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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Large-Strain Time-Temperature Equivalence and Adiabatic Heating of Polyethylene

Time-temperature equivalence is a well-known phenomenon in time-dependent material response, where rapid events at a moderate temperature are indistinguishable from some occurring at modest rates but elevated temperatures. However, there is as-yet little elucidation of how well this equivalence holds for substantial plastic strains. In this work, we demonstrate time-temperature equivalence over a large range in a previously studied high-density polyethylene formulation (HDPE). At strain-rates exceeding 0.1/s, adiabatic heating confounds the comparison of nominally isothermal material response, apparently violating time-temperature equivalence. Strain-rate jumps can be employed to access the instantaneous true strain rate without heating. Adiabatic heating effects were isolated by comparing a locus of isothermal instantaneous flow stress measurements from strain-rate jumps up to 1/s with the predicted equivalent states at 0.01/s and 0.001/s in compression. Excellent agreement between the isothermal jump condition locus and the quasi-static tests was observed up to 50% strain, yielding one effective isothermal plastic response for each material for a given time-temperature equivalent state. These results imply that time-temperature equivalence can be effectively used to predict the deformation response of polymers during extreme mechanical events (large strain and high strain-rate) from measurements taken at reduced temperatures and nominal strain-rates in the laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series: Supporting Data

This dataset consists of soil temperature time series that were used to estimate soil thermal diffusivity and its uncertainty trough the probabilistic modelling approach developed and presented in the article named "Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series" and published in Earth Surface Dynamics. There are two compressed (.zip) files that contains synthetic (Synthetic_soiltemp_Teller.zip) and field (Field_soiltemp_Teller.zip) data. There is one MATLAB file that requires MATLAB to execute but any text editor can open it. The Synthetic_soiltemp_Teller.zip file includes 5 comma-delimited data files (.csv) each of which contains soil temperature time series generated through forward modeling (i.e., heat-conduction process in a heterogeneous medium using an explicit finite difference method) to mimic various types of temperature gradients, trend and fluctuations. These synthetic soil temperatures were used to investigate the impact of different environmental conditions on the uncertainty of thermal diffusivity estimates. The Field_soiltemp_Teller.zip file contains 28 comma-delimited data files (.csv) out of which (a) 27 files includes soil temperature time series recorded from 27 temperature probes located in a site along Teller Road about 40 km northwest of Nome, Alaska (64.72°N, 165.94°W), (b) one includes the name and coordinates of the 27 probes. These field soil temperatures were used to infer soil thermal diffusivity at numerous locations and depths in a discontinuous permafrost environment, and to evaluate the links between the estimated soil thermal diffusivity values and soil physical properties. The comma-delimited data files (.csv) of the synthetic and field soil temperature time series includes date and time (UTC) in the first column and soil temperature from 5 cm below the ground surface to 1.05 m depth (with 5 or 10 cm spacing between sensors) in the other columns. The measurements were acquired every 15 minutes. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The effect of varying Co layer thickness on the time-temperature characteristics of Co/Sb semimetal embedded magnetic nanoparticles

We report the effect of varying cobalt thickness on the temperature-dependent time decay of the electrical resistance of Co/Sb multilayer samples. We find that for a given temperature, a five fold change in the Co thickness produces a 100 fold change in the characteristic decay time of the resistance. We find that the characteristic decay time, as a function of temperature, follows an Arrhenius law. During deposition, the Co evolves single domain magnetic nanoparticles, on the Sb, in either a Volmer-Weber or Stranski-Krastanov growth mode. This metastable state is then encased in 2.5 nm of Sb producing an embedded nanoparticle system. Scanning tunneling microscopy measurements taken before sample aging (annealing at a given temperature for enough time to complete the resistance decay) and after aging show that these nanoparticles undergo morphological transformations during aging. These transformations lead to well defined time dependent decays in both the magnetization and the electrical resistance, making this material an excellent candidate for an electronic time-temperature sensor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Second-order statistical bootstrap for the uncertainty quantification of time-temperature-superposition analysis

Time-temperature superposition (TTS), which for decades has been a powerful method for long-term prediction from accelerated aging data, involves rigid-shifting isotherms in logarithmic time to produce a single master prediction curve. For simple thermo-rheological properties that accurately follow the TTS principle, the shifts can be easily determined, even manually by the eye. However, for many properties of interest, where the principle is obeyed only approximately, or the data is noisy, it is imperative to develop objective shifting techniques along with reliable uncertainty bounds. This work analyzes in detail the method of arclength-minimization as an unsupervised algorithm to determining optimum shifts and demonstrates that the method is nearly unbiased for all practical datasets with a variety of noise distributions. Moreover, if averaged over with-replacement (bootstrap) resamples, the predicted shifts follow a normal distribution, a fact that can be used to construct confidence interval for the master curve through a second-order bootstrap procedure.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermal Skin Damage During Reirradiation and Hyperthermia Is Time-Temperature Dependent

Purpose: To investigate the relationship of thermal skin damage (TSD) to time–temperature isoeffect levels for patients with breast cancer recurrence treated with reirradiation plus hyperthermia (reRT + HT), and to investigate whether the treatment history of previous treatments (scar tissue) is a risk factor for TSD. Methods and Materials: In this observational study, temperature characteristics of hyperthermia sessions were analyzed in 262 patients with recurrent breast cancer treated in the AMC between 2010 and 2014 with reirradiation and weekly hyperthermia for 1 hour. Skin temperature was measured using a median of 42 (range, 29-82) measurement points per hyperthermia session. Results: Sixty-eight patients (26%) developed 79 sites of TSD, after the first (n=26), second (n=17), third (n=27), and fourth (n=9) hyperthermia session. Seventy percent of TSD occurred on or near scar tissue. Scar tissue reached higher temperatures than other skin tissue (0.4°C, P<.001). A total of 102 measurement points corresponded to actual TSD sites in 35 of 79 sessions in which TSD developed. Thermal skin damage sites had much higher maximum temperatures than non-TSD sites (2.8°C, P<.001). Generalized linear mixed models showed that the probability of TSD is related to temperature and thermal dose values (P<.001) and that scar tissue is more at risk (odds ratio 0.4, P<.001). Limiting the maximum temperature of a measurement point to 43.7°C would mean that the probability of observing TSD was at most 5%. Conclusion: Thermal skin damage during reRT + HT for recurrent breast cancer was related to higher local temperatures and time–temperature isoeffect levels. Scar tissue reached higher temperatures than other skin tissue, and TSD occurred at lower temperatures and thermal dose values in scar tissue compared with other skin tissue. Indeed, TSD developed often on and around scar tissue from previous surgical procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Statistics of particle time-temperature histories: Progress report for August 2012

Progress toward predictions of the statistics of particle time-temperature histories is presented. The joint statistics of a scalar like the temperature and an exposure time scale are of interest. We have related these quantities to statistics of conserved scalars, their gradients and their diffusive time scales. Future work will apply the one-dimensional turbulence (ODT) model to evaluate the suitability of these statistics to predicting particle time-temperature histories. To that end, we have reported on past performance of the ODT model and have implemented a Lagrangian particle tracking capability into the ODT code. The results of ODT-driven particle dispersion have been compared with classical experimental data, and preliminary statistics of particle evolution in reacting flows are also reported.

Hewson, John C.↗
DOE OSTI2024

Interpreting test temperature and loading rate effects on the fracture toughness of polymer-metal interfaces via time–temperature superposition

Here, in this letter, we present interfacial fracture toughness data for a polymer-metal interface where tests were conducted at various test temperatures T and loading rates $\dot{δ}$. An adhesively bonded asymmetric double cantilever beam (ADCB) specimen was utilized to measure toughness. ADCB specimens were created by bonding a thinner, upper adherend to a thicker, lower adherend (both 6061 T6 aluminum) using a thin layer of epoxy adhesive, such that the crack propagated along the interface between the thinner adherend and the epoxy layer. The specimens were tested at T from 25 to 65 °C and $\dot{δ}$ from 0.002 to 0.2 mm/s. The measured interfacial toughness Γ increased as both T and $\dot{δ}$ increased. For an ADCB specimen loaded at a constant $\dot{δ}$, the energy release rate G increases as the crack length a increases. For this reason, we defined rate effects in terms of the rate of change in the energy release rate $\dot{G}$. Although not rigorously correct, a formal application of time–temperature superposition (TTS) analysis to the Γ data provided useful insights on the observed dependencies. In the TTS-shifted data, Γ decreased and then increased for monotonically increasing $\dot{G}$. Thus, the TTS analysis suggests that there is a minimum value of Γ. This minimum value could be used to define a lower bound in Γ when designing critical engineering applications that are subjected to T and $\dot{δ}$ excursions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probabilistic estimation of depth-resolved profiles of soil thermal diffusivity from temperature time series

Abstract. Improving the quantification of soil thermal and physical properties is key to achieving a better understanding and prediction of soil hydro-biogeochemical processes and their responses to changes in atmospheric forcing. Obtaining such information at numerous locations and/or over time with conventional soil sampling is challenging. The increasing availability of low-cost, vertically resolved temperature sensor arrays offers promise for improving the estimation of soil thermal properties from temperature time series, and the possible indirect estimation of physical properties. Still, the reliability and limitations of such an approach need to be assessed. In the present study, we develop a parameter estimation approach based on a combination of thermal modeling, sliding time windows, Bayesian inference, and Markov chain Monte Carlo simulation to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution (i.e., down to 5 to 10 cm vertical resolution) from soil temperature time series. We provide the necessary framework to assess under which environmental conditions (soil temperature gradient, fluctuations, and trend), temperature sensor characteristics (bias and level of noise), and deployment geometries (sensor number and position) soil thermal diffusivity can be reliably inferred. We validate the method with synthetic experiments and field studies. The synthetic experiments show that in the presence of median diurnal fluctuations ≥ 1.5 ∘C at 5 cm below the ground surface, temperature gradients > 2 ∘C m−1, and a sliding time window of at least 4 d the proposed method provides reliable depth-resolved thermal diffusivity estimates with percentage errors ≤ 10 % and posterior relative standard deviations ≤ 5 % up to 1 m depth. Reliable thermal diffusivity under such environmental conditions also requires temperature sensors to be spaced precisely (with accuracy to a few millimeters), with a level of noise ≤ 0.02 ∘C, and with a bias defined by a standard deviation ≤ 0.01 ∘C. Finally, the application of the developed approach to field data indicates significant repeatability in results and similarity with independent measurements, as well as promise in using a sliding time window to estimate temporal changes in soil thermal diffusivity, as needed to potentially capture changes in bulk density or water content.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Statistics of particle time-temperature histories

Particles in non-isothermal turbulent flow are subject to a stochastic environment that produces a distribution of particle time-temperature histories. This distribution is a function of the dispersion of the non-isothermal (continuous) gas phase and the distribution of particles relative to that gas phase. In this work we extend the one-dimensional turbulence (ODT) model to predict the joint dispersion of a dispersed particle phase and a continuous phase. The ODT model predicts the turbulent evolution of continuous scalar fields with a model for the cascade of fluctuations to smaller scales (the ‘triplet map’) at a rate that is a function of the fully resolved one-dimensional velocity field. Stochastic triplet maps also drive Lagrangian particle dispersion with finite Stokes numbers including inertial and eddy trajectory-crossing effects included. Two distinct approaches to this coupling between triplet maps and particle dispersion are developed and implemented along with a hybrid approach. An ‘instantaneous’ particle displacement model matches the tracer particle limit and provides an accurate description of particle dispersion. A ‘continuous’ particle displacement model translates triplet maps into a continuous velocity field to which particles respond. Particles can alter the turbulence, and modifications to the stochastic rate expression are developed for two-way coupling between particles and the continuous phase. Each aspect of model development is evaluated in canonical flows (homogeneous turbulence, free-shear flows and wall-bounded flows) for which quality measurements are available. ODT simulations of non-isothermal flows provide statistics for particle heating. These simulations show the significance of accurately predicting the joint statistics of particle and fluid dispersion. Inhomogeneous turbulence coupled with the influence of the mean flow fields on particles of varying properties alters particle dispersion. The joint particle-temperature dispersion leads to a distribution of temperature histories predicted by the ODT. Predictions are shown for the lower moments and the full distributions of the particle positions, particle-observed gas temperatures and particle temperatures. An analysis of the time scales affecting particle-temperature interactions covers Lagrangian integral time scales based on temperature autocorrelations, rates of temperature change associated with particle motion relative to the temperature field and rates of diffusional change of temperatures. These latter two time scales have not been investigated previously; they are shown to be strongly intermittent having peaked distributions with long tails. The logarithm of the absolute value of these time scales exhibits a distribution closer to normal.

Hewson, John C.↗
DOE OSTI2020

Metamodeling Time-Temperature Creep Parameters

Abstract There exist many time-temperature parameter (TTP) models for creep rupture prediction of components including the Larson–Miller (LM), Manson–Haferd (MH), Manson–Brown (MB), Orr–Sherby–Dorn (OSD), Manson–Succop (MS), Graham–Walles (GW), Chitty–Duval (CD), Goldhoff–Sherby (GS) models. It remains a challenge to determine which model is “best”, capable of accurate interpolation and physically realistic extrapolation of creep rupture data for a given material. In this study, metamodeling is applied to create a unified TTP metamodel that combines and regresses into twelve TTP models (eight existing and four newly derived). An analysis of the mathematical problems that exist in TTP models is provided. A matlab code is written that can: (1) calibrate the material constants of any of the twelve TTP models (using the metamodel); (2) determine the most suitable stress-parameter function; (3) and report the normalized mean square error (NMSE) of rupture predictions for a given material database. Using the metamodel, and code, a design engineer can make an intelligent selection of the “best” TTP model for creep resistant design. This process is demonstrated using four isotherms of alloy P91 creep rupture data. To assess the influence of material, further validation is performed on alloys Hastelloy X, 304SS, and 316SS. It is determined that the “best” model is dependent on material type and the quality and quantity of available data.

Engineering↗
DOE OSTI2015

A geometry-based approach to determining time-temperature superposition shifts in aging experiments

A powerful way to expand the time and frequency range of material properties is through a method called time-temperature superposition (TTS). Traditionally, TTS has been applied to the dynamical mechanical and flow properties of thermo-rheologically simple materials, where a well-defined master curve can be objectively and accurately obtained by appropriate shifts of curves at different temperatures. However, TTS analysis can also be useful in many other situations where there is scatter in the data and where the principle holds only approximately. In such cases, shifting curves can become a subjective exercise and can often lead to significant errors in the long-term prediction. This mandates the need for an objective method of determining TTS shifts. Here, we adopt a method based on minimizing the “arc length” of the master curve, which is designed to work in situations where there is overlapping data at successive temperatures. We examine the accuracy of the method as a function of increasing noise in the data, and explore the effectiveness of data smoothing prior to TTS shifting. In conclusion, we validate the method using existing experimental data on the creep strain of an aramid fiber and the powder coarsening of an energetic material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Time-Temperature-Transformation (TTT) Diagram for a Sludge Batch 9 Glass Composition Based on Coupled-Operation with the Salt Waste Processing Facility

The amorphous structure of a glass waste form has the potential to rearrange into crystalline phases at temperatures between the liquidus temperature and the glass transition temperature (Tg). Certain phases that can form will be detrimental to the durability of the glass and it is important to know the conditions that promote devitrification. The canister-centerline-cooling (CCC) profile is used to replicate the area within the center of the Defense Waste Processing Facility (DWPF) canister with the slowest cooling during the initial cool down after pouring, which has the greatest potential for crystallization. Other time-temperature conditions that cause significant changes in either phase structure or phase composition are identified by a time-temperature-transformation (TTT) study. The phase stability of a waste form must be determined as a part of the Waste Acceptance Product Specifications (WAPS) if it is to eventually be stored in a geologic repository. This requires the creation of a TTT diagram and analysis of the Tg, as defined by the Department of Energy (DOE). The previous TTT study for a DWPF glass waste form was completed in 2010 prior to coupled operation with the Salt Waste Processing Facility (SWPF). SWPF transfers two high activity waste streams to DWPF for vitrification: a cesium-containing strip effluent and a stream containing monosodium titanate/sludge solids. These SWPF streams were first transferred to DWPF for vitrification during Sludge Batch 9 (SB9) in 2021. The impact of these SWPF streams on crystallization behavior was not determined in previous studies and the need for data to satisfy WAPS Specification 1.4 for SB9 was identified.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Statistics of particle time-temperature histories :

Progress toward predictions of the statistics of particle time-temperature histories is presented. These predictions are to be made using Lagrangian particle models within the one-dimensional turbulence (ODT) model. In the present reporting period we have further characterized the performance, behavior and capabilities of the particle dispersion models that were added to the ODT model in the first period. We have also extended the capabilities in two manners. First we provide alternate implementations of the particle transport process within ODT; within this context the original implementation is referred to as the type-I and the new implementations are referred to as the type-C and type-IC interactions. Second we have developed and implemented models for two-way coupling between the particle and fluid phase. This allows us to predict the reduced rate of turbulent mixing associated with particle dissipation of energy and similar phenomena. Work in characterizing these capabilities has taken place in homogeneous decaying turbulence, in free shear layers, in jets and in channel flow with walls, and selected results are presented.

Hewson, John C.↗
DOE OSTI2021

Time-temperature-superposition analysis of diverse datasets by the minimum-arclength method: long-term prediction with uncertainty margins

In a recent publication, we carried out an extensive analysis of an unsupervised method of determining optimum shift factors in time-temperature-superposition of accelerated-aging data that involves minimizing the vertical arclength to obtain the master curve. For synthetic Arrhenius data with a variety of noise distributions, the work showed that, in conjunction with bootstrap-resampling, the method can produce reliable estimates of the mean activation energy along with uncertainty quantification. Here, we apply the above method to six different datasets taken from the published literature and demonstrate accurate prediction of mean activation energy from the data as-is without the need for any pre-processing or fitting. We also compare uncertainty margins computed by second-order bootstrap with that by linear regression theory and show that the former appears to provide consistent margins in the presence of common noise types in real data, including intra-isotherm measurement-errors, sample-to-sample variations, and intrinsic deviation from perfect Arrhenius behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing the Understanding of Snow Accumulation, Melting, and Associated Thermal Insulation Using Spatially Dense Snow Depth and Temperature Time Series

Snow thermal insulation is a critical factor influencing ground thermal dynamics and associated biogeochemical processes. We analyzed the spatiotemporal variability of snow accumulation, melting, and thermal insulation dynamics using spatially dense, collocated snow depth and ground interface temperature time series over two consecutive years. We demonstrated that considering late‐winter snow depth alone was insufficient to fully capture the complexity in snow and insulation dynamics. The influence of vegetation and topography on snow depth distribution varied over the season, across sites and years. We found that deep snow with a long melting period had a substantial impact on thawing n‐factors. To better predict snow insulation effects, we proposed a new weighted snow depth metric that integrates mean daily snow depth and air temperature throughout the cold season. Our results provide insights for developing space‐time remote sensing products and evaluating the representation of snow and permafrost processes in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An 8.8 ps RMS Resolution Time-To-Digital Converter Implemented in a 60 nm FPGA with Real-Time Temperature Correction

This paper presented a non-uniform multiphase (NUMP) time-to-digital converter (TDC) implemented in a field-programmable gate array (FPGA) with real-time automatic temperature compensation. NUMP-TDC is a novel, low-cost, high-performance TDC that has achieved an excellent performance in Altera Cyclone V FPGA. The root mean square (RMS) for the intrinsic timing resolution was 2.3 ps. However, the propagation delays in the delay chain of some FPGAs (for example, the Altera Cyclone 10 LP) vary significantly as the temperature changes. Thus, the timing performances of NUMP-TDCs implemented in those FPGAs are significantly impacted by temperature fluctuations. In this study, a simple method was developed to monitor variations in propagation delays using two registers deployed at both ends of the delay chain and compensate for changes in propagation delay using a look-up table (LUT). When the variations exceeded a certain threshold, the LUT for the delay correction was updated, and a bin-by-bin correction was launched. Using this correction approach, a resolution of 8.8 ps RMS over a wide temperature range (5 °C to 80 °C) had been achieved in a NUMP-TDC implemented in a Cyclone 10 LP FPGA.

59 BASIC BIOLOGICAL SCIENCES↗