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Results for “Temperature estimation”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Estimating Soil Thermal Inertia Profiles From the Passive Equilibration of a Temperature Probe

Knowledge of the distribution of soil thermal properties is important for understanding subsurface hydrological and biogeochemical processes. This study describes and evaluates quick thermal profiling (QTP), a new measurement technique aimed at providing rapid, depth-resolved measurements of soil thermal inertia at numerous locations across the landscape. A cylindrical probe with temperature sensors at multiple depths is quickly inserted into the ground, and soil thermal inertia is estimated from how quickly the probe temperature equilibrates with the soil. To this end, a finite volume heat transfer model is used to generate temperature equilibration time series across combinations of controlling factors, and a gridded search inversion approach is applied to infer soil thermal inertia. Field tests in the Arctic indicate that QTP measurements have a minimum uncertainty of 0.14 J m −2 K −1 s −1/2 and covary with dual-probe heat pulse thermal analyzer measurements (concordance correlation coefficient = 0.56) with a root-mean-square error of 0.40 J m −2 K −1 s −1/2 . Besides demonstrating the value of QTP for estimating thermal inertia, this study identifies various sources of measurement uncertainty, particularly probe-soil contact resistance and frictional heating. Further, analysis of soil samples indicates that thermal inertia can be used to estimate thermal conductivity and dry bulk density in the studied area, although such inferences are highly site-specific. Overall, the QTP method holds promise to generate thermal inertia data products and to complement other characterization approaches for advancing understanding of soil properties across far more locations than is currently possible.

Lamb, J. R. [Lawrence Berkeley National Laboratory↗
DOE OSTI2021

Kinetic studies of excited singlet oxygen atom O( 1 D) reactions with ethanol

In this work, themultichannel reaction of excited singlet oxygen atom with ethanol, O( 1 D) + C 2 H 5 OH (1), was studied in a photolysis flow reactor coupled with mid-infrared Faraday rotation spectroscopy (FRS) and UV-IR direct absorption spectroscopy (DAS) at 297 K with reactor pressures of 60, 120, and 150 Torr (bath He). The excited singlet oxygen atom was generated through the photolysis of O 3 at 266 nm. The photon flux and O( 1 D) concentrations were determined by in situ actinometry based on O 3 depletion. Temporal profiles of OH and H 2 O were monitored via DAS signals at ca. 3568.62 and 3568.29 cm –1 , while temporal profiles of HO 2 were measured via FRS signals at ca. 1396.90 cm –1 . The branching ratios of the target reaction (1) were determined by fitting temporal profiles to simulations from an in-house reaction mechanism. Two major reaction channels were identified as CH 3 CHOH + OH and CH 3 O + CH 2 OH, and their branching ratios were determined as 0.46 ± 0.12 and 0.42 ± 0.11, respectively. A specific HO 2 + RO 2 reaction between HO 2 and O 2 CH 2 CH 2 OH (β-RO 2 ) at the low-temperature range is estimated in this work as HO 2 + O 2 CH 2 CH 2 OH → products with a rate constant of 7 × 10 –12 cm 3 molecule –1 s –1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine learning for fundamental spectroscopic and thermodynamic data of actinides and lanthanides

Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.

Atomic spectroscopy↗
DOE OSTI2020

Transient solute drag and strain aging of dislocations

The transient drag force exerted by mobile solutes on a moving dislocation is computed using continuum theory. These mobile solutes form so-called Cottrell atmospheres around dislocations during static and dynamic strain aging. We evaluate the evolution of the drag force exerted by the atmosphere under two velocity time-histories: impulsive acceleration to a chosen velocity and a constant acceleration rate. A particular focus is on the conditions under which the stationary limit assumed by theories of dynamic strain aging is obeyed. According to our results, two conditions—one on the dislocation velocity and one on the acceleration rate—must be satisfied for the stationary limit to hold. Using the Orowan relation and a line tension model, we obtain estimates for the temperature, stress, strain rate, and dislocation density regimes where the stationary limit is valid, and compare these results with experiments for a few material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A detailed study of pre-heating effects in electron beam melting powder bed fusion process

Metal-based additive manufacturing processes, such as powder bed fusion with electron beam (PBF-EB) process, also referred to as electron beam melting (EBM), can produce high-density parts with minimal residual stresses due to the uniform and coherent preheating of the powder bed. However, understanding and controlling the multiple stages of preheating is required to enable the production of high-quality, consistent parts of various materials. This work presents a large-scale, multi-layer, three-dimensional numerical analysis focused on studying the preheating stages for predicting thermal history during the PBF-EB process. The model follows a continuous multi-stage cyclic process, that incorporates all the main stages of the PBF-EB process for 316 L stainless steel. This includes the gradual deposition of a new powder layer, the first and second preheating levels of the powder bed, and the energy deposition during melting (excluding the actual melt-pool behavior simulation). The model employs an adaptive time-scaling approach that automatically adjusts the energy deposition for each solution time-increment. This allows for localized changes in time-resolution over an otherwise computationally expensive multi-layer procedure. The material property variations are also taken into account, with an emphasis on the subtle irreversible changes in powder effective thermal conductivity after the two requisite preheating stages of the powder bed. This effect is studied using simplified conductivity models from the literature for partially sintered powder, validated by a dedicated experiment and numerical simulation. The large-scale model is then used to estimate the actual temperatures during first and second preheating levels for 316 L steel, which is not yet fully supported commercially for PBF-EB. Model predictions are corroborated by experiments, using and analyzing IR images, taken at the completion of each layer by the machine’s built-in infrared camera. The current model also incorporates a qualitative assessment for the effects of conductivity change during pre-heating, as well as evaluates the applicability of the time-scaling approach.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The impact of anisotropic thermal expansion on the isothermal annealing of polycrystalline α -uranium

Although grain growth impacts microstructural evolution in a wide variety of materials systems, the effect of anisotropic thermal expansion on grain boundary mobility and texture evolution has not been widely studied. Anisotropic thermal expansion occurs in multiple non-cubic metals, and the thermomechanical processing behavior of these materials can be better understood with further study into the impact of thermal expansion on grain boundary mobility and texture evolution. In this work, we develop a mesoscale phase field model of grain growth that includes the effect of anisotropic thermal expansion, which is applied to study polycrystalline α-uranium, a highly anisotropic metal. Three-dimensional simulations on polycrystalline a-uranium with and without thermal expansion eigenstrains are performed to study the grain boundary mobility and texture evolution as a function of temperature. A strain-free temperature of 933 K is selected, and the system is studied at within the range of 873 – 933 K at intervals of ten degrees, resulting in increasing thermal eigenstrain with decreasing temperature. We also estimate a grain boundary mobility prefactor and activation energy based on existing experimental data of isothermal annealing of α-uranium. The grain boundary mobility is found to display significant deviation from Arrhenius behavior with the inclusion of thermal expansion eigenstrain as the amount of thermal eigenstrain (and thus elastic strain energy within the system) increases. This result explains an experimentally observed grain boundary mobility deviation from Arrhenius behavior. Furthermore, the texture evolution is affected, such that the grain orientations become less random with increasing thermal eigenstrain, which could explain experimentally observed texture behavior. These results indicate that the effect of thermal expansion should be considered when predicting the thermomechanical processing behavior of α-uranium and other materials with anisotropic thermal expansion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

First principles investigation of dopants and defect complexes in CdSe$_x$Te$_{1-x}$

Se alloying is a common approach to improve the performance of CdTe solar cells by tuning the bandgap, defect levels, and carrier density. A fundamental understanding of these improvements, specifically the effect of Se alloying on the behavior of defects and dopants in CdTe, remains unclear. Here, in this work, we present a density functional theory (DFT) study of point defect energetics in CdTe and CdSe x Te 1-x with x = 0.25, leading to a comparison of how native defects, dopants (As and Cu), impurities (Cl and O), and related defect complexes behave in CdTe vs CdSe x Te 1-x . Our calculations, performed by combining semi-local and nonlocal hybrid functionals, show a general lowering of the formation energies of native defects as well as substitutional defects formed by As and Cl upon Se addition. For successful p-type doping with As, destabilizing Cl-based defects in the CdSeTe lattice would be essential. We find evidence for some low-energy defect complexes of As, Cl, and O in CdSe 0.25 Te 0.75 . The computed defect formation energies further enable estimates of temperature-dependent defect concentrations and self-consistent Fermi levels. A comparison of defect energetics with the energies of impurity phases reveals that As, Cu, Cl, and O overwhelmingly prefer being segregated to unwanted As 2 O 5 , AsCl 3 , Cd 2 AsCl 2 , and CuO x phases rather than remain at defect sites, but such segregation is less likely to happen in CdSe 0.25 Te 0.75 than in CdTe. Overall, our work presents a list of likely defects and complexes in CdTe and Se-incorporated CdTe, paving the way to explain and mitigate limited dopant activation in experimental observations.

CdTe↗
DOE OSTI2022

Unconventional Pathways to Carbide Phase Synthesis via Thermal Decomposition of UI 4 (1,4-dioxane) 2

UI4(1,4-dioxane) 2 was subjected to laser-based heating-a method that enables localized, fast heating (T > 2000 °C) and rapid cooling under controlled conditions (scan rate, power, atmosphere, etc.)-to understand its thermal decomposition. A predictive computational thermodynamic technique estimated the decomposition temperature of UI 4 (1,4-dioxane) 2 to uranium (U) metal to be 2236 °C, a temperature achievable under laser irradiation. Dictated by the presence of reactive, gaseous byproducts, the thermal decomposition of UI 4 (1,4-dioxane) 2 under furnace conditions up to 600 °C revealed the formation of UO 2 , UI x , and U(C 1–x O x ) y , while under laser irradiation, UI 4 (1,4-dioxane) 2 decomposed to UO 2 , U(C 1–x O x ) y , UC 2–z O z , and UC. Despite the fast dynamics associated with laser irradiation, the central uranium atom reacted with the thermal decomposition products of the ligand (1,4-dioxane = C 4 H 8 O 2 ) instead of producing pure U metal. In conclusion, the results highlight the potential to co-develop uranium precursors with specific irradiation procedures to advance nuclear materials research by finding new pathways to produce uranium carbide.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Assessment of Molten Eutectic LiF-NaF-KF Density through Experimental Determination and Semiempirical Modeling

Molten salts have favorable material properties for use in high-temperature energy systems, including thermal energy storage systems, concentrating solar power plants, nuclear reactors, and various industrial manufacturing processes. Knowledge of chemical and thermophysical property data is essential for the design and optimization of these systems, yet data are often limited or uncertain for many candidate salts due to the difficulty of thermophysical property measurements at relevant temperatures (e.g., 500–900 °C). Here, the density of molten LiF-NaF-KF eutectic is reassessed through review of previous experimental data, new density measurements from 470 to 800 °C, and semiempirical modeling. Additionally, the density was measured using the displacement technique. Compositional and temperature-dependent density estimates were calculated with a multidimensional Redlich–Kister model. The results of the new experimental measurements agree within 2% of the modeled density of molten eutectic LiF-NaF-KF. The Redlich–Kister model’s prediction shows a near-ideal density behavior for the LiF-NaF-KF system and is promising for the estimation of off-eutectic LiF-NaK-KF densities. Finally, through review of the existing literature and comparison to new measurements, recommendations are made for the density of LiF-NaF-KF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Metastable CaSH 3 Phase Composed of HS Honeycomb Sheets that is Superconducting Under Pressure

Evolutionary searches have predicted a number of ternary Ca–S–H phases that could be synthesized at pressures of 100–300 GPa. P6 3 /mmc CaSH 2 , Pnma CaSH 2 , Cmc2 1 CaSH 6 , and I$\bar{4}$CaSH 20 were composed of a Ca–S lattice along with H 2 molecules coordinated in a “side-on” fashion to Ca. The H–H bond lengths in these semiconducting phases were elongated because of H 2 σ → Ca d donation, and Ca d → H 2 σ* back-donation, via a Kubas-like mechanism. P$\bar{6}$m2 CaSH 3 , consisting of two-dimensional HS and CaH 2 sheets, was metastable and metallic above 128 GPa. The presence of van Hove singularities increased its density of states at the Fermi level and concomitantly the superconducting critical temperature, which was estimated to be as high as ~100 K at 128 GPa. Furthermore, this work will inspire the search for superconductivity in materials based upon honeycomb HX (X = S, Se, Te) and MH 2 (M = Mg, Ca, Sr, Ba) layers under pressure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

On the Importance of Sea Surface Temperature for Aerosol–Induced Brightening of Marine Clouds and Implications for Cloud Feedback in a Future Warmer Climate

Marine low clouds are one of the greatest sources of uncertainty for climate projection. We present an observed climatology of cloud albedo susceptibility to cloud droplet number concentration perturbations (S 0 ) with changing sea surface temperature (SST) and estimated inversion strength for single-layer warm clouds over the North Atlantic Ocean, using eight years of satellite and reanalysis data. The key findings are that SST has a dominant control on S 0 in the presence of co-varying synoptic conditions and aerosol perturbations. Cloud brightening in regions with positive S 0 weakens with increasing local SST. Higher SST significantly hastens cloud-top evaporation with increasing aerosol loading, by accelerating entrainment and facilitating entrainment drying. In a global-warming-like scenario where aerosol loading is reduced, more cloud brightening is expected, mainly as a result of reduced entrainment drying. Furthermore, our results imply a less positive low-cloud liquid water path feedback in a warmer climate with decreasing aerosol loading.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Recent Weakening of the Global Radiative Feedback

Earth's climate stability, characterized by the global radiative feedback parameter (𝜆), varies decadally due to changing surface temperature patterns. Recent variations in 𝜆 are poorly understood as coordinated model simulations typically end in 2014. We apply a convolutional neural network trained on climate model simulations to observation-based surface temperature reconstructions to estimate variations in 𝜆 up to 2025. We find that 𝜆 reached a minimum (maximum stability) around the mid 1990s (𝜆 ≃ −3 Wm −2 /K), but has since weakened significantly (𝜆 ≃ −2 Wm −2 /K). We confirm these results with climate model simulations extended to 2022. The recent 𝜆 weakening is not significantly affected by El Niño Southern Oscillation or Pacific Decadal Oscillation. Attribution reveals that warming in the subtropical Northeast Pacific is an important driver of the recently weakened feedback, confirmed by targeted experiments in E3SMv2. Our approach enables near real-time monitoring of Earth's climate stability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High-temperature phonon-mediated superconductivity in monolayer Mg 2 B 4 C 2

A two-dimensional material – Mg 2 B 4 C 2 , belonging to the family of the conventional superconductor MgB 2 , is theoretically predicted to exhibit superconductivity with critical temperature T c estimated in the 47–48 K range (predicted using the McMillian-Allen-Dynes formula) without any tuning of external parameters such as doping, strain, or substrate-induced effects. The origin of such a high intrinsic T c is ascribed to the presence of strong electron-phonon coupling and large density of states at the Fermi level. This system is obtained after replacing the chemically active boron-boron surface layers in a MgB 2 slab by chemically inactive boron-carbon layers. Hence, the surfaces of this material are inert. Our calculations confirm the stability of 2D Mg 2 B 4 C 2 . We also find that the key features of this material remain essentially unchanged when its thickness is increased by modestly increasing the number of inner MgB 2 layers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Compositional heterogeneity in Cs y FA 1- y Pb(Br x I 1- x ) 3 perovskite films and its impact on phase behavior

Hybrid organic inorganic lead halide perovskite semiconductors of the form Cs y FA 1-y Pb(Br x I 1-x ) 3 are promising candidate materials for high-efficiency photovoltaics. Notably, cation and anion substitution can be used to tune the band gaps to optimize performance and improve stability. However, multi-component materials can be prone to compositional and structural inhomogeneity and the extent, length scale and impact of this heterogeneity on lead halide perovskite properties are not well understood. Here we use synchrotron X-ray diffraction to probe the evolution of crystal structure across the tetragonal-to-cubic phase transition for a series of Cs y FA 1-y Pb(Br x I 1-x ) 3 thin films with x = 0.05 to 0.3 and y = 0.17 to 0.40. We find that the transition occurs across a broad temperature range of approximately 40 °C, much larger than for pure compounds such as MAPbI 3 and MAPbBr 3 . Finally, we hypothesize that this smearing of the phase transition is due to compositional inhomogeneities that give rise to a distribution of local transition temperatures and we estimate the composition varies by about 10% to 15% with likely greater heterogeneity for the halide anion than the cation. This approach of mapping the transition is a simple and effective method of assessing heterogeneity, enabling evaluation of its impact.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Resistive hose modes in tokamak runaway electron beams

Beams of energetic runaway electrons are generated during disruptions in tokamaks, and fluid models are used to study their effects on macroscale dynamics. Linear computations of a massless, runaway electron beam coupled to MHD plasma show that resistive hose instabilities grow faster than tearing modes at large resistivity. Eigenvalue results with reduced models of the resistive hose instability are compared with results from the full MHD and beam system, showing that the resistive hose decouples from any plasma response. An estimate of plasma temperature at which growth of the resistive hose dominates tearing for post-disruption DIII-D plasma parameters is in a physically relevant regime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Wave steepening and shock formation in ultracold neutral plasmas

We present observations of wave steepening and signatures of shock formation during expansion of ultracold neutral plasmas formed with an initial density distribution that is centrally peaked and decays exponentially with distance. The plasma acceleration and velocity decrease at large distance from the plasma center, leading to central ions overtaking ions in the outer regions and the development of a steepening front that is narrow compared to the size of the plasma. The density and velocity change dramatically across the front, and significant heating of the ions is observed in the region of steepest gradients. For a reasonable estimate of electron temperature, the relative velocity of ions on either side of the front modestly exceeds the local sound speed (Mach number M≳1). This indicates that by sculpting steep density gradients, it is possible to create the conditions for shock formation, or very close to it, opening a new avenue of research for ultracold neutral plasmas.

Computational fluid dynamics↗
DOE OSTI2023

Data-driven predictions of the time remaining until critical global warming thresholds are reached

Leveraging artificial neural networks (ANNs) trained on climate model output, we use the spatial pattern of historical temperature observations to predict the time until critical global warming thresholds are reached. Although no observations are used during the training, validation, or testing, the ANNs accurately predict the timing of historical global warming from maps of historical annual temperature. The central estimate for the 1.5 °C global warming threshold is between 2033 and 2035, including a ±1σ range of 2028 to 2039 in the Intermediate (SSP2-4.5) climate forcing scenario, consistent with previous assessments. However, our data-driven approach also suggests a substantial probability of exceeding the 2 °C threshold even in the Low (SSP1-2.6) climate forcing scenario. While there are limitations to our approach, our results suggest a higher likelihood of reaching 2 °C in the Low scenario than indicated in some previous assessments—though the possibility that 2 °C could be avoided is not ruled out. Explainable AI methods reveal that the ANNs focus on particular geographic regions to predict the time until the global threshold is reached. Our framework provides a unique, data-driven approach for quantifying the signal of climate change in historical observations and for constraining the uncertainty in climate model projections. Given the substantial existing evidence of accelerating risks to natural and human systems at 1.5 °C and 2 °C, our results provide further evidence for high-impact climate change over the next three decades.

54 ENVIRONMENTAL SCIENCES↗