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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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To report the long-term outcome of patients with prostate cancer treated with external beam radiation therapy and high dose rate (HDR) brachytherapy from a prospective multi-institutional trial conducted by NRG Oncology/RTOG.
We report long-term outcomes from our phase 1 dose-escalation study to determine the maximum tolerated dose of single-fraction liver SABR pooled with our subsequent single institutional experience with patients treated postprotocol at the highest dose level (40 Gy) established from the phase 1 study.
To report long-term outcome of fractionated stereotactic body radiation therapy (SBRT) for painful spinal metastases.
Uncertainties in relative biological effectiveness (RBE) constitute a major pitfall of the use of protons in clinics. An RBE value of 1.1, which is based on cell culture and animal models, is currently used in clinical proton planning. The purpose of this study was to determine RBE for temporal lobe radiographic changes using long-term follow-up data from patients with nasopharyngeal carcinoma.
Long-term outcomes after external beam radiation therapy (EBRT) and radiofrequency ablation (RFA) for medically inoperable early-stage non-small cell lung cancer (NSCLC) are not well known.
This study introduces an innovative approach to modeling meandering river morphology, integrating and investigating the effects of geometric characteristics and vegetation-induced channel coarsening. The developed comprehensive framework combines several advanced techniques: Genetic Programming for refining the scour factor of transverse bed slope, a Leaf Area Index (LAI)-enhanced analytical model for quantifying vegetative flow resistance, and an upstream-weighted moving average method for efficient approximation of the convolution integral in meander migration calculations. The model is validated against both an idealized Kinoshita meander and a natural bend of the Tumen River (China) in equilibrium, demonstrating its robustness across diverse scales and conditions. The model's ability to simulate the long-term evolution, including cutoff events, provides valuable insight for river management strategies. The current findings demonstrate that channel geometry, particularly width-to-depth ratio, plays a dominant role in meander evolution, with wider channels prone to more complex and rapid morphological changes. Vegetation effects are most pronounced in channels with moderate width-to-depth ratios, where they can significantly influence migration rates and bed topography. A combination of channel widening and deepening, coupled with strategic vegetation management, can effectively enhance navigability while maintaining channel stability in the studied Tumen River reach. Sensitivity analyses highlight the complex interplay between hydraulic conditions, sediment characteristics, and vegetation in shaping river morphology. This research advances understanding of the multifaceted nature of meandering river systems and offers practical tools for informed decision-making in river engineering and environmental management, particularly in the context of climate change and increasing anthropogenic pressures on fluvial ecosystems.
An increasingly decarbonized yet resilient power grid requires the corresponding build-out of dispatchable zero-emission resources to supply peak power. However, there is a recognized dearth of solutions which can serve multi-day peak demand events both cost-effectively and with near-term deployability. Here, we find that pairing low-cost automotive fuel cells with hydrogen storage in salt caverns can serve as a peaker plant at less than 500 US$/kW at present, a fraction of the cost of conventional fossil fuel-fired peakers. We demonstrate the peaker’s value for long duration storage by comparing it with pumped hydro and assessing its profitability within Texas’ energy-only market region. Although deployment of these peakers is constrained by the presence of salt caverns, we show that a number of sites in the United States and Europe are endowed with suitable salt formations, while utilizing hydrogen storage in pressurized containers could form a location-agnostic peak power solution.
Here, the formation of γ′/γ″ co-precipitates is investigated in Ni-based superalloys with a varying Ti/Al ratio and Ta content and their stability is studied using long-term high-temperature exposure. Transmission electron microscopy and atom probe tomography analyses demonstrate that both higher Ti/Al ratios and increased Ta promote γ″ phase formation, leading to sandwich and compact structures. The compact co-precipitation significantly reduces γ′ precipitate coarsening during 10,000 h exposure at 700°C by restricting elemental diffusion, particularly of aluminum, from the γ matrix to the γ′ phase. For the alloy without a compact structure, and only γʹ precipitates, at the beginning of the exposure, the coarsening rate over 10,000 h was 3.5 times faster than for the alloy with compact γʹ/γʺ precipitates. Evidence of destabilization of the compact morphology was found to occur between 5,000 and 10,000 h exposure and originated from the extensive formation and growth of δ platelets that extended throughout the grains. Thus, the outer layer of the compact, which consists of γʺ, was subjected to the γ″ to δ phase transformation.
We report a novel methodology for accurate treatment of viscous terms in evaporation problems. The proposed scheme is an extension of the sharp viscous treatment of Kang et al. (2000) [7] to 3D phase change problems. To ensure accuracy and grid converging solutions, a new implicit approach to computing viscous fluxes across the phase interfaces is proposed, which was previously unavailable in fixed grid numerical schemes. Analytical relations were derived for the jump in velocity gradients across the 2D and 3D phase interfaces, an important constituent of the proposed scheme. The relations show a non-vanishing jump in the tangential gradients across the phase interface that are associated with evaporative flux and interfacial curvature. The proposed methodology demonstrated first order accuracy in canonical test cases. It is general and applicable to arbitrarily oriented interfaces, and can be readily implemented in existing evaporation flow solvers.
Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, quantum algorithms have shown promise in solving linear systems of equations as well as systems of linear ordinary differential equations (ODEs) and partial differential equations (PDEs). Reducedorder modeling (ROM) algorithms for studying fluid dynamics have shown success in identifying linear operators that can describe flowfields, where dynamic mode decomposition (DMD) is a particularly useful method in which a linear operator is identified from data. In this work, DMD is reformulated as an optimization problem to propagate the state of the linearized dynamical system on a quantum computer. This reformulation was chosen as a means of facilitating implementation on a near-term quantum computer. Quadratic unconstrained binary optimization (QUBO), a technique for optimizing quadratic polynomials in binary variables, allows for quantum annealing algorithms to be applied. A quantum circuit model (quantum approximation optimization algorithm, QAOA) is utilized to obtain predictions of the state trajectories. Results are shown for the quantum-ROM predictions for flow over a 2D cylinder at Re = 220 and flow over a NACA0009 airfoil at Re = 500 and α = 15°. The quantum-ROM predictions are found to depend on the number of bits utilized for a fixed point representation and the truncation level of the DMD model. Comparisons with DMD predictions from a classical computer algorithm are made, as well as an analysis of the computational complexity and prospects for future, more fault-tolerant quantum computers.
An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. The final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.
In the quest for net-zero greenhouse gas emission, CO 2 capture and storage technologies play a prominent role. Estimates suggest a potential of sequestering up to several thousand metric gigatons of CO 2 . However, some of the main challenges in its development are an assessment of reservoir-specific storage capacities due to variabilities and heterogeneities in the underlying properties (structure, formations, facies, petrophysics, reservoir architecture, and others) and understanding the migration of CO 2 in the subsurface. How much CO 2 can be stored in a reservoir, and how quickly can it be injected? What will be the dominant storing (trapping) mechanisms? These questions, among others, remain as challenges to the commercial deployment of this technology. This article critically investigates CO 2 plume characteristics and determines the evolving contributions of different trapping mechanisms during a 100-year injection period. Here, simulation results on the CO 2 plume dynamics indicate that lateral propagation is much larger than vertical propagation. The ratio of average lateral to vertical dispersion ranged between 6.3 and 18.8 for the cases investigated. On average, for all the scenarios investigated, the free phase (mobile supercritical CO 2 ) is the dominant trapping mechanism (in terms of the amount of CO 2 stored) during the injection period, accounting up to 45% of the CO 2 injected. Both residual (30%) and solubility (25%) trapping storage ratios decline with time during injection, and no significant mineral trapping appeared during the 100-year injection period. However, the behavior of CaCO 3 suggests a predominant dissolution of CaCO 3 , especially in the vicinity of the injection well.
Corrosion mitigation has long been considered a challenge in long-term service of both pyrochemical reprocessing and molten salt reactor applications. Corrosion coupons consisting of 2.25Cr 1Mo ASME SA387 Grade 22 Class 2 steel, were installed in a uranium electrorefiner containing molten chloride salt at 500°C. This electrorefiner went into service at Idaho National Laboratory in 1994. Seven of these coupons were removed in 2019 (i.e., submerged in molten salt for 25 years) for analysis and characterization including neutron imaging, optical microscopy, scanning electron microscopy, and wave/energy dispersive x-ray spectroscopy. The results showed that all seven coupons experienced between 30 to 175 µm of surface degradation without evidence of brittle fracture. Meaning there was no evidence of accelerated corrosion or fractures, and the average steel surface loss was of less than 5 µm per year. All indications were that the environmental conditions inside the electrorefiner were favorable for the steel alloy.
The authors designed and executed the integrated assessment modeling for the United States long-term strategy. They bring diverse expertise to the modeling and analysis of United States decarbonization. Russell Horowitz, Matthew Binsted, and Haewon McJeon are scientists at the Joint Global Change Research Institute, a partnership between Pacific Northwest National Laboratory and the University of Maryland. Allen Fawcett, James McFarland, and Morgan Browning are economists at the Environmental Protection Agency’s Climate Economics Branch. Claire Henly is White House Fellow at the Office of the US Special Presidential Envoy for Climate. Nathan Hultman is the Director of the Center for Global Sustainability at the University of Maryland.
Water exposure of layered nickel-rich transition metal oxide electrodes, widely used in high-energy lithium-ion batteries, has detrimental effects on the electrochemical performance, which complicates electrode handling and prevents implementation of environmentally benign aqueous processing procedures. Elucidating the degradation mechanisms in play may help rationally mitigate/circumvent key challenges. Here, the bulk structural consequences of long-term (>2.5 years) deuterated water (D2O) exposure of intercalation materials with compositions Li x Ni 0.5 Co 0.2 Mn 0.3 O 2 (NCM523) and Li x Ni 0.8 Co 0.1 Mn 0.1 O 2 (NCM811) are studied by neutron powder diffraction (NPD). Detailed inspection of the NPD data reveals gradual formation of a secondary crystalline phase in all exposed samples, not previously reported for this system. This unknown phase forms faster in liquid- compared to vapor-exposed compounds. Structural modelling of the NPD data shows a stable level of Li/Ni anti-site defects and does not indicate any significant changes in lattice parameters or hydrogen-lithium (D + /Li + ) exchange in the structure. Consequently, the secondary phase formation must take place via transformation rather than modification of the parent material. X-ray photoelectron spectroscopy data indicate formation of LiHCO 3 /Li 2 CO 3 at the surface and a Li-deficient oxide in the sub-surface region of the pristine compounds, and the presence of adsorbed water and transition metal hydroxides at the exposed sample surfaces.
The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.
Introduction: Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers. Methods: Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking. Results: The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity. Conclusions: The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.
Hydrogen is a critically important molecule in the chemicals and energy industries, driving the need for efficient purification technologies such as membrane-based separations. However, polymer membranes often suffer from physical aging, leading to decreased gas permeability over time. This study leverages the unique aging behavior of contorted microporous polymers, synthesized via catalytic arene-norbornene annulation (CANAL) polymerization, to enhance molecular sieving for H 2 separations. While prior studies demonstrated a 1000% increase in H 2 /CH 4 selectivity with minimal permeability loss over 150 days, we extend this investigation to hyperaged (∼1 year) CANAL polymers and evaluate their performance under various conditions of industrial relevance. We report a remarkable 6100% increase in H 2 /CH 4 selectivity and a 2400% improvement in H 2 /N 2 selectivity, with only a 55% decline in H 2 permeability. These membranes exhibit excellent long-term stability in binary and ternary gas mixtures and at elevated temperatures. Additionally, extended aging enhances H 2 /CO 2 selectivity beyond the 2008 upper bound due to improved size-sieving. High-temperature permeation experiments and grand canonical Monte Carlo simulations reveal that H 2 permeability increases from 570 to 3500 barrer as the temperature rises from 35 °C to 190 °C, while H 2 /CO 2 selectivity declines from 14 to 3 due to the higher diffusion activation energy of CO 2 . Despite this tradeoff, hyperaged CANAL membranes maintain performance beyond the 200 °C upper bound, having selectivity–permeability performance comparable to many state-of-the-art membranes for H 2 /CO 2 separations. As a result, this study highlights the robustness and industrial viability of ultra-selective CANAL polymers for hydrogen purification, contributing to advancements in clean energy and sustainable separation technologies.