observed that small errors in a simplified degradation model can lead to a large discrepancy in the remaining useful life estimation attributed to error accumulation over time. Aiming to address this issue in failure prognostics, this paper presents a dynamic model correction framework for a simplified degradation model using strain measurements. In the proposed framework, a polynomial chaos expansion (PCE) model is employed to compensate the missing physics in a simplified stochastic degradation model. Here, a maximum likelihood estimation method is developed to estimate the uncertain parameters of the simplified physics-based degradation model along with the unknown PCE model parameters using strain measurements as the observables. The updated damage degradation model is then applied to failure prognostics of a miter gate. Results of a case study show that the proposed approach can effectively improve the accuracy of failure prognostics in miter gates.

42 ENGINEERING↗
DOE OSTI2020

Method for estimating the density of high-level nuclear waste glass

A database of over 1100 silicate glass compositions and densities was compiled and used to evaluate the efficacy of an algorithm for estimating the density of silicate glass compositions. We sought to develop a parsimonious algorithm based on the additivity of partial molar volumes of individual oxide components weighted by their mole fraction in a glass composition. Bound molar volumes were used for oxides in which the density of the oxide bound in a glass matrix was previously determined. The bound molar volumes were known for oxides covering 97.5 mole percent of the database compositional space. The measured glass densities were plotted against the estimated glass densities and a linear regression yielded an R 2 adj. = 0.95 and a slope and intercept of approximately one and zero, respectively. This regression suggests that glass densities estimated by the algorithm, within analysis uncertainty, are equal to the measured densities of the glasses. In addition to the development of the density estimation, we corroborated many of the referenced bound molar volume data used in the parameterization of the estimation algorithm via linear regression of the individual partial molar volumes versus the inverse measured densities (specific volumes) of the glasses in the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A data-driven method to estimate the antiproton background in Mu2e

The Mu2e experiment will search for the CLFV process of neutrinoless, coherent conversion of muon to electron in the field of an Al nucleus. One of the expected backgrounds is antiprotons produced by the proton beam at the Production Target and annihilating in the Stopping Target to produce signal-like electrons. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, $p\bar{p}$ annihilation is the only source of events with multiple, simultaneous particles coming from the Stopping Target. We utilized this unique feature and developed a novel approach to reconstruct multi-track events and estimate the antiproton background.

Chithirasreemadam, Namitha [Pisa U.; INFN, Pisa]↗
DOE OSTI2021

Protection of Distribution Circuits with High Penetration of Solar PV: Distance, Learning, and Estimation-Based Methods

The results of DOE Solar Energy Technologies Office project 34233 are presented. The newest version of IEEE Standard 1547 enables photovoltaic inverters to ride through voltage disturbances, improving bulk system reliability, at the expense of removing undervoltage trip as a fast method of de facto fault detection in high PV adoption scenarios. The project explored new methods of fault detection that don't rely on communication systems and could be available quickly, including distance-based schemes, focused directional relays, and two different data-driven schemes. The schemes were evaluated with fast-phasor simulation, electromagnetic transient simulation, and field data collection at partner utilities Chattanooga Electric Power Board and Dominion Energy Virginia. Accomplishments and possible paths forward are summarized.

14 SOLAR ENERGY↗