Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “validation data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A thermodynamic model of integrated liquid-to-liquid thermoelectric heat pump systems

Thermoelectric (TE) heat pumps (TEHPs) are advantageous for heating and cooling in various applications because of their modularity and simple design. A TEHP system includes the TE modules with p- and n-type materials bonded to substrates, plus heat exchangers, thermal interfaces to the heat exchangers, and heat transfer fluids. Although modeling an individual TE module has been extensively studied, limited studies have reported performance at the larger system-level. Furthermore, no prior study has addressed the impact of temperature-dependent TE material properties (e.g., electric resistivity, thermal conductivity, and Seebeck coefficient) on overall heat-pump-system-level performance. Here, this work presents a mathematical model for TEHP system performance based on Goldsmid's approach for TE material performance, “effective” TE material properties, Gnielinski's correlation for convective heat transfer, and thermal balance theory for a heat exchange network. This combined approach provides an accurate model of the liquid-to-liquid TEHP system. Three different approaches—one empirical, one based on the manufacturer's specifications, and one drawn from the literature—were then used to determine values for TE material properties. The first two methods treated properties as constants, while the last approach treated properties as surface-temperature-based functions. Finally, experimental TEHP data was used to validate the models, all with relative absolute deviations of approximately 10% when predicting heating capacity and 10%–25% when forecasting cooling capacity up to a 30 K surface temperature lift. The results demonstrated that, at the TEHP system level, the TE material properties could be treated as constants, avoiding solver iterations and reducing the performance uncertainty by up to 95%.

42 ENGINEERING↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The role of surface forces in environment-enhanced cracking of brittle solids

Fracture initiation and propagation in brittle materials is promoted in surface-reactive (sorptive) environments, a phenomenon known as subcritical crack growth (SCG). Laboratory measured crack-propagation velocity vs. stress intensity factor relationships typically exhibit highly nonlinear, multi-stage characteristics that are sensitive to environmental factors such as adsorbate concentration and temperature. For practical purposes, empirical relationships (e.g., a power law) have been used to describe this complex phenomenon. However, how the overall SCG behavior emerges from the underlying fundamental processes near the crack tip, such as the interaction of the crack surfaces separated by only a few nanometers and mass transport within the nano-confined space, is still not well understood. This paper develops a mechanistic, surface-force-based fracture theory (SFFT) which integrates surface force models, fluid transport models, and linear elastic fracture mechanics to quantitatively explain the multi-stage characteristics of SCG in brittle solids. A numerical model is developed based on SFFT and solved through an implicit partitioned scheme for efficiency and modularity. The results are validated by Wiederhorn's data on crack propagation in soda-lime glasses at a wide range of relative humidity levels. We show that, for the first time, the entire range of an SCG curve can be captured by a single physics-based model. The predicted SCG curves reveal that the development of repulsive disjoining pressure behind the crack tip can be responsible for the reduced apparent fracture toughness in a sorptive environment. In conclusion, the shape of the SCG curve, and its changes with respect to the environment, is found to critically depend on the assumed transport models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

NaroNet: Discovery of tumor microenvironment elements from highly multiplexed images

Understanding the spatial interactions between the elements of the tumor microenvironment -i.e. tumor cells. fibroblasts, immune cells- and how these interactions relate to the diagnosis or prognosis of a tumor is one of the goals of computational pathology. We present NaroNet, a deep learning framework that models the multi-scale tumor microenvironment from multiplex-stained cancer tissue images and provides patient-level interpretable predictions using a seamless end-to-end learning pipeline. Trained only with multiplex-stained tissue images and their corresponding patient-level clinical labels, NaroNet unsupervisedly learns which cell phenotypes, cell neighborhoods, and neighborhood interactions have the highest influence to predict the correct label. To this end, NaroNet incorporates several novel and state-of-the-art deep learning techniques, such as patch-level contrastive learning, multi-level graph embeddings, a novel max-sum pooling operation, or a metric that quantifies the relevance that each microenvironment element has in the individual predictions. We validate NaroNet using synthetic data simulating multiplex-immunostained images where a patient label is artificially associated to the -adjustable- probabilistic incidence of different microenvironment elements. We then apply our model to two sets of images of human cancer tissues: 336 seven-color multiplex-immunostained images from 12 high-grade endometrial cancer patients; and 382 35-plex mass cytometry images from 215 breast cancer patients. In both synthetic and real datasets, NaroNet provides outstanding predictions of relevant clinical information while associating those predictions to the presence of specific microenvironment elements.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Simulation of methanol synthesis in a membrane-contactor reactor

In a recent paper we investigated experimentally in the laboratory the methanol synthesis (MeS) reaction in a novel high-pressure membrane reactor (MR) using tetraethylene glycol dimethyl ether (TGDE) and an ionic liquid (IL) as the sweep liquids. We reported significantly higher carbon conversion (per single-pass) compared to a traditional packed-bed reactor (PBR), as a result of the in-situ removal of methanol by the sweep liquid. Here, in this paper, a steady-state model is developed to simulate the MR behavior during MeS and is validated by the experimental data. The model is then used to study the reactor behavior for a broader range of operating conditions beyond those that can be accessed and studied experimentally in the laboratory-scale reactor. Converting the model equations into their dimensionless form helps to identify the key dimensionless groups determining reactor performance, enables one to better evaluate the efficacy of the MR system, and guides further process design and scale-up.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Gamma-ray spectrum variations for surface measurements of uranium hexafluoride cylinders

Here, this work aims to characterize features that contribute to the spectral variations that appear in gamma-23 ray spectra of filled uranium hexafluoride (UF 6 ) cylinders. While the enrichment of the UF 6 in a 24 cylinder can greatly affect gamma-ray spectra at peaks such as the 185.7 keV peak and be used to 25 verify enrichment, many other characteristics of a filled UF 6 cylinder can affect the spectra. These 26 characteristics vary on a per-cylinder basis and can include: the time between when a cylinder was 27 filled and when it was measured; the previous fill history of a cylinder; how the cylinder was most 28 recently filled; the UF 6 geometric distribution within the cylinder; and the measurement location 29 along the cylinder. This work explores spectral variations seen in measurement data and uses 30 validated simulation models to find trends in these uncertainties. These trends are analyzed to observe 31 how each parameter can contribute to the overall uncertainty of UF 6 spectral measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Two-phase flow measurements and observations in a cooling panel of the reactor cavity cooling system

The analysis of the flow behavior within a cooling panel of a scaled water-cooled reactor cavity cooling system has been performed supported by high-resolution measurements of the void fraction at the risers’ outlet. The results have shown a stable, symmetric distribution of the flow through the risers during subcooled boiling conditions, characterized by small, slow bubbles with average void fraction lower than 0.3 for all risers. As the temperature increased to saturation, flow distribution appeared strongly asymmetrical and unstable, with peaks of void fraction as high as 0.9. Large, faster bubbles were observed during these peaks, immediately followed by flow of subcooled liquid water. Flow stagnation and inversion has also been observed. The high resolution void fraction results, complemented by the measurements of the coolant flow rate and temperatures, not only have further described the behavior of the system under hypothetical accident conditions, but represent a unique set of data to support the validation of system-level codes, and more advanced computational tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Chemical insights into ethyl acetate flames from experiment and kinetic modeling: Laminar burning velocity, speciation and NO$_x$ emission

Oxygenated fuels, such as alcohols, ethers, and esters, are promising alternatives to conventional fuels. These fuels can help reduce detrimental emissions like carbon monoxide and unburned hydrocarbons and enhance octane ratings. Among these oxygenates, ethyl acetate (EA), a small alkyl ester sourced from biomass, emerges as a clean, promising energy carrier. It serves as a surrogate fuel to facilitate investigations into the combustion behaviours of biodiesel. Despite its importance, the literature knowledge of EA combustion characteristics is limited. Therefore, this study aims to broaden the knowledge of the combustion behaviour of this type of oxygenated fuel compound. In this study, we measured the laminar burning velocities of EA by employing a heat flux burner and a closed combustion vessel over the equivalence ratios of 0.7 – 1.7, pressures of 1 – 10 bar and temperatures ranging from 353 – 423 K. Further, we also measured the NOx emissions in exhaust gas of the premixed flames fueled by EA/air for the first time over the equivalence ratio of 0.8 – 1.2. Additionally, we employed a non-premixed counterflow flame setup for extensive characterisation of species and their concentration under diverse conditions encompassing various strain rates and oxygen concentrations. Finally, we utilized these newly measured data to construct and validate a detailed kinetic model developed as part of this work. The newly developed model will help characterize the combustion properties of EA.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Computational studies of hydraulic stressors for biological performance assessment in a hydropower plant with Kaplan turbine

We report Hydropower is currently one of the preeminent sources of renewable energy in the United States and globally. Hydropower plants also have detrimental impacts on the environment and ecology, including direct impacts to anadromous fish populations. The computational fluid dynamics (CFD) – based Biological Performance Assessment (BioPA) toolset is used for biological evaluations of fish passage through hydropower plants. The hydraulic environment of a hydropower plant was evaluated using CFD coupled with discrete element method (DEM) simulations. The predicted flow field and particle collision rate were validated against the experimental data in a water flume that has an idealized hydroturbine distributor geometry. Flow simulations were conducted to evaluate the hydraulic stressors, such as nadir pressure, fluid shear, runner collision, in a physical scale in a hydropower plant with Kaplan turbine which are responsible for injury and mortality of fish in a downstream migration. The cumulative exposure probability for the nadir pressure and collision with turbine runner was found to decrease with increased discharge rate. The lowest discharge rate shows the higher value of cumulative shear exposure probability. The maximum value of collision velocity increases with increased discharge rate. We offer the conclusions that will help in understanding various hydraulic stressors for biological assessment for environmentally sustainable hydroturbine passage.

13 HYDRO ENERGY↗
DOE OSTI2021

Analytical solution for the cumulative wake of wind turbines in wind farms

This paper solves an approximate form of conservation of mass and momentum for a turbine in a wind farm array. The solution is a fairly simple explicit relationship that predicts the streamwise velocity distribution within a wind farm with an arbitrary layout. As this model is obtained by solving flow-governing equations directly for a turbine that is subject to upwind turbine wakes, no ad hoc superposition technique is needed to predict wind farm flows. A suite of large-eddy simulations (LES) of wind farm arrays is used to examine self-similarity as well as validity of the so-called conservation of momentum deficit for turbine wakes in wind farms. The simulations are performed with and without the presence of some specific turbines in the wind farm. This allows us to systematically study some of the assumptions made to develop the analytical model. A modified version of the conservation of momentum deficit is also proposed to provide slightly better results at short downwind distances, as well as in the far wake of turbines deep inside a wind farm. Model predictions are validated against the LES data for turbines in both full-wake and partial-wake conditions. While our results highlight the limitation in capturing the flow speed-up between adjacent turbine columns, the model is overall able to acceptably predict flow distributions for a moderately sized wind farm. Finally, the paper employs the new model to provide insights on the accuracy of common wake superposition methods.

17 WIND ENERGY↗
DOE OSTI2020

Molecular Dynamics Simulation of the Structures, Dynamics, and Aggregation of Dissolved Organic Matter

Dissolved organic matter (DOM) plays a significant role in the transport and transformation of pollutants in the aquatic environment. However, the experimental characterization of DOM has been limited mainly to bulk properties, and the molecular-level interactions among various components of DOM remain to be fully characterized. In this study, we use molecular dynamics (MD) simulations to probe the structural properties of model DOM systems at atomic detail. The 200 ns simulations, validated by available experimental data, reveal processes and mechanisms by which chemical species (cations, peptides, lipids, lignin, carbohydrates, and some low-molecular-weight aliphatic and aromatic compounds) aggregate to form complex DOM. The DOM aggregates are dynamic, consisting of a hydrophobic core and amphiphilic exterior. The lipid tails and other hydrophobic fragments form the core, with hydrophilic and amphiphilic groups exposed to water, making DOM accessible to both polar and nonpolar species. Thus, the lipid component acts as a nucleator, whereas cations (especially Ca 2+ ) connect the molecular fragments on the surface by coordinating with the O-containing functional groups of DOM. The structural details revealed here provide new insights including surface accessible atoms, overall assemblage, and interactions among the molecules of DOM for understanding the kinetics and mechanisms through which DOM interacts with metal and other contaminants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Particle-Scale Reduction Analysis of CuFeMnO 4 with Hydrogen for Chemical Looping Combustion

In this work CuFeMnO 4 (copper iron manganese oxide) oxygen carrier was characterized using DSC/TGA, in-situ XRD, and SEM-EDS to gain a clear elucidation of the chemical looping combustion (CLC) reactions with H2 which is a component of synthesis gas. A reaction model which best described the experimental TGA reaction data was selected from the analysis. Intrinsic rate of reaction parameters obtained from the study could be used for designing a reactor for scale-up. A model with combined contributions from nucleation and growth model and dimensional phase boundary model was found to exhibit the best correlation with the experimental data. The analysis provided validation of intrinsic reaction rates and other rate parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Reactor Scale-Up Methodology from Lab-Scale to Pilot-Scale Operations: Numerical Modeling of THFA Dehydration to DHP in Packed-Bed Reactors

This manuscript discusses developing a model-based scale-up methodology for a successful technology transfer of gas-phase catalytic reactors from a lab-scale to a pilot-scale operation. The manuscript demonstrates the methodology for gas-phase dehydration of tetrahydrofurfuryl alcohol (THFA) to dihydropyran (DHP) process over commercial Al 2 O 3 catalysts. A two-dimensional reactor model was developed using COMSOL Multiphysics 6.1 software. The model solves heat and mass transport equations in bed-scale and particle scales simultaneously. This powerful feature enables accurate prediction of the heat and mass transfer limitations in pilot-scale reactors, if any exists. Further, the model uses isothermal lab-scale experimental data to derive and validate the reaction chemistry, flow fields and boundary conditions. The model was then scaled-up to project conversion, selectivity, yield and formation rate of DHP in a pilot-scale reactor. The results highlight the complex nature of chemistry, heat, and mass transfer effects in lab-scale and pilot-scale reactors. The model results inform the possible operational limitations of the pilot-scale reactor and design strategies to improve process efficiency. Although the scale-up approach is explained through the THFA dehydration process, the methodology is applicable to any catalytic packed-bed reactor models for a successful process scale-up.

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

Hyperbolic Cooper-Pair Polaritons in Planar Graphene/Cuprate Plasmonic Cavities

Hyperbolic Cooper-pair polaritons (HCP) in cuprate superconductors are of fundamental interest due to their potential for providing insights into the nature of unconventional superconductivity. In this study, we critically assess an experimental approach using near-field imaging to probe HCP in Bi 2 Sr 2 CaCu 2 O 8+x (Bi-2212) in the presence of graphene surface plasmon polaritons (SPP). Our simulations show that inherently weak HCP features in the near-field can be strongly enhanced when coupled to graphene SPP in layered graphene/hexagonal boron nitride (hBN)/Bi-2212 heterostructures. This enhancement arises from our multilayered structures effectively acting as plasmonic cavities capable of altering collective modes of a layered superconductor by modifying its electromagnetic environment. The degree of enhancement can be selectively controlled by tuning the insulating spacer thickness with atomic precision. Finally, we verify the expected renormalization of room-temperature graphene SPP using near-field infrared imaging. Our modeling, augmented with data, attests to the validity of our approach for probing HCP modes in cuprate superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Use of Shallow Ice Core Measurements to Evaluate and Constrain 1980–1990 Global Reanalyses of Ice Sheet Precipitation Rates

Abstract Sea‐level rise (SLR) projections by Earth System Models (ESMs) depend on ice sheet surface mass balances. Accurate, global atmosphere reanalyses would be ideal for providing equilibrated ice sheet model initial conditions in fully coupled ESM simulations. Here we present the first evaluation of 1980–1990 global reanalysis precipitation over Greenland and Antarctica that uses independent observations of net accumulation rates derived from shallow ice cores. Precipitation distributions from both the European Centre for Medium‐Range Weather Forecast's Reanalysis (ERA5) and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are highly correlated with contemporaneous co‐located net accumulation rates from Greenland ( r 2 > 0.95) and West Antarctica ( r 2 > 0.7). Three other commonly used reanalyses (WFDE5, CRUNCEP, and GSWP3) exhibit significantly weaker correlations on one or both ice sheets. Our findings imply that ESMs should use ERA5 or MERRA‐2 in data‐forced simulations to validate ice sheet model dynamics and precondition firn for SLR projections.

Schneider, Adam↗
DOE OSTI2024

Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach ( R 2 = 0.51) outperformed the estimates obtained from Harmonized World Soil Database ( R 2 = 0.23) and SoilGrids 2.0 ( R 2 = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models.

58 GEOSCIENCES↗
DOE OSTI2025

Deep Roots Supply Reactivity and Enhance Silicate Weathering in the Bedrock Vadose Zone

In upland environments, roots commonly extend deep below soil into partially saturated bedrock. This Bedrock Vadose Zone (BVZ) has been shown to store and circulate water, host organic carbon respiration and serve as a critical source of rock-derived nutrients. However, the extent to which deep roots influence chemical weathering rates remains poorly understood. Here, we report 4 years of depth-resolved major ion chemistry over a 16-m thick BVZ hosting a deep rhizosphere in a catchment subject to a Mediterranean climate. These data allow development and validation of a reactive transport model (RTM), revealing that the timescales of water storage and drainage in the BVZ are sufficient to facilitate substantial chemical weathering of the shale bedrock. However, observed solute concentrations are only reproduced by the RTM when we explicitly include measured rates of CO 2(g) production meters below soil driven by the deeply rooted forest. By combining direct observations and a process-based RTM we conclude that the carbon respiration promoted by deep roots significantly enhances chemical weathering rates in the BVZ, constituting 43% ± 3% of total solute flux from the base of the BVZ to the water table.

Osorio‐Leon, Ivan D. [University of Illinois Urban↗