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Results for “Coefficient of performance”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ettringite hysteresis under sorption from molecular simulations

Highlights: • Hybrid Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) simulations are performed. • Anisotropic large strain coefficient of crystal shrinkage of ettringite is provided. • Entropy increases when ettringite converts into metaettringite. • Sorption-induced stress may lead to columns collapse upon drying. • H-bonds involving OH groups in columns play a role in hysteresis. The response of ettringite under sorption is critical for the utilization of this mineral as an energy storage material and to understand the concrete durability problems associated with ettringite formation. We report molecular simulations directly sampling the osmotic ensemble to understand the sorption processes and structural changes in ettringite-metaettringite transition. Desorption branch and sorption-induced volume changes from simulations agree with the experiments. The structural changes at low relative humidity are analyzed, revealing for the first time details of metaettringite structure. The reversibility under sorption observed experimentally is captured by simulation in the ettringite domain. The reversibility in the conversion of metaettringite into ettringite is not captured by the direct simulations in the osmotic ensemble due to the large volume changes associated with ettringite desorption. Finally, we discuss the role of hydrogen bonds on the hysteresis in ettringite.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Collisional Excitation of HCN by CO to Refine the Modeling of Cometary Comae

Here, we present the first dataset of collisional (de)-excitation rate coefficients of HCN induced by CO, one of the main perturbing gases in cometary atmospheres. The dataset spans the temperature range of 5–50 K. It includes both state-to-state rate coefficients involving the lowest ten and nine rotational levels of HCN and CO, respectively, and the so-called “thermalized” rate coefficients over the rotational population of CO at each kinetic temperature. The derivation of these coefficients exploited the good performance of the statistical adiabatic channel model (SACM) on top of an accurate interaction potential computed at the CCSD(T)-F12b/CBS level of theory. The reliability of the SACM approach was validated by comparison with full quantum calculations restricted at the lowest total angular momentum of the system. These results provide essential input to accurately model the distribution among the rotational energy levels and the abundance of HCN in cometary atmospheres, accounting for deviations from local thermodynamic equilibrium that typically occurs in such environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning

Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. Here, we address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Thermal Management System for an Electric Machine With Additively Manufactured Hollow Conductors With Integrated Heat Pipes

Here, this paper discusses steps taken to develop a novel thermal management system for an aircraft propulsion electric machine containing additively manufactured coils integrated with heat pipes aimed at boosting its specific power. Experimental setups are used to size and characterize heat pipes for the application and 3D thermal finite element analysis is used to determine optimum heat transfer coefficient of the convective boundaries. For some of the convective boundaries, fin-based surface area enhancement is required to reach a target combined overall heat transfer coefficient and surface area performance (UA). This enhancement is worked out using a combination of the Engineering Equation Solver tool and 3D thermal FEA. The thermal management system's UA, and by extension its specific power, sensitivity to coolant temperature is explored. Temperature distribution plots of optimized machine components are also presented and discussed. Lastly, additional heat pipe testing is carried out to study its maximum heat transfer capability's sensitivity to condenser coolant temperature and configuration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Improved Gamma-Ray Point Source Quantification in Three Dimensions by Modeling Attenuation in the Scene

We discuss using a series of detector measurements taken at different locations to localize a source of radiation as a well-studied problem. The source of radiation is sometimes constrained to a single point-like source, in which case the location of the point source can be found using techniques such as maximum likelihood. Recent advancements have shown the ability to locate point sources in 2D and even 3D, but few have studied the effect of intervening material on the problem. In this work we examine gamma-ray data taken from a freely moving system and develop voxelized 3-D models of the scene using data from the onboard LiDAR. Ray casting is used to compute the distance each gamma ray travels through the scene material, which is then used to calculate attenuation assuming a single attenuation coefficient for solids within the geometry. Parameter estimation using maximum likelihood is performed to simultaneously find the attenuation coefficient, source activity, and source position that best match the data. Using a simulation, we validate the ability of this method to reconstruct the true location and activity of a source, along with the true attenuation coefficient of the structure it is inside, and then we apply the method to measured data with sources and find good agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Impacts of Guanidine Degradation Products on Next Generation Solvent (NGS) Caustic Side Solvent Extraction (CSSX) Processing

Savannah River National Laboratory researchers have been requested to perform testing to assess the potential for build-up of guanidine degradation compounds in the Next Generation Solvent Caustic Side Solvent Extraction process. Testing was also requested to determine the impact of guanidine degradation compounds [3,7-dimethyloctylamine (iDA) and Bis-N,N’-(3,7-dimethyloctyl)urea (DiDU)] on cesium behavior in the flowsheet. Twelve partitioning experiments were performed to quantify the partitioning coefficient of identified guanidine degradation compounds in various organic-aqueous mixtures. Six Extraction, Scrub, and Strip (ESS) experiments were performed to quantify the impact of degradation products on cesium behavior.

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

Assessment and Improvement of the SST-Gamma Transition Model in Nalu-Wind

We conduct laminar–turbulent boundary-layer transition simulations using a local correlation-based transition model for two-dimensional incompressible flow and present enhancements to improve the accuracy of transition predictions. Menter’s Galilean-invariant 𝛾 transition model is implemented in the incompressible, unstructured-grid flow solver Nalu-Wind and is validated against experimental data and results from NASA’s flow solvers. The test cases of the AIAA Transition Prediction and Modeling Workshop are investigated, namely, the T3A/T3B flat plates and the NLF(1)-0416 and S809 airfoils. Based on the results, best practices for transition simulations, particularly for an unstructured-grid flow solver, are identified. Additional airfoil simulations are conducted for two wind turbine airfoils, S822 at Reynolds numbers of 𝑂⁡(10 5 ) and DU00-W-212 at Reynolds numbers of 𝑂⁡(10 7 ), to assess the model at low and high Reynolds numbers. Furthermore, through this work, we propose several approaches to enhance transition simulations, including 1) enforcing positivity of the implicit operator for the source terms of the transition model, 2) employing a constant turbulence intensity in stationary external flow simulations, and 3) recommending meshing for unstructured-grid flow solvers. Finally, we provide detailed documentation of the validation and data for the canonical cases to the transition modeling community.

17 WIND ENERGY↗
DOE OSTI2023

The Role of the Anion in Imidazolium-Based Ionic Liquids for Fuel and Terpenes Processing

The potentialities of methylimidazolium-based ionic liquids (ILs) as solvents were evaluated for some relevant separation problems—terpene fractionation and fuel processing—studying selectivities, capacities, and solvent performance indices. The activity coefficients at infinite dilution of the solute (1) in the IL (3), γ13∞, of 52 organic solutes were measured by inverse gas chromatography over a temperature range of 333.2–453.2 K. The selected ILs are 1-butyl-3-methylimidazolium hexafluorophosphate, [C4mim][PF6], and the equimolar mixture of [C4mim][PF6] and 1-butyl-3-methylimidazolium chloride, [C4mim]Cl. Generally, low polar solutes follow γ1,C4mimCl∞ > γ1,C4mimPF6+C4mimCl∞ > γ1,C4mimPF6∞ while the opposite behavior is observed for alcohols and water. For citrus essential oil deterpenation, the results suggest that cations with long alkyl chains, such as C12mim+, promote capacity, while selectivity depends on the solute polarity. Promising results were obtained for the separation of several model mixtures relevant to fuel industries using the equimolar mixture of [C4mim][PF6] and [C4mim]Cl. This work demonstrates the importance of tailoring the polarity of the solvents, suggesting the use of ILs with mixed anions as alternative solvents for the removal of aliphatic hydrocarbons and contaminants from fuels.

Zambom, Aline (ORCID:0000000324512716)↗
DOE OSTI2021

Technoeconomic Benefits of Film-Forming Amine Products Applied to Steam Surface Condensers

In a conventional Rankine cycle, the majority of power plants employ surface condensers that use pumped cooling water to reject heat from the cycle. In such cases, heat rejection occurs in a shell and tube heat exchanger by film-wise condensation of low-pressure steam on stainless steel, titanium, brass, or copper-nickel tubing. To improve the thermal performance of steam surface condensers, a replenishable film-forming substance (FFS) can be ap-plied to the condenser tubing to promote efficient dropwise condensation. Conventionally, film-forming amine prod-uct (FFAP) coatings protect boiler surfaces from oxidative corrosion, which substantially reduces the operation and maintenance costs. To quantify the technical and economic benefits of FFAP coatings applied to condenser tubing due to the promotion of dropwise condensation, a thermal resistance network model was established. Using a rep-resentative steam surface condenser, the improvements in thermal performance (overall heat transfer coefficient) and process parameters (net plant efficiency, cooling water flowrate, and turbine backpressure) were determined due to the enhancement in the condensation heat transfer coefficient. Experimentally measured condensation heat transfer coefficients for common condenser materials were compared with the modeling results and were found to be within attainable bounds. Lastly, the trend in total heat exchanger cost reduction is generalized to understand the trade-off between reduced surface area for heat rejection and increase in coating application costs for a re-plenishable coating system.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties

Anisotropic metal nanostructures exhibit polarization-dependent light scattering, a property which has been widely studied and exploited to determine orientations of subwavelength structures using far-field microscopy. Here we explore the use of variational autoencoders (VAEs) to determine the geometries of gold nanorods (NRs) such as in-plane orientation and aspect ratio under linearly polarized dark-field illumination in an optical microscope. We enforce a shared latent space to connect two VAEs trained separately with polarized dark-field scattering spectra and electron microscopy images and achieve image prediction (shape, orientation, and size) of Au NRs using only polarized dark-field scattering spectra. We determine the geometrical parameters of orientational angle and aspect ratio quantitatively via both our dual-VAE and physics-based analysis on the input scattering spectra. We show that orientational angle prediction by dual-VAE performs well with only a small (~300 particle) training set, yielding a mean absolute error (MAE) of 14.4° and a concordance correlation coefficient (CCC) of 0.95. This performance is only marginally worse than the physics-based cos(2?) fitting approach between the scattering intensity and the polarizing angle, which achieves MAE of 8.78° and CCC of 0.99. Aspect ratio determination is also comparable for the dual-VAE and physics-based fitting comparison (MAE of 0.21 vs. 0.23 and CCC of 0.53 vs. 0.68). Here, this dual encoder-decoder architecture effectively exploits the structure-property relationships of plasmonic nanostructures to construct a cross-modal machine learning (ML) approach, providing a pathway to employ ML approaches to address other structure-property relationships in materials science.

Dark-field scattering↗
DOE OSTI2023

Investigation into Na and Cs activity coefficients in high salt solutions to support Cs removal in Hanford tank waste

The treatment of Hanford tank waste is one of the most technically challenging environmental cleanup activities for the U.S. Department of Energy to date. To expedite the processing of liquid waste stored in underground tanks in southeastern Washington state, it is necessary to remove the significant dose contributor, 137 Cs. Toward this effort, ion exchange with crystalline silicotitanate (CST) has been employed as part of the Tank Side Cesium Removal system. The model used to predict Cs exchange onto CST was developed using activity coefficients calculated from the Bromley equation. A series of batch contact tests that varied in [Na] were conducted to look at the impact of Na concentration on Cs distribution. Experimental distribution ratios ( K d ) were compared to the distribution ratios predicted using three different activity coefficient models: (1) commercially available HSC software, (2) the Bromley equation, and (3) a simplified approach adapted from Marcos-Arroyo et al. Ultimately, the Bromley method underpredicted the effect of ionic strength on the Na activity coefficient ( γ Na+ ), HSC overestimated the impact of ionic strength on the expected performance due to the Cs activity coefficient ( γ Cs+ ), but the simplified approach predicted the experimental K d values quite well in a binary matrix. In conclusion, expansion of this approach in complex matrices is necessary for application to Hanford tank waste.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhanced thermoelectric performance of PbSe-graphene nanocomposite manufactured with acoustic cavitation induced defects

An order of magnitude rise in the thermoelectric (TE) performance of the PbSe, a scalable and easy-to-manufacture TE material, has been achieved by incorporating reduced graphene oxide (Gr) nanoplatelets in a PbSe/PbSeO 3 heterostructure formed by acoustic cavitation-assisted oxidation. The fabricated Gr/PbSe/PbSeO 3 nanocomposites exhibit high TE performance with an exceptionally high Seebeck coefficient coupled with low thermal conductivity. The variation in the Seebeck coefficient has been attributed to a reduction in charge carrier mobility due to the ferroelectric polarization effect. Furthermore, the increase in electrical resistivity is minimized by adding graphene. At an optimal weight fraction (0.2 wt%), graphene nano-inclusions lead to superior Seebeck coefficient values as high as ~2000 μV/K at ~500 K, providing high overall TE performance. This study shows substantial changes in the TE properties of PbSe through the incorporation of graphene and PbSeO 3 . The understanding and methodology developed in this study can be exploited for the scalable manufacturing of high-performance TE materials.

Structure-property correlations↗
DOE OSTI2021

PyOECP: A flexible open-source software for the measurement and modeling of the dielectric spectrum from the reflection coefficient

We present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on two different models, including the capacitance model and the antenna model. The software library contains dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) are only required so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, the electrode polarization, which is a common measurement artifact for interpreting the dielectric spectra, can be incorporated in the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem) is also included.

Yoon, Tae-Jun↗
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 OSTI2021

Updates to the n+ 63,65 Cu Angular Distributions [Slides]

The performance of the benchmark suite is very sensitive to changes in the angular distribution data. The quasi-differential measurements performed by Blain et al at RPI provide a valuable constraint. Furthermore, ENDF/B-VIII.0 disagrees with their measurements consistently at 300 keV (the transition between RRR and high energy). The next step is to conservatively adjust the Legendre coefficients near 300 keV, validating performance against both critical benchmarks and the RPI quasi-differential measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Tuning Thermal Stability through Dopant Size in Chemically Doped DPP–Thiophene Polymers

Molecular doping of conjugated polymers (CPs) is a key strategy for improving the performance of organic electronics devices, particularly thermoelectrics. Doped donor–acceptor (D–A) conjugated polymers, characterized by a tunable energy gap between the Fermi level and the transport band, show great promise in achieving high electrical conductivity (σ) while preserving a favorable Seebeck coefficient (S). Despite the promising performance enhancement of chemically doped D–A polymers, their thermal stability remains largely underexplored, a crucial consideration for the long-term operation of organic thermoelectric devices. In this study, we investigated the dopant size-dependent thermal stability of a diketopyrrolopyrrole-thiophene (DPP-T) D–A copolymer, utilizing two p-dopants: 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F 4 TCNQ) and Mo(tfd-CO 2 Me) 3 . Temperature-dependent UV–vis–NIR spectroscopy revealed that DPP-T/F 4 TCNQ is more prone to dedoping under a high temperature thermal stress than DPP-T/Mo(tfd-CO 2 Me) 3 . Although the F 4 TCNQ doped polymer shows higher initial in-plane conductivity than its Mo(tfd-CO 2 Me) 3 counterpart, it undergoes a conductivity loss of more than an order of magnitude after annealing at 120 °C for 30 min. In contrast, the in-plane conductivity of DPP-T/Mo(tfd-CO 2 Me) 3 remains stable under the same thermal conditions. Thermogravimetric analysis ruled out dopant sublimation as a primary contributor to dedoping, leading us to attribute the conductivity loss in F 4 TCNQ-doped DPP-T to dopant phase separation and migration. This observation was further confirmed by X-ray scattering studies and nanoscale infrared microscopy and spectroscopy studies. This work could provide further insights into the thermal stability of doped conjugated polymers and suggests that incorporating bulkier dopants is an effective strategy to enhance the thermal robustness of doped DPP-type systems.

Conjugated polymers↗
DOE OSTI2021

Evaluation of U-net-based Image Segmentation Model to Digital Mammography

Detecting suspicious lesions in medical imaging is the important first step in computer-aided detection (CAD) systems. However, detecting abnormalities in breast tissue is difficult due to the lesion's varying size, shape, margin, and contrast with the background tissue. We focused on mass segmentation, a method that provides notable morphological features by outlining contours of masses. Accurate segmentation is crucial for correct diagnosis. Recent advancements in deep learning have improved object detection and segmentation, and these techniques are also being applied to medical imaging studies. We focused on U-net, which is a recently developed mass segmentation algorithm based on a fully convolutional network. The U-net architecture consists of (1) a contracting path to increase the resolution of the output and (2) a symmetric expanding path to better locate the region of interest. The performance of a U-net model was tested with 63 digital mammograms from INbreast, a publicly available database. We trained the model with images resized to 40x40 pixels and conducted 10-fold cross-validation to prevent overfitting. The model's performance with respect to breast density and the lesion's BI-RADS rating was also investigated. Dice coefficients (DC) were used as a performance measure to compare the predicted segmentation of the model with the ground truth. Logistic regression and an analysis of variance were performed to determine the significance of the DCs with regards to breast density and lesion behavior and to calculate the 95% confidence interval. The average DC was 0.80. The difference between DCs for BI-RADS 2 and 4c and for BI-RADS 2 and 5 were significant, suggesting that the model has more difficulty in segmenting benign lesions.

Cho, Priscilla↗