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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 1,261 records · Page 70

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DOE OSTI2013

Hydration of Portland cement with additions of calcium sulfoaluminates

The effect of mineral additions based on calcium aluminates on the hydration mechanism of ordinary Portland cement (OPC) was investigated using isothermal calorimetry, thermal analysis, X-ray diffraction, scanning electron microscopy, solid state nuclear magnetic resonance and pore solution analysis. Results show that the addition of a calcium sulfoaluminate cement (CSA) to the OPC does not affect the hydration mechanism of alite but controls the aluminate dissolution. In the second blend investigated, a rapid setting cement, the amorphous calcium aluminate reacts very fast to ettringite. The release of aluminum ions strongly retards the hydration of alite but the C-S-H has a similar composition as in OPC with no additional Al to Si substitution. As in CSA-OPC, the aluminate hydration is controlled by the availability of sulfates. The coupling of thermodynamic modeling with the kinetic equations predicts the amount of hydrates and pore solution compositions as a function of time and validates the model in these systems.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Thermal design analysis for SuperTruck II lithium-titanate battery pack

Here, this paper presents a systematic thermal management analysis for a new lithium-titanate-oxide battery pack to be installed in a SuperTruck II, Class 8 hybrid truck. The authors investigate the feasibility of mounting the battery pack inside the vehicle and air-cooling it with fans supplying conditioned air from the cabin. Moreover, the cells within each module are to be immersed in a heat-transfer fluid to improve temperature homogeneity. A multi-stage thermal analysis is performed to ensure adequate thermal regulation of the battery under the proposed design, considering installation and operational constraints. The authors perform thermal and electrical characterization tests on a single cell. Results inform the development of computational fluid dynamics models of the cells, modules, and pack. Module-level analysis shows that the cell temperatures can be maintained below their upper operational limit of 55 °C with module wall-to-air heat transfer coefficients between 10 and 20 Wm -2 K -1 . Pack-level analysis of distinct configurations determines effective airflow paths for adequate heat transfer and delivers a final battery pack design that achieves sufficient cooling and temperature uniformity. A reduced-order electrothermal model is developed to rapidly predict the transient battery performance and to develop a temperature control strategy.

25 ENERGY STORAGE↗

DOE OSTI2024

The Interplay of Solvation and Polarization Effects on Ion Pairing in Nanoconfined Electrolytes

The nature of ion–ion interactions in electrolytes confined to nanoscale pores has important implications for energy storage and separation technologies. However, the physical effects dictating the structure of nanoconfined electrolytes remain debated. Here we employ machine-learning-based molecular dynamics simulations to investigate ion–ion interactions with density functional theory level accuracy in a prototypical confined electrolyte, aqueous NaCl within graphene slit pores. We find that the free energy of ion pairing in highly confined electrolytes deviates substantially from that in bulk solutions, observing a decrease in contact ion pairing but an increase in solvent-separated ion pairing. These changes arise from an interplay of ion solvation effects and graphene’s electronic structure. Notably, the behavior observed from our first-principles-level simulations is not reproduced even qualitatively with the classical force fields conventionally used to model these systems. The insight provided in this work opens new avenues for predicting and controlling the structure of nanoconfined electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Near-surface modification of defective KTaO 3 by ionizing ion irradiation

The synergistic effect of nuclear (S n ) and electronic (S e ) energy loss observed in some ABO 3 perovskites has attracted considerable attention due to the real possibility to modify various near-surface properties, such as the electronic and optical properties, by patterning ion tracks in the defective near-surface regions. In this study, we show that low-energy ion-induced disordering in conjunction with ionizing ion irradiation (18 MeV Si, 21 MeV Ni and 91.6 MeV Xe) is a promising approach for tailoring ion tracks in the near-surface of defective KTaO 3 . Experimental characterization and computer simulations reveal that the size of these latent ion tracks increases with S e and level of pre-existing damage. These results further reveal that the threshold S e value (S e th ) for track creation increases with decreasing pre-damage level. The values of S e th increase from 5.02 keV nm -1 , for a pre-existing fractional disorder of 0.53 in KTaO 3 , to 10.81 keV nm -1 for pristine KTaO 3 . Above these thresholds, amorphous latent tracks are produced due local melting and rapid quenching. Below a disorder fraction of 0.08 and S e ≤ 6.68 keV nm -1 , the synergistic effect is not active, and damage accumulation is suppressed due to a competing ionization-induced damage annealing process. These results indicate that, depending on S e and the amount of pre-existing damage, highly ionizing ions can either enhance or suppress damage accumulation in KTaO 3 , thus providing a pathway to tailoring defects states. Comprehending the conflicting roles of highly ionizing ions in defective ABO 3 oxides is vital for understanding and predictive modeling of ion-solid interactions in complex oxides, as well as for achieving control over ion track size in the near-surface of defective KTaO 3 .

36 MATERIALS SCIENCE↗

DOE OSTI2019

Preface for focus issue on uncertainty quantification in materials modeling

This focus issue is motivated by the growing demand for rigorous uncertainty quantification (UQ) in materials modeling, which is driven by the need to use these tools, in conjunction with experiments, to support decision making in materials design, development, and deployment. Traditionally, predictive materials modeling has focused on gaining qualitative insight into the range of mechanisms that control material behavior and how those mechanisms interact to govern material properties and processes. In that context, quantitative evaluation of modeling uncertainty was not a priority. As materials modeling advances, there is increased impetus to employ it in the context of materials design and qualification. This trend is manifested in the establishment of integrated computational materials engineering (ICME) as a growing sub-discipline, as well as by initiatives such as the materials genome in the USA and similar efforts around the globe. Current practice and future needs are described in several recent reports including NASA's Vision 2040: A Roadmap for Integrated, Multiscale Modeling and Simulation of Materials and Systems. Invariably, these studies point out the need for the field to embrace the challenge of UQ.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance

Abstract Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Pioneering Heat Pump Project

This project was initiated at a time when ground coupled heat pump systems in this region were limited in size and quantity. There were economic pressures with costs for natural gas and electric utilities that had many organizations considering ground coupled heat pumps; The research has added to the understanding of how ground temperatures fluctuate seasonally and how this affects the performance and operation of the heat pumps. This was done by using a series of temperature sensors buried within the middle of one of the vertical bore fields with sensors located at various depths below grade. Trending of the data showed that there is a lag in ground temperature with respect to air temperatures in the shoulder months, however as full cooling and heating season arrives, the heat rejection and heat extraction from the ground has a significant effect on the ground temps; Additionally it is better understood that while a large community geothermal bore field serving multiple buildings does provide a convenient central plant to use, it introduces complexity of not being able to easily model and predict how each building will contribute to the loads in real time. Additional controllers and programming were added to provide more insight into this real time load profile and allow for intelligent shedding of load via a dry cooler during cool nights in lieu of rejecting to the ground loop. This serves as a means to ‘condition’ the ground loop and mitigate thermal creep of the field, as is typically observed; and It has been observed when compared to traditional heating and cooling equipment, there is still a cost premium to use ground source heat pumps that is driven mostly by the cost for vertical bore holes. Horizontal loop systems are less costly to install, but do not perform as well in this climate zone for heating mode

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Using Magnetic Proximity Effects to Build Designer Quantum Materials [Slides]

The properties of naturally occurring materials are not necessarily commensurate with those needed for next-generation devices. Counter to conventional thought, we find magnetic proximity effects can be asymmetric. In fact, our experimental data and DFT calculations indicate this effect is universal, with material and heterostructure specificity being vital for predictive modeling. This work suggests routes towards rational design of device component materials via selective control over spin valley states.

36 MATERIALS SCIENCE↗

DOE OSTI2011

A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series

Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Integration of Nuclear Material Accounting Data and Process Monitoring Data for Improvement on Detection Probability in Safeguarding Electrochemical Processing Facilities (Final Technical Report)

The KAERI advanced spent fuel conditioning process (ACP) process is a critical component of the US- South Korean nuclear cooperation and the following “123 Agreement.” Its development has received considerable attention in both countries. The ACP is an electrochemical processing (pyroprocessing) that recycles over 96% of the used nuclear fuel (UNF). It is also intrinsically proliferation-resistant in theory. In normal operation, the U/TRU product is very hot radiologically. In addition, the Cm provides a high level of spontaneous neutrons, making the product unsuitable for weapon use. However, as pointed in some study, “the need for safeguards to protect against the diversion and misuse of separated plutonium applies essentially equally to all grades of plutonium.” As pointed by many studies, the well-established traditional Nuclear Material Accounting (NMA) approach cannot be directly applied to electrochemical processing because of the lack of an input accountability tank, the non-continuous material flow, and the unsatisfactory level of confidence in sampling methods. Therefore, nuclear safeguards remain a grand challenge in the developing of commercial electrochemical separations facilities, especially around the heart of such facilities, the electrorefiner (ER) systems. In contrast to NMA data, process monitoring (PM) data is normally an indirect measurement of the SNM and is acquired much more frequently. In a broad sense, PM includes monitoring by various types of equipment, e.g. radiation detectors, cameras, voltage, current sensors. Because it is already being collected by the operator, the additional cost to safeguards is low. It has long been believed that PM data can supplement NMA data and help improve safeguards, although the benefits are hard to quantify. The U.S. DOE’s Material Protection, Accounting, and Control Technology (MPACT) campaign has made substantial investments into innovative PM sensor technology and predictive model development for real- or near real-time measurement and prediction of molten salt density and level, salt composition and actinide concentration especially Pu, the cell voltage, and the cell current to supplement traditional NMA. For aqueous-based reprocessing facilities, it is reported that PM, integrated with traditional NMA, have a high detection probability for specific diversions. For electrochemical reprocessing, preliminary studies have shown that PM data can support traditional NMA in various ways by providing a basis to estimate some of the in-processing nuclear material inventories. Despite early success, further studies on fusion of PM data and NMA data are still needed, which is the goal of this proposed work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Effect of net direct current on the properties of radio frequency sheaths: simulation and cross-code comparison

In order to understand, predict and control ion cyclotron range of frequency (ICRF) interactions with tokamak scrape-off layer plasmas, computational tools which can model radio frequency (RF) sheaths are needed. In particular, models for the effective surface impedance and DC rectified sheath potentials may be coupled with full wave RF simulation codes to predict self-consistent wave fields near surfaces and the resulting power dissipation and plasma-material interactions from ion sputtering. In this study, previous work assuming zero net DC current flow through the sheath is generalized to allow the surface to collect net positive or negative current, as is often observed in experiments. The waveforms, DC potential and RF admittance are investigated by means of analytical theory, nonlinear fluid and particle-in-cell (PIC) codes. Cross-code comparisons provide detailed model verification and elucidate the roles of ion and electron kinetics. When the sheath draws negative (positive) DC current, the voltage rectification is reduced (increased) compared with the zero-current case, and the magnitude of both the real and imaginary parts of the admittance are increased (reduced). In conclusion, a previous four-input parametrization of the sheath rectification and admittance properties is generalized to include a fifth parameter describing the DC sheath current.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2018

Data-Driven Wind Farm Optimization Incorporating Effects of Turbulence Intensity

We demonstrate assimilation of field data from a nacelle-mounted lidar and meteorological tower into a medium-fidelity Reynolds-Averaged Navier Stokes wind farm flow model to better predict the effects of atmospheric stability. Increased predictability under a variety of atmospheric conditions can lead to more effective control design and optimization of a wind farm. In particular, atmospheric stability affects wind turbine wake propagation and, therefore, aspects of wind farm control and performance, such as active wind farm control, layout optimization, and power output. Accurately modeling wakes in different stability conditions remains a persistent challenge. This paper presents an optimization framework that leverages high-fidelity field or simulation data to correct a lower-fidelity flow model. Optimal model corrections are found by solving a regularized, high-dimensional, gradient-based optimization problem using adjoint flowfield information. We validate the trained model against large eddy simulation results, and perform separate gradient-based layout optimizations of a simulated utility-scale wind farm to maximize power. Using the data-driven model corrections, we find that atmospheric stability significantly impacts layout optimization and power production: the optimal layout for stable conditions produced 9.1% more power than the optimal layout for unstable conditions, and the optimal layout for neutral conditions underperformed by 8.5% in unstable conditions.

17 WIND ENERGY↗

DOE OSTI2010

Geochemical modeling of leaching of Ca, Mg, Al, and Pb from cementitious waste forms

Results from extraction tests on cement-waste samples were simulated with a thermodynamic equilibrium model using a consistent database, to which lead data were added. Subsequent diffusion tests were modeled by means of a 3D diffusive transport model combined with the geochemical model derived from the extraction tests. Modeling results of the leached major element concentrations for both uncarbonated and (partially) carbonated samples agreed well with the extraction test using the set of pure minerals and solid solutions present in the database. The observed decrease in Ca leaching with increasing carbonation level was qualitatively predicted. Simulations also revealed that Pb leaching is not controlled by dissolution/precipitation only. The addition of the calcite-cerrusite solid solution and adsorption reactions on amorphous Fe- and Al-oxides improved the predictions and are considered to control the Pb leaching during the extractions tests. The dynamic diffusive leaching tests were appropriately modeled for Na, K, Ca and Pb.

36 MATERIALS SCIENCE↗

DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗

DOE OSTI2020

Controlling Hydrocarbon (De)Hydrogenation Pathways with Bifunctional PtCu Single-Atom Alloys

The conversions of surface-bound alkyl groups to alkanes and alkenes are important steps in many heterogeneously catalyzed reactions. On the one hand, while Pt is ubiquitous in industry because of its high activity toward C–H activation, many Pt-based catalysts tend to overbind reactive intermediates, which leads to deactivation by carbon deposition and coke formation. On the other hand, Cu binds intermediates more weakly than Pt, but activation barriers tend to be higher on Cu. We examine the reactivity of ethyl, the simplest alkyl group that can undergo hydrogenation and dehydrogenation via β-elimination, and show that isolated Pt atoms in Cu enable low-temperature hydrogenation of ethyl, unseen on Cu, while avoiding the decomposition pathways on pure Pt that lead to coking. Lastly, we confirm the predictions of our theoretical model and experimentally demonstrate that the selectivity of ethyl (de)hydrogenation can be controlled by changing the surface coverage of hydrogen.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Scattering neutrinos, spin models, and permutations

We consider a class of Heisenberg all-to-all coupled spin models inspired by neutrino interactions in a supernova with N degrees of freedom. These models are characterized by a coupling matrix that is relatively simple in the sense that there are only a few, relative to N , nontrivial eigenvalues, in distinction to the classic Heisenberg spin-glass models, leading to distinct behavior in both the high-temperature and low-temperature regimes. When the momenta of the neutrinos are uniform and random in directions, we can calculate the large- N partition function for the quantum Heisenberg model. In particular, the high-temperature partition function predicts a non-Gaussian density of states, providing interesting counterexamples showing the limits of general theorems on the density of states for quantum spin models. We can repeat the same argument for classical Heisenberg models, also known as rotor models, and we find the high-temperature expansion is completely controlled by the eigenvalues of the coupling matrix, and again predicts non-Gaussian behavior for the density of states as long as the number of eigenvalues does not scale linearly with N . Indeed, we derive the amusing fact that these partition functions are essentially the generating function for counting permutations in the high-temperature regime. Finally, for the case relevant to neutrinos in a supernova, we identify the low-temperature phase as a unique state with the direction of the momenta of the neutrino dictating its coherent state in flavor-space, a state we dub the “flavor-momentum-locked” state. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2011

Conducting Well-Controlled Ion Irradiations To Understand Neutron Irradiation Effects In Materials

A firm understanding of the effect of radiation on materials is required to develop predictive models of materials behavior in-reactor and provide a foundation for creating new, more radiation-tolerant materials. Ion irradiation can serve this purpose for nuclear reactor components and is becoming a key element of materials development for advanced nuclear reactors. Ion irradiations can be conducted quickly, at low cost, and with precise control over irradiation temperature, temperature uniformity, dose rate, dose uniformity and total dose. During proton irradiations the 2{sigma}(twice the standard deviation) of the sample temperature is generally below {approx}7 deg. C, the dose rate variation {approx}3%, the dose uncertainty {approx}3%, and there is an excellent temperature and dose uniformity across the irradiated area. In this article, we describe the experimental setup and irradiation procedure used to conduct well-controlled ion irradiations at the University of Michigan.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

A HPC Theory-Guided Machine Learning Cyberinfrastructure for Communicating Hydrometeorological Data Across Scales

High-resolution predictions of hydrometeorological variables are critical for supporting hydropower generation decisions and flood control at hydroelectric power plants. Traditional climate and hydrologic models rely on the numerical simulation of detailed physical processes. Therefore, running these simulations is time-, labor-, and computation-intensive. Improving the spatial and temporal resolution in these modeling outputs could lead to cubic increases in both the simulation time and computational demands, rendering high-resolution hydrometeorological predictions expensive and impractical. Many past studies apply the super resolution (SR) technique to downscale climate models using deep learners. However, deep learners are deemed “black-boxes,” as their derivation processes from low-resolution outputs to high-resolution outputs are often hidden. Their results are difficult for domain scientists to interpret and validate. Thus, there is a need for an exploratory machine learning approach that can partially integrate domain-specific theory and knowledge into the data-driven mapping process between simulation outputs of different spatial scales. The domain-specific theory and knowledge can be incorporated into the data model through an inductive approach in which process-related environmental variables are used and analyzed as key drivers (i.e., environmental surrogates) to reflect the complex physical processes. Many of these variables, such as land use land cover, soil types, topography, digital elevation, air temperature, and various watershed characteristics, can be directly measured through sensors or remote sensing techniques. Additionally, SR applications that can downscale hydrological and hydrodynamics models to efficiently produce high-resolution (1 m) flood depth grids are still rare. Since the flood depth grid can be used to support critical decisions for flood control operation at hydroelectric power plants, it is crucial to enable an SR-based capability for interpolating high-resolution flood inundation maps.

13 HYDRO ENERGY↗