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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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The dWindDS model analyses the market diffusion of distributed wind generation for behind the meter applications. It is consumer decision based and uses a variety of data sets including a high resolution wind data set. It projects market development through 2050 based on input on specified by the user. This presentation covers some initial runs with draft base case assumptions.
We show that gravitational lensing can provide a direct method to probe the nature of dark energy at astrophysical scales. For lensing system as an isolated astrophysical object, we derive the dark energy contribution to gravitational potential as a repulsive power-law term, containing a generic equation of state parameter w . We find that it generates w -dependent and position-dependent modification to the conventional light orbital equation of w =−1. With post-Newtonian approximation, we compute its direct effect for an isolated lensing system at astrophysical scales and find that the dark energy force can deflect the path of incident light rays. We demonstrate that the dark-energy-induced deflection angle Δα{sub DE}∝ M {sup (1+1/3} {sup w} {sup )} (with 1+1/3 w > 0), which increases with the lensing mass M and consistently approaches zero in the limit M → 0. This effect is distinctive because dark energy tends to diffuse the rays and generates concave lensing effect . This is in contrast to the conventional convex lensing effect caused by both visible and dark matter. Measuring such concave lensing effect can directly probe the existence and nature of dark energy. We estimate this effect and show that the current gravitational lensing experiments are sensitive to the direct probe of dark energy at astrophysical scales. For the special case w =−1, our independent study favors the previous works that the cosmological constant can affect light bending, but our prediction qualitatively and quantitatively differ from the literature, including our consistent realization of Δα{sub DE} → 0 (under 0 M → ) at the leading order.
Spectroscopic performance of Schottky barrier alpha particle detectors fabricated on 50 μm thick n-type 4H-SiC epitaxial layers containing Z{sub 1/2}, EH{sub 5}, and Ci1 deep levels were investigated. The device performance was evaluated on the basis of junction current/capacitance characterization and alpha pulse-height spectroscopy. Capacitance mode deep level transient spectroscopy revealed the presence of the above-mentioned deep levels along with two shallow level defects related to titanium impurities (Ti(h) and Ti(c)) and an unidentified deep electron trap located at 2.4 eV below the conduction band minimum, which is being reported for the first time. The concentration of the lifetime killer Z{sub 1/2} defects was found to be 1.7 × 10{sup 13} cm{sup −3}. The charge transport and collection efficiency results obtained from the alpha particle pulse-height spectroscopy were interpreted using a drift-diffusion charge transport model. Based on these investigations, the physics behind the correlation of the detector properties viz., energy resolution and charge collection efficiency, the junction properties like uniformity in barrier-height, leakage current, and effective doping concentration, and the presence of defects has been discussed in details. The studies also revealed that the dominating contribution to the charge collection efficiency was due to the diffusion of charge carriers generated in the neutral region of the detector. The 10 mm{sup 2} large area detectors demonstrated an impressive energy resolution of 1.8% for 5486 keV alpha particles at an optimized operating reverse bias of 130 V.
A computational study was performed to investigate the influence of transient needle motion on gasoline direct injection (GDI) internal nozzle flow and near-field sprays. Simulations were conducted with a compressible Eulerian flow solver modeling liquid, vapor, and non-condensable gas phases with a diffuse interface. Variable rate generation and condensation of fuel vapor were captured using the homogeneous relaxation model (HRM). The non-flashing (spray G) and flashing (spray G2) conditions specified by the Engine Combustion Network were modeled using the nominal spray G nozzle geometry and transient needle lift and wobble were based upon ensemble averaged x-ray imaging preformed at Argonne National Lab. The minimum needle lift simulated was 5 μm and dynamic mesh motion was achieved with Laplacian smoothing. The results were qualitatively validated against experimental imaging and the experimental rate of injection profile was captured accurately using pressure boundary conditions and needle motion to actu- ate the injection. Needle wobble was found to have no measurable effect on the flow. Low needle lift is shown to result in vapor generation as fuel rushes past the needle. In conclusion, the internal injector flow is shown to contain many transient and interacting vortices which cause perturbations in the spray angle, fluctuations in the mass flux, and frequently result in string flash-boiling.
The concept of disposal of high-level nuclear waste in deep boreholes drilled into crystalline bedrock is gaining renewed interest and consideration as a viable mined repository alternative. A large amount of work on conceptual borehole design and preliminary performance assessment has been performed by researchers at MIT, Sandia National Laboratories, SKB (Sweden), and others. Much of this work relied on analytical derivations or, in a few cases, on weakly coupled models of heat, water, and radionuclide transport in the rock. Detailed numerical models are necessary to account for the large heterogeneity of properties (e.g., permeability and salinity vs. depth, diffusion coefficients, etc.) that would be observed at potential borehole disposal sites. A derivation of the FALCON code (Fracturing And Liquid CONvection) was used for the thermal-hydrologic modeling. This code solves the transport equations in porous media in a fully coupled way. The application leverages the flexibility and strengths of the MOOSE framework, developed by Idaho National Laboratory. The current version simulates heat, fluid, and chemical species transport in a fully coupled way allowing the rigorous evaluation of candidate repository site performance. This paper mostly focuses on the modeling of a deep borehole repository under realistic conditions, including modeling of a finite array of boreholes surrounded by undisturbed rock. The decay heat generated by the canisters diffuses into the host rock. Water heating can potentially lead to convection on the scale of thousands of years after the emplacement of the fuel. This convection is tightly coupled to the transport of the dissolved salt, which can suppress convection and reduce the release of the radioactive materials to the aquifer. The purpose of this work has been to evaluate the importance of the borehole array spacing and find the conditions under which convective transport can be ruled out as a radionuclide transport mechanism. Preliminary results show that modeling of the borehole array, including the surrounding rock, predicts convective flow in the system with physical velocities of the order of 10-5 km/yr over 105 years. This results in an escape length on the order of kilometers, which is comparable to the repository depth. However, a correct account of the salinity effects reduces convection velocity and escape length of the radionuclides from the repository.
We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.
Temperature-dependent dark current transport mechanism has been analyzed for GaAs-based blocked-impurity-band detectors. Device structure, processing steps and physical models are described in detail. In this work, the analysis of dark current transport mechanism has been greatly simplified by electron drift current related discussion based on the assumption that the diffusion current and the generation–recombination current as two dominating components of dark current in the absorbing layer can be equivalent to the electron drift current in the blocking layer. After confirming this assumption, our discussion focuses on why dark current is a monotonically increasing function of operation temperature for a fixed anode bias. Our results reveal that from dark current suppression point of view, low donor doping level and low operation temperature are preferred.
A local incident flux response expansion transport method is developed to generate transport solutions for coupling to diffusion theory codes regardless of their solution method (e.g., fine mesh, nodal, response based, finite element, etc.) for reactor core calculations in both two-dimensional (2-D) and three-dimensional (3-D) cylindrical geometries. In this approach, a Monte Carlo method is first used to precompute the local transport solution (i.e., response function library) for each unique transport coarse node, in which diffusion theory is not valid due to strong transport effects. The response function library is then used to iteratively determine the albedo coefficients on the diffusion-transport interfaces, which are then used as the coupling parameters within the diffusion code. This interface coupling technique allows a seamless integration of the transport and diffusion methods. The new method retains the detailed heterogeneity of the transport nodes and naturally constructs any local solution within them by a simple superposition of local responses to all incoming fluxes from the contiguous coarse nodes. A new technique is also developed for coupling to fine-mesh diffusion methods/codes. The local transport method/module is tested in 2-D and 3-D pebble-bed reactor benchmark problems consisting of an inner reflector, an annular fuel region, and a controlled outer reflector. It is found that the results predicted by the transport module agree very well with the reference fluxes calculated directly by MCNP in both benchmark problems.
Metal biogeochemistry in the sediment, water, and the sediment–water interface (SWI) was studied in a free water surface constructed wetland. Concentrations of labile copper (Cu), zinc (Zn), sulfate, chloride, and dissolved organic carbon (DOC) were measured with the diffusive gradients in thin film (DGT) and peeper. A good agreement between peeper- and DGT-measured metals was observed for Cu (regression r 2 = 0.3, 95% CI of the slopes > 0), but not for Zn (95% CI of the slopes overlapped with 0), which was attributed to the different complexed compounds between Cu and Zn in porewater. The depth profile of labile metals in sediment porewater varied with time and was consistent with the solid-phase metal deposition, showing higher concentrations in the surface layer (3 to –3 cm) than in the bottom layer (–4 to –13 cm). The depth-averaged labile Cu and Zn concentrations measured by DGT were 1.0 and 3.1 µg/L, and labile sulfate, chloride, and DOC concentrations measured by peeper were 1.8, 3.6, and 2.1 mg/L, respectively. A sharp decrease in sulfate occurred in September when sulfate concentrations became the lowest among sampling months. This was caused by the seasonal sulfur cycles in the wetland, where the dominant sulfur reaction is sulfate reduction in warm seasons and sulfide oxidation in cold seasons. Different metal-removal mechanisms were observed in the two wetland cells; sulfur dynamics controlled the removal processes in the cell without frequent disturbance but failed to influence metal removal in the cell with frequent disturbance due to the interruption of anoxic layers. The flux ratios that compare labile element concentrations between the water column and the SWI (R-Cu, R-Zn, R-DOC, R-sulfate, and R-chloride) were generated to determine metal diffusive fluxes at the interface. Labile Zn was mostly transported from the water to the SWI during all seasons (R-Zn < 1 for all months except January). Labile Cu moved from the SWI to the water during the warm months (R-Cu < 1), which was explained by the bioturbation-induced transport of organic matter based on the positive correlations between R-Cu and R-DOC. In general, sediment can serve either as a sink or a source depending on the environmental conditions, metal speciation, and presence of living organisms. Here, metal flux at the SWI is a key component in the biogeochemical cycling of a constructed wetland.
Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024
A novel synthesis route to the formation of vertically aligned single–crystalline oxide nanostructures is found by precisely controlling the nonequilibrium pulsed laser deposition process. Here, the columnar nanostructures with deep crevices offering a large surface area are generated owing to the diffusion limited geometric shadowing effect.
Abstract Diketopyrrolopyrrole (DPP)‐conjugated polymers are a versatile class of semiconductors for application in organic solar cells because of their tunable optoelectronic properties. A record power conversion efficiency (PCE) of 9.4% was recently achieved for DPP polymers, but further improvements are required to reach true efficiency limits. Using five DPP polymers with different chemical structures and molecular weights, the device performance of polymer:fullerene solar cells is systematically optimized by considering device polarity, morphology, and light absorption. The polymer solubility is found to have a significant effect on the optimal device polarity. Soluble polymers show a 10–25% increase in PCE in inverted device configurations, while the device performance is independent of device polarity for less soluble DPP derivatives. The difference seems related to the polymer to fullerene weight ratio at the ZnO interface in inverted devices, which is higher for more soluble DPP polymers. Optimization of the nature of the cosolvent to narrow the fibril width of polymers in the blends toward the exciton diffusion length enhances charge generation. Additionally, the use of a retroreflective foil increases absorption of light. Combined, the effects afford a PCE of 9.6%, among the highest for DPP‐based polymer solar cells.
Leveraging the interactions between ionomer and catalyst can increase the performance of proton exchange membrane fuel cells. The impacts of the equivalent weight (EW) of perfluorosulfonic acid–based ionomers on the platinum group metal-free electrode structure and fuel cell performance have not been fully explored. Four membrane electrode assemblies (MEAs) were prepared by using a commercial Fe–N–C catalyst, two perfluorosulfonic acid ionomers with different EWs, that is, Aquivion 720 (A720) and Nafion 1100 (N1100), and two ionomer-to-catalyst (I/C) ratios. The four MEAs were characterized to understand the impact of the ionomer EW and content on the capacitance, proton conductivity, and mass transport on the cathode. The mass transport resistance was measured for the first time using a new oxygen reduction reaction limiting current method enabling to couple the effects of oxygen diffusion with liquid water generation. Low EW ionomer combined with a moderate I/C results in improved performance due to its enhanced proton conductivity. However, when used at high I/C, it can cause severe water flooding at high current density due to the enhanced liquid water uptake, especially at high relative humidity, resulting in lower catalyst utilization and higher mass transport resistance.
As Electric Field Assisted Sintering (EFAS) gains more industrial acceptance and use, it becomes more important to develop more efficient means to implement this technology. To this aim, 3D printed continuous carbon fiber reinforced carbon (CCC) was manufactured and fabricated into tooling for EFAS systems as an alternative to traditional graphite tooling. The impact of fiber orientation on the thermal and electrical properties of the CCC was characterized. Sample material was sintered in Tokai G535 graphite tooling, under common processing conditions and compared with CCC tooling. There was nearly 50 % energy savings compared to graphite while maintaining equivalent sample density and microstructure plus keeping ram temperatures 39 % cooler. This is due to spatial control of generated heat and thermal diffusivity within the molds, by means of fiber orientation anisotropy. Finite element modeling of the tooling design supported the experimental results as well as displays the effect of optimization of this 3D printed CCC material.
LiNO 3 is a commonly used electrolyte additive in Li-S batteries, but the intricacies of its stabilizing effect on these batteries are not well-understood due to the complex chemical nature of Li-S electrolytes. This complexity often hampers clear characterization and interpretation of data. In this study, we explore the reactivity of LiNO 3 in Li-S batteries through in operando sulfur K-edge spectroscopy. To remove ambiguities, a sulfur-free electrolyte with LiNO 3 as the sole salt is used. This allows a precise elucidation of the reaction mechanism between LiNO 3 and polysulfides. Here, we observe that the reaction is chemical in nature and progresses spatially, influenced by the polysulfide concentration gradient across the electrolyte. Polysulfides are electrochemically generated in situ on the cathode side, leading to high local concentration. As a result, they cannot be fully oxidized by LiNO 3 and forms sulfites initially which are gradually further oxidized into sulfonates and sulfates. Conversely, polysulfides on the anode side have lower local concentration as they are not generated in situ but diffused from the cathode side. Their reaction with LiNO 3 leads to highly oxidized species like sulfonates and sulfates. These reaction products are not reduced during electrochemically cycling, suggesting their capabilities of passivating the electrodes and contributing to the cycle stabilities of Li-S batteries.
The effects of ionizing radiation on materials often reduce to "bad news". Radiation damage usually leads to detrimental effects such as embrittlement, accelerated creep, phase instability, and radiation-altered corrosion. Here we report that proton irradiation decelerates intergranular corrosion of Ni-Cr alloys in molten fluoride salt at 650 °C. We demonstrate this by showing that the depth of intergranular voids resulting from Cr leaching into the salt is reduced by proton irradiation alone. Interstitial defects generated from irradiation enhance diffusion, more rapidly replenishing corrosion-injected vacancies with alloy constituents, thus playing the crucial role in decelerating corrosion. Our results show that irradiation can have a positive impact on materials performance, challenging our view that radiation damage usually results in negative effects.
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Two rf schemes are being studied on the MST reversed field pinch for their potential in current profile control experiments. MHD modeling has shown that a substantial externally-driven off axis parallel current can improve stability of the dominant core tearing modes. A radially localized axisymmetric population of fast electrons has been observed by SXR emission during LH injection (100kW at 800MHz), and is consistent with CQL3D modeling which predicts a small driven current. Computational work suggests that doubling the input power will statistically improve the LH-induced SXR signal to background ratio, and that about 2MW of injected power (an order of magnitude increase) will drive enough current for stabilization of tearing modes. Additionally, a 1 MW 5.5 GHz electron Bernstein wave (EBW) experiment is under construction, which utilizes a very simple and compact antenna compatible with the demands of the RFP. EBW allows access to electron cyclotron heating and current drive in the overdense plasma. Coupling of the external electromagnetic wave to the EBW has been demonstrated, and initial tests at {approx}100kW power have produced a small, localized xray flux consistent with rf heating and high diffusivity of fast electrons. Computational work is currently underway to answer the very important questions of how much power is required, and what level of electron diffusivity is tolerable, to generate a consequential amount of EBW current.