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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Formation and fate of freshwater on an ice floe in the Central Arctic

The melt of snow and sea ice during the Arctic summer is a significant source of relatively fresh meltwater. The fate of this freshwater, whether in surface melt ponds or thin layers underneath the ice and in leads, impacts atmosphere–ice–ocean interactions and their subsequent coupled evolution. Here, we combine analyses of datasets from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (June–July 2020) for a process study on the formation and fate of sea ice freshwater on ice floes in the Central Arctic. Our freshwater budget analyses suggest that a relatively high fraction (58 %) is derived from surface melt. Additionally, the contribution from stored precipitation (snowmelt) outweighs by 5 times the input from in situ summer precipitation (rain). The magnitude and rate of local meltwater production are remarkably similar to those observed on the prior Surface Heat Budget of the Arctic Ocean (SHEBA) campaign, where the cumulative summer freshwater production totaled around 1 m during both. A relatively small fraction (10 %) of freshwater from melt remains in ponds, which is higher on more deformed second-year ice (SYI) compared to first-year ice (FYI) later in the summer. Most meltwater drains laterally and vertically, with vertical drainage enabling storage of freshwater internally in the ice by freshening brine channels. In the upper ocean, freshwater can accumulate in transient meltwater layers on the order of 0.1 to 1 m thick in leads and under the ice. The presence of such layers substantially impacts the coupled system by reducing bottom melt and allowing false bottom growth; reducing heat, nutrient, and gas exchange; and influencing ecosystem productivity. Regardless, the majority fraction of freshwater from melt is inferred to be ultimately incorporated into the upper ocean (75 %) or stored internally in the ice (14 %). Terms such as the annual sea ice freshwater production and meltwater storage in ponds could be used in future work as diagnostics for global climate and process models. For example, the range of values from the CESM2 climate model roughly encapsulate the observed total freshwater production, while storage in melt ponds is underestimated by about 50 %, suggesting pond drainage terms as a key process for investigation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A New Search for Low-mass Dark Matter and an Examination and Reduction of the Uncertainty due to the Photoelectric Absorption Cross Section using a Cryogenic Silicon Detector with Single-charge Sensitivity

Many astrophysical observations point to the abundant existence of dark matter (DM) in the universe composed of particles beyond the Standard Model. However despite numerous experiments using different techniques, DM particles have yet to be directly observed. The Super Cryogenic Dark Matter Search (SuperCDMS) SNOLAB experiment will employ silicon and germanium crystal detectors operated at temperatures as low as 15~mK to probe DM interactions using phonon and ionization signals. Recently, R\&D; facilities have developed gram-sized high-voltage eV-scale (HVeV) silicon detectors that achieve single-electron-hole-pair resolution. By probing effective DM-electron interactions, these devices can be used for searches of low-mass DM candidates.This dissertation presents a DM search experiment known as HVeV Run~2 that employs a second-generation HVeV detector operated in an above-ground laboratory at Northwestern University (IL, USA). Energy spectra are obtained from a blind analysis with 0.39 and 1.2\,g-days of exposure with the detector biased at 60 and 100\,V, respectively. The 0.93~gram detector achieves a 3\,eV phonon energy resolution, corresponding to a world-leading charge resolution of 3\,\% of a single electron-hole pair for a detector bias of 100\,V. With charge carrier trapping and impact ionization effects incorporated into the DM signal models, the resulting exclusion limits are reported for inelastic DM-electron scattering for DM masses from 0.5--$10^{4}$\,MeV$/c^{2}$; in the mass range from 1.2--50\,eV$/c^{2}$ the limits for dark photon and axion-like particle absorption are reported.Several DM search experiments, including HVeV Run~2, are sensitive to low-mass DM candidates that rely on the temperature-dependent photoelectric absorption cross section of silicon. However discrepancies in the underlying literature data result in dominating systematic uncertainties on the DM exclusion limits. In order to reduce these systematic uncertainties, this dissertation presents a novel method of making a direct, low-temperature measurement of the photoelectric absorption cross section of silicon at energies near the band gap (1.2--2.8\,eV).

Wilson, Matthew James↗
DOE OSTI2023

Role of Fe in long-range ordered Ni 2 Cr precipitates in Ni-Cr-Fe model alloys during isothermal aging

The precipitation of new phases during long-term service at elevated temperatures is a concern for the thermal stability of engineering alloys. In Ni-Cr-based alloys, e.g., Alloy 690, the formation of long-range ordered Ni 2 Cr causes embrittlement and may impact the lifetime of nuclear power plant components. In this work, we quantify the formation and evolution of Ni 2 Cr precipitation in eleven Ni-Cr-Fe model alloys with 0, 5, 7, and 10wt % Fe contents, and with Ni/Cr atomic ratios of 1.8, 2.0, 2.2, 2.4. These alloys were isothermally aged up to 10,000h at temperatures between 330 and 475°C. The alloys were characterized by synchrotron-based x-ray diffraction and Vickers hardness testing to quantify Ni 2 Cr precipitate size, and the impact of precipitate size on the mechanical properties as a function of Fe content. After 10,000 h of aging at 475°C and 418°C, the formation of Ni 2 Cr was observed in all alloys with 0 and 5wt % Fe. After 10,000h of aging at 418°C, Ni 2 Cr precipitates were also observed in the 7wt % Fe containing Ni/Cr=2.0 sample. Further, no clear evidence of Ni 2 Cr was observed in any of the 10wt % Fe samples at any time and temperature combination. We find that the face-centered cubic matrix lattice contraction and Vickers hardness are correlated with the Ni 2 Cr formation. The greatest change in hardness and lattice contraction occurs in stoichiometric alloys (Ni/Cr=2.0) with 0wt % Fe at 475°C. The rate of change in the material properties for the 5wt % Fe alloys is reduced, however the magnitude of changes is similar to 0wt % Fe alloys. A precipitation hardening model developed for Ni-Cr alloys based on critical resolved shear stress with weakly coupled dislocations shows a clear link between Ni 2 Cr precipitate size and hardness. This trend held across all alloys with Ni 2 Cr formation regardless of Fe concentration. This important structure-property relationship can potentially help define Ni-Cr-Fe-based component lifetimes directly through an understanding of how Ni 2 Cr formation impacts mechanical properties as a function of Fe content.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Implementation of Manifold-Based Combustion Models in a Highly Scalable Low Mach Number Reacting Flow Solver: Preprint

Manifold-based representations of the thermochemistry are often employed in conjunction with large eddy eimulation (LES) to lower the cost of combustion simulations. This work describes steps taken to implement this modeling approach in PeleLM, a scalable and performance-portable low Mach number flow solver. Most significantly, this includes adapting the projection method used by PeleLM to satisfy the mass conservation constraint for use with manifold-based models. The implementation is designed to be general across manifold-based models, including both those that employ traditional tabulation and those that employ neural networks. An initial demonstration for simple test cases is presented and will be used for performance assessment.

high-performance computing↗
DOE OSTI2019

AOI2 - Initial Engineering, Testing, and Design of a Commercial-Scale Postcombustion CO 2 Capture System on an Existing Coal-Fired Generating Unit (Project Carbon)

The continued long-term use of North Dakota lignite is likely dependent on creating a business case for carbon capture, utilization, and storage (CCUS) that also addresses society’s desire to reduce carbon emissions. CCUS coupled with enhanced oil recovery appears to be the most feasible option for utilities to sustain and grow the lignite industry. Establishing a market where lignite-powered utilities provide carbon dioxide (CO 2 ) to oil producers depends upon having a cost-effective method to capture CO 2 . The application of CO 2 capture technology to lignite-fired flue gas has challenges in addition to the cost of capture, including the effects of the buildup of heat-stable salts, aerosol formation, degradation of solvents or solid sorbents, or subsequent solvent loss. Lignite, especially North Dakota lignite, presents its own set of challenges that include the combination of high sodium, sulfur, and ash contents; NO x reduction; and footprint limitations. Project Carbon was designed to address these technical barriers and provide pre-FEED (preliminary front-end engineering and design) information necessary for additional commercial demonstrations to move forward. A long-term pilot-scale evaluation of Mitsubishi Heavy Industries’ (MHI’s) KS-1™ solvent at Minnkota Power Cooperative’s lignite-fired Milton R. Young Station Unit 2 (MRY2) was a major objective of the project. The purpose of the test was to demonstrate the technology’s compatibility with MRY2’s flue gas over an extended period of time. During the long-term test, aerosol sampling and measurement were performed, and a pilot-scale baghouse was tested to determine the effect of bag material and operation on aerosol concentrations in the flue gas. Project engineering and design work were performed to support the other major project objective: a pre-FEED study of CO 2 capture at MRY2. Finally, a techno-economic assessment (TEA) of the application of CO 2 capture to MRY2 was performed. This project was performed over a period of 28 months, with a total budget of $12,700,000. The U.S. Department of Energy’s (DOE’s) National Energy Technology Laboratory, through the Energy & Environmental Research Center’s (EERC’s) Cooperative Agreement, the North Dakota Industrial Commission, and industry partners ALLETE and Minnkota Power Cooperative, funded the project. The pilot-scale test system at MRY2 performed very well for the continuous test period of 83 days. The CO 2 capture plant achieved stable operation while utilizing MHI’s KS-1™ solvent and the MHI amine emission reduction unit. No major concerns were encountered when capturing CO 2 from lignite-fired flue gas. Challenges were encountered because the cooling water temperature was warmer than needed for the capture system to operate efficiently. This affected the ability of the solvent to capture CO 2 and promoted solvent loss through solvent entrainment caused by high CO 2 gas temperature within a cyclone separator. The KS-1™ solvent and the lignite-derived flue gas were analyzed during the long-term test. Analysis of the solvent indicates that, although some metals and heat-stable salts increased over the course of the long-term test, they were at levels that can be treated with filtration and reclaiming and do not pose a concern for effective solvent operation. Fourier transform infrared (FTIR) spectrometry was successfully used to determine the composition of the lignite-derived flue gas as well as concentrations of select components of the flue gas at various locations within the EERC’s CO 2 capture system. The formation of aerosols during the capture process is of interest, as are approaches for their reduction, especially in a baghouse. Testing of aerosol removal was performed using the EERC’s portable baghouse. It was found that both Ryton and polytetrafluoroethylene (PTFE) membrane bags were effective at reducing the level of aerosols during steady-state operation. During backpulse events, however, the amount of aerosols passing through the bags was an order of magnitude lower for the Ryton bags than the PTFE membrane bags. The testing showed that proper design of the baghouse air-to-cloth ratio as well as minimization of backpulsing can dramatically reduce the amount of aerosols that pass through the bags and remain in the flue gas stream exiting a baghouse system. A technology maturation plan (TMP) was performed to enable successful development of the Kansai Mitsubishi Carbon Dioxide Recovery® (KM CDR®) Process for application to a combustion flue gas for which the process has not yet been demonstrated. In the TMP, new challenges to the MHI CO 2 capture technology during its application to MRY2 were identified. These challenges included capture from lignite-fired (as opposed to subbituminous coal-fired) flue gas, a larger scope than previous applications of the technology, and implementation in a colder climate that could affect process and equipment operability. Because MRY2 is located where stack icing in the winter could be problematic, a rime icing study was performed. Rime icing modeling indicated that, under both 100% and 50% operating loads, there is a potential for many icing events during any given part of the year in which below-freezing weather conditions exist. The models showed that a critical amount of rime ice could be formed at the stack top during two or more events per year. CO 2 capture plant stack height and location will need to be determined in order to minimize the formation of rime ice. It was found that the integration of the MHI CO 2 capture process into the normal operation of the MRY2 steam turbine is a viable alternative to a cogeneration facility to provide steam and power for the carbon capture facility. The research performed under this project showed that CO 2 capture at a lignite-fired power plant can successfully be performed. No known fatal flaws that would prevent the continued development of the MRY2 CO 2 capture facility were identified during the pre-FEED study. A TEA performed following DOE methodology shows that applying capture technology to MRY2 may not only meet but exceed the DOE-recognized state-of-the-art baseline.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

An accelerated approach to inline non-LTE modeling

A goal across many physics and engineering disciplines is to use radiation-hydrodynamics codes to accurately simulate systems that are expected to deviate from local thermodynamic equilibrium (LTE), such as laser-driven hohlraums, stellar coronae, and supernova ejecta. The computational expense of traditional non-LTE (NLTE) opacity calculations can dominate the cost of simulating these systems and render NLTE physics untenable in most cases. In this paper, we present a new parallel implementation for NLTE opacity calculations that employs advanced load balancing, vectorization, and memory management to significantly reduce the computational costs of these calculations. Using the radiation-hydrodynamics code xRAGE , we demonstrate this new implementation on three test problems and present order-of-magnitude speed improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Neural-network learning of SPOD latent dynamics

Here, we aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary systems using model order reduction via the spectral proper orthogonal decomposition (SPOD). The proposed method is based on three fundamental steps: in the first, once that the mean flow field has been subtracted from the realizations (also referred to as snapshots), we compress the data from a high-dimensional representation to a lower dimensional one by constructing the SPOD latent space; in the second, we build the time-dependent coefficients by projecting the snapshots containing the fluctuations onto the SPOD basis and we learn their evolution in time with the aid of recurrent neural networks; in the third, we reconstruct the high-dimensional data from the learnt lower -dimensional representation. The proposed method is demonstrated on two different test cases, namely, a compressible jet flow, and a geophysical problem known as the Madden-Julian Oscillation. An extensive comparison between SPOD and the equivalent POD-based counterpart is provided and differences between the two approaches are highlighted. The numerical results suggest that the proposed model is able to provide low rank predictions of complex statistically stationary data and to provide insights into the evolution of phenomena characterized by specific range of frequencies. The comparison between POD and SPOD surrogate strategies highlights the need for further work on the characterization of the interplay of error between data reduction techniques and neural network forecasts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2022

Magnon heat transport in a two-dimensional Mott insulator

Whether or not anomalies in the thermal conductivity in insulating cuprates can be attributed to antiferromagnetic order and magnons in a 2D Mott insulator remains an intriguing open question. To shed light on this issue, we investigate the thermal conductivity $\kappa$ and its relationship with the specific heat $c_v$ in the half-filled 2D single-band Hubbard model, using the numerically exact determinant quantum Monte Carlo algorithm and maximum entropy analytic continuation. At low temperatures where the charge degrees of freedom are gapped-out and $c_v$ exhibits a clear magnon peak, we observe that thermal conductivity $\kappa$ also tends to form a peak at similar temperatures. Reducing temperature further produces a sharp upturn in $\kappa$, associated with an increasing mean-free path. Here we identify this as the high-temperature side of the anomalous peak in insulating cuprates, where the mean-free path eventually is cut-off by other scattering effects, including phonons, disorder, and physical size. Different scattering effects in our model are identified and analyzed in the thermal diffusivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Assessing the Feasibility of Bordered Block Diagonal Reordering in Power System Matrices using Fully Convolutional Network

In electromagnetic transient (EMT) simulations for power systems and inverter-based resources (IBRs), the arrangement of states within the system's linear equations, represented by matrix A in Ax=b, is critical. The state ordering in matrix A can highlight distinct characteristics of the system's graph, and identifying an optimal state ordering is crucial for efficient computation. The choice of state ordering, however, is dependent on the solver used, as each solver may perform optimally with different matrix patterns. With a wide array of matrix reordering algorithms available, selecting the most suitable one becomes challenging without insights into the matrix's ideal configuration. To address this, the paper proposes a fully convolutional network (FCN) to evaluate the reordering potential of the A matrix into a bordered block diagonal (BBD) pattern, which is commonly observed in power system and IBR modeling. The FCN's assessment aims to streamline the solver's operation, which in turn could substantially reduce the computational time required to find a solution.

Xia, Qianxue↗
DOE OSTI2020

Probing the Effects of NO x and SO x Impurities on Oxy-Fuel Combustion in Supercritical CO 2 : Shock Tube Experiments and Chemical Kinetic Modeling

The direct-fired supercritical carbon dioxide cycles are one of the most promising power generation methods in terms of their efficiency and environmental friendliness. Two important challenges in implementing these cycles are the high pressure (300 bar) and high CO 2 dilution (>80%) in the combustor. The design and development of supercritical oxy-combustors for natural gas require accurate reaction kinetic models to predict the combustion outcomes. The presence of a small amount of impurities in natural gas and other feed streams to oxy-combustors makes these predictions even more complex. During oxy-combustion, trace amounts of nitrogen present in the oxidizer is converted to NO x and gets into the combustion chamber along with the recirculated CO 2 . Similarly, natural gas can contain a trace amount of ammonia and sulfurous impurities that get converted to NO x and SO x and get back into the combustion chamber with recirculated CO 2 . In this work, a reaction model is developed for predicting the effect of impurities such as NO x and SO x on supercritical methane combustion. The base mechanism used in this work is GRI Mech 3.0. H 2 S combustion chemistry is obtained from Bongartz et al. while NOx chemistry is from Konnov. The reaction model is then optimized for a pressure range of 30–300 bar using high-pressure shock tube data from the literature. It is then validated with data obtained from the literature for methane combustion, H 2 S oxidation, and NO x effects on ignition delay. The effect of impurities on CH 4 combustion up to 16 atm is validated using NO x -doped methane studies obtained from the literature. In order to validate the model for high-pressure conditions, experiments are conducted at the UCF shock tube facility using natural gas identical mixtures with N 2 O as an impurity at ~100 bar. Current results show that there is a significant change in ignition delay with the presence of impurities. A comparison is made with experimental data using the developed model and predictions are found to be in good agreement. The model developed was used to study the effect of impurities on CO formation from sCO 2 combustors. It was found that NO x helps in reducing CO formation while the presence of H 2 S results in the formation of more CO. The reaction mechanism developed herein can also be used as a base mechanism to develop reduced mechanisms for use in CFD simulations.

Energy & Fuels↗
DOE OSTI2022

Self‐Assembled LuFeO 3 /LuFe 2 O 4 Heterostructure with Emergent Ferroic Orderings

Abstract Achieving self‐assembling structures that exhibit emergent properties and functionalities is one of the holy grails in nanotechnology and holds great promise for a wide range of potential applications. Herein, a self‐assembled LuFeO 3 –(1/3)LuFe 2 O 4 –LuFeO 3 heterostructure that consists of two model multiferroics, i.e., hexagonal LuFeO 3 and rhombohedral LuFe 2 O 4 , is reported in single‐layer epitaxial LuFeO 3 thin films. Atomic‐scale electron microscopy investigations reveal the spontaneous organization of ferroelectric polarization in each building block, which forms a series of polar configurations. Specifically, the charge and lattice of neighboring LuFeO 3 and LuFe 2 O 4 blocks are closely coupled with competing degrees of freedom. A combination of the quantitative analysis of atomically resolved images and spectroscopy with theoretical calculations determines how charge‐ordering‐induced polarization accommodates charge naturality across the interfaces to reduce the system energy and unravels the formation mechanism of this heterostructure. The results point to the possibilities of generation and control of multiferroic self‐assembled systems, which may provide a basis for engineering novel materials with emergent properties.

Deng, Shiqing↗
DOE OSTI2021

Computational Evaluation of Thermal Response of Open-Cell Foam With Circular Pore (Computional Evaluation of Thermal Response of Open-Cell Foam with Circular Pore)

The evaluation of effective material properties in heterogeneous materials (e.g., composites or multicomponent structures) critically relevant to a wide spectrum of applications, including nuclear power, electronic packaging, flame retardants, hypersonics, and gas turbine power. The work described in this paper is centered around the numerical assessment of the thermal behavior of porous materials obtained from finite element thermal modeling and simulation. Here, two-dimensional, steady state analyses were performed on unit cells with centered, circular pores using a second order accurate Galerkin finite element method (FEM). The effective thermal conductivities of the porous systems were examined, encompassing a range of porosities from 4.9% to 60.1%. The geometries of the models were generated based on ordered circular pores for each modeled porosity level. The system response quantity (SRQ) under investigation was the dimensionless effective thermal conductivity across the unit cell. The dimensionless effective thermal conductivity was compared across all simulated cases, producing a trend between porosity and effective thermal conductivity. In the presented investigation, the method of manufactured solutions (MMS) was used to perform code verification, and the grid convergence index (GCI) was employed to estimate discretization uncertainty as solution verification. Code verification concluded an approximately second order accurate Galerkin FEM solver. It was found that the introduction of porosity to the unit cell material structure reduces effective thermal conductivity, as anticipated. Numerical results obtained in this study are compared to an analytical solution and to a sample of empirical data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Three-dimensional core-collapse supernova models with phenomenological treatment of neutrino flavor conversions

Abstract We perform three-dimensional supernova simulations with a phenomenological treatment of neutrino flavor conversions. We show that the explosion energy can increase to as high as $\sim 10^{51}$ erg depending on the critical density for the onset of flavor conversions, due to a significant enhancement of the mean energy of electron antineutrinos. Our results confirm previous studies showing such energetic explosions, but for the first time in three-dimensional configurations. In addition, we predict neutrino and gravitational wave (GW) signals from a nearby supernova explosion aided by flavor conversions. We find that the neutrino event number decreases because of the reduced flux of heavy-lepton neutrinos. In order to detect GWs, next-generation GW telescopes such as Cosmic Explorer and the Einstein Telescope are needed even if the supernova event is located at the Galactic Center. These findings show that the neutrino flavor conversions can significantly change supernova dynamics and highlight the importance of further studies on the quantum kinetic equations to determine the conditions of the conversions and their asymptotic states.

Mori, Kanji↗
DOE OSTI2019

Highly Efficient Separation/Recycling Palladium(II) Ions from Aqueous Solutions by Silica Gel-Coated Graphene Oxide Modified with Mercapto Groups

The separation and recovery of precious metals from secondary resources is an extremely important and challenging task. Herein, a novel sorbent is successfully prepared by chemical grafting and utilized for the extraction and separation of Pd(II) ions from aqueous solutions. Various characterization techniques, including Fourier transform infrared spectroscopy, scanning electron microscopy, Brunauer–Emmett–Teller, X-ray photoelectron spectroscopy, and element analysis, are employed to study the structure, morphology, porous nature, and chemical composition of the as-prepared SiO{sub 2}@GO-SH nanocomposites. The adsorption of Pd(II) ions on the SiO{sub 2}@GO-SH can be described by the pseudo-second-order model and Langmuir isotherm model. The adsorption equilibrium has been attained within 90 min and the maximum adsorption capacity of 423.2 mg g{sup −1} was attained at pH 3.5 and T = 308 K. The results reveal that SiO{sub 2}@GO-SH exhibits an excellent adsorption performance towards Pd(II) ions. In addition, we have proposed the adsorption mechanism for Pd(II) ions on the SiO{sub 2}@GO-SH surface. The adsorption of Pd(II) in SiO{sub 2}@GO-SH is a chemisorption process, where partial adsorbed Pd(II) ions are reduced to Pd(0) by functional groups (-SH) in the SiO{sub 2}@GO-SH. The results indicate that SiO{sub 2}@GO-SH can serve as a promising sorbent for the efficient separation and recovery of palladium from the palladium-containing secondary resources.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ab initio study of short-range ordering in vanadium-based disordered rocksalt structures

Disordered rocksalt Li-excess (DRX) compounds are attractive new cathode materials for Li-ion batteries as they contain resource-abundant metals and do not require the use of cobalt or nickel. Understanding the delithiation process and cation short-range ordering (SRO) in DRX compounds is essential to improve these promising cathode materials. Herein, we use first-principles calculations along with the cluster-expansion approach to model the disorder in DRX Li 2-x VO 3 , 0 < x < 1. We discuss the SRO of Li in tetrahedral and octahedral sites, and the order in which Li delithiates and V oxidizes with respect to local environments. We reveal that the number of nearest-neighbor V dictates the order of delithiation from octahedral sites and that V is oxidized in a manner that minimizes the electrostatic interactions among V. In conclusion, our results provide valuable insight for tailoring the performance of V-based DRX cathode materials in general by controlling the SRO features that reduce energy density.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Role of microtearing mode in DIII-D and future high- β p core plasmas

This paper provides modeling evidence for a new variant of microturbulence instability that regulates the electron thermal fluxes in the transport barrier region of DIII-D "high β p " discharges. This scenario is being investigated as a possible scenario for future steady-state reactors, and thus it is essential to understand the physics processes which govern its performance in order to confidently extrapolate to future devices. Additional analysis examining the extrapolation of this scenario to ITER shows that while the new microtearing mode identified here is unlikely to be unstable in that regime (due to increased temperatures and reduced collisionality), the large shift in magnetic axis (Shafranov shift) that arises in these plasmas may still favor stabilization of common ballooning instabilities which peak on the low field side, but destabilize electrostatic slab modes on the high field side.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

MACHINE LEARNING-ENABLED PREDICTION OF TRANSIENT INJECTION MAP IN AUTOMOTIVE INJECTORS WITH UNCERTAINTY QUANTIFICATION

Accurate prediction of injection profiles is a critical aspect of linking injector operation with engine performance and emissions. However, highly resolved injector simulations can take one to two weeks of wall-clock time, which is incompatible with engine design cycles with desired turnaround times of less than a day. Hence, it is important to reduce the time-to-solution of the internal flow simulations by several orders of magnitude to make it compatible with engine simulations. This work demonstrates a data-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed to understand the effect of design parameters on the total void fraction field. Then, autoencoders are utilized for efficient dimensionality reduction of the flowfields. Gaussian process models are finally used to predict the spatiotemporal void fraction field at the injector exit for unknown operating conditions. The Gaussian process models produce principled uncertainty estimates associated with the emulated flowfields, which provide the engine designer with valuable information of where the data-driven predictions can be trusted in the design space. The Bayesian flowfield predictions are compared with the corresponding predictions from a deep neural network, which has been transfer-learned from static needle simulations from a previous work by the authors. The emulation framework can predict the void fraction field at the exit of the orifice within a few seconds, thus achieving a speed-up factor of up to 38 x 10(6) over the traditional simulation-based approach of generating transient injection maps.

machine learning↗