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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Solving Coupled Cluster Equations by the Newton Krylov Method

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A guide to the design of the virtual element methods for second- and fourth-order partial differential equations

Here we discuss the design and implementation details of two conforming virtual element methods for the numerical approximation of two partial differential equations that emerge in phase-field modeling of fracture propagation in elastic material. The two partial differential equations are: (i) a linear hyperbolic equation describing the momentum balance and (ii) a fourth-order elliptic equation modeling the damage of the material. Inspired by, we develop a new conforming VEM for the discretization of the two equations, which is implementation-friendly, i.e., different terms can be implemented by exploiting a single projection operator. We use C 0 and C 1 virtual elements for the second-and fourth-order partial differential equation, respectively. For both equations, we review the formulation of the virtual element approximation and discuss the details pertaining the implementation.

42 ENGINEERING↗
DOE OSTI2022

Assessing the Optimality of LinDist3Flow for Optimal Tap Selection of Step Voltage Regulators in Unbalanced Distribution Networks: Preprint

The adoption of distributed energy resources such as photovoltaics (PVs) has increased dramatically during the previous decade. The increased penetration of PVs into distribution networks (DNs) can cause voltage fluctuations that have to be mitigated. One of the key utility assets employed to this end are step-voltage regulators (SVRs). It is desirable to include tap selection of SVRs in optimal power flow (OPF) routines, a task that turns out to be challenging because the resultant OPF problem is nonconvex with added complexities stemming from accurate SVR modeling. While several convex relaxations based on semi-definite programming (SDP) have been presented in the literature for optimal tap selection, SDP based schemes do not scale well and are challenging to implement in large-scale planning or operational frameworks. This paper deals with the optimal tap selection (OPTS) problem for wye-connected SVRs using linear approximations of power flow equations. Specifically, the LinDist3Flow model is adopted and the effective SVR ratio is assumed to be continuous–enabling the formulation of a problem called LinDist3Flow-OPTS, which amounts to a linear program. The scalability and optimality gap of LinDist3Flow-OPTS are evaluated with respect to existing SDP-based and nonlinear programming techniques for optimal tap selection in three standard feeders, namely, the IEEE 13-bus, 123-bus, and 8500-node DNs. For all DNs considered, LinDist3Flow-OPTS achieves an optimality gap of approximately 1% or less while significantly lowering the computational burden.

linear approximations↗
DOE OSTI2020

Direct Optical Patterning of Quantum Dot Light-Emitting Diodes via In Situ Ligand Exchange

Precise patterning of quantum dot (QD) layers is an important prerequisite for fabricating QD light-emitting diode (QLED) displays and other optoelectronic devices. However, conventional patterning methods cannot simultaneously meet the stringent requirements of resolution, throughput, and uniformity of the pattern profile while maintaining a high photoluminescence quantum yield (PLQY) of the patterned QD layers. Here, a specially designed nanocrystal ink is introduced, "photopatternable emissive nanocrystals" (PENs), which satisfies these requirements. Photoacid generators in the PEN inks allow photoresist-free, high-resolution optical patterning of QDs through photochemical reactions and in situ ligand exchange in QD films. Various fluorescence and electroluminescence patterns with a feature size down to approximate to 1.5 mu m are demonstrated using red, green, and blue PEN inks. The patterned QD films maintain approximate to 75% of original PLQY and the electroluminescence characteristics of the patterned QLEDs are comparable to thopse of non-patterned control devices. The patterning mechanism is elucidated by in-depth investigation of the photochemical transformations of the photoacid generators and changes in the optical properties of the QDs at each patterning step. This advanced patterning method provides a new way for additive manufacturing of integrated optoelectronic devices using colloidal QDs.

Cho, Himchan↗
DOE OSTI2022

Oxygen–Chlorine Chemisorption Scaling for Seawater Electrolysis on Transition Metals: The Role of Redox

To clarify what controls species oxidation selectivity in seawater electrolysis, density functional theory (DFT) is used to identify chemisorption enthalpy trends and scaling relations for the simplest relevant adsorbates (O, Cl, and H) on relevant surfaces of 3d transition metals, as well as Pd and Pt, in face-centered-cubic and, if different, their ground-state crystal structures. Approximations are tested for electron exchange-correlation (XC) and van der Waals interactions to assess their ability to reproduce experimental adsorption enthalpies of H and O on Pt(111). The vdW-uncorrected generalized gradient approximation to XC of Perdew, Burke, and Ernzerhof (PBE) agrees most closely with experiments. Using DFT-PBE thereafter, it is determined that the O chemisorption enthalpy on this wide range of transition-metal surfaces is proportional to the sum of first and second atomic ionization energies, akin to a Born–Haber cycle for a redox reaction, indicating that metal redox activity controls O chemisorption strength. Then it is shown that the O and Cl chemisorption enthalpies are strongly correlated, suggesting that the transition metals considered will oxidize unselectively water and Cl – . This strong correlation appears also for crystal reduction potentials of binary oxides and chlorides, indicating a fundamental challenge for future seawater electrode materials design.

08 HYDROGEN↗
DOE OSTI2021

Response surface methodology guided adsorption and recovery of free fatty acids from oil using resin

The presence of free fatty acids interferes with the conversion of plant oils to biodiesel. In this work, four strong and weak base resins were evaluated for the removal of free fatty acids (FFA) from oil. Amberlite FPA 51 showed the highest adsorption capacity of FFA. A resin concentration above 3% could enable a higher percentage FFA adsorption. The adsorption process fitted a pseudo-first-order kinetic model and achieved equilibrium in approximately 8 h. A full factorial design was used to optimize the resin and FFA concentrations at a fixed temperature (40°C). A ratio of resin to fatty acid concentrations above 1.875 was sufficient for 70% adsorption and the amount adsorbed continued to increase with further added resin. A two-step washing of resin using hexane and ethanol recovered approximately 67.55%±4.05% of the initially added fatty acid. The resin that was used was regenerated with 5% NaOH and re-used for a minimum of three consecutive cycles. However, the adsorption capacity diminished to 75% of the initial cycle in cycles 2 and 3. Thus, the work presents a resin-based process for deacidification of oil to reduce fatty acid content of oil for biodiesel production.

09 BIOMASS FUELS↗
DOE OSTI2020

Au x Pd (300‐ x ) Alloy Nanoparticles for the Oxygen Reduction Reaction in Alkaline Media

Abstract Au x Pd (300‐ x ) nanoalloys were prepared using a dendrimer‐templating method and their electrocatalytic efficiencies towards the oxygen reduction reaction (ORR) were analyzed in alkaline media. The composition‐dependent PdO x reduction potentials of the Au x Pd (300‐ x ) nanoalloys were correlated to the oxygen‐binding (O‐binding) energy of the catalysts. Multiple PdO x reduction peaks were present for the Au 150 Pd 150 and Au 50 Pd 250 catalysts. These peaks were assigned to the alloyed and bulk‐like Pd active sites on the alloyed surface. The results described here are significant because modulation of the O‐binding energies improved the ORR activity on the nanoalloys. Specifically, a linear trend in O‐binding energies of Au x Pd (300‐ x ) dendrimers (DENs) as a function of Pd composition resulted in a volcano‐shaped trend in ORR activity. The optimal O‐binding energy of Au 50 Pd 250 resulted in an approximate 89 mV shift in PdO x reduction relative to Pd 300 and a shift of approximately 76 mV in ORR peak potential.

Trindell, Jamie A.↗
DOE OSTI2021

Finding Liebig’s law of the minimum

Abstract Liebig’s law of the minimum (LLM) is often used to interpret empirical biological growth data and model multiple substrates co‐limited growth. However, its mechanistic foundation is rarely discussed, even though its validity has been questioned since its introduction in the 1820s. Here we first show that LLM is a crude approximation of the law of mass action, the state of art theory of biochemical reactions, and the LLM model is less accurate than two other approximations of the law of mass action: the synthesizing unit model and the additive model. We corroborate this conclusion using empirical data sets of algae and plants grown under two co‐limiting substrates. Based on our analysis, we show that when growth is modeled directly as a function of substrate uptake, the LLM model improperly restricts the organism to be of fixed elemental stoichiometry, making it incapable of consistently resolving biological adaptation, ecological evolution, and community assembly. When growth is modeled as a function of the cellular nutrient quota, the LLM model may obtain good results at the risk of incorrect model parameters as compared to those inferred from the more accurate synthesizing unit model. However, biogeochemical models that implement these three formulations are needed to evaluate which formulation is acceptably accurate and their impacts on predicted long‐term ecosystem dynamics. In particular, studies are needed that explore the extent to which parameter calibration can rescue model performance when the mechanistic representation of a biogeochemical process is known to be deficient.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Transference Number

The performance of rechargeable batteries and other electrochemical systems depends on the rate at which the working ion (often a cation) is transported from one electrode to the other. The cation transference number is an important transport parameter that affects this rate. The purpose of this perspective is to distinguish between approximate and rigorous methods used in the literature to measure the transference number. We emphasize the fact that this parameter is dependent on the reference frame used in the analysis; care must be taken when comparing values obtained from different sources to account for differences in reference frames. We present data obtained from a well‐characterized electrolyte. We compare rigorously determined transference numbers in two reference frames with values obtained by an approximate method. We conclude with a qualitative discussion of the relationship between the transference number and salt concentration gradients that are obtained when current is drawn through a battery.

Gao, Kevin W.↗
DOE OSTI2023

Optimal Strategies for Hybrid Battery‐Storage Systems Design

As stationary hybrid energy‐storage systems (HESS) for power systems applications have recently drawn interest due to their enhanced performance and decreasing cost, developing systematic approaches for HESS design while considering controls is gaining traction. Herein, a method is presented to optimally design hybrid battery storage by proposing a mathematical modeling framework, formulated as a mixed integer linear programming model. The optimization is capable of handling multiple subsystems of batteries, considering their economic and technological performance. Decisions involve sizing of the batteries, optimal temporal and strategic dispatch to end uses, and energy sources for charging each battery. The applicability of the model is tested on four case studies for three battery chemistries representing distinct objectives: high‐power, high‐energy, and second life. Compared to traditionally designed battery storage with a homogeneous battery, optimally designed hybrid systems can save 12%–26% of system costs, depending on the nature of the dispatch profile. Findings point to design preference toward the second life battery supplemented with some high‐power or high‐energy battery capacity, or both. With the utilized electricity price structure, customers can experience approximately 10%–35% reduction in their bills.

Koleva, Mariya↗
DOE OSTI2025

Compactly‐Supported Nonstationary Kernels for Computing Exact Gaussian Processes on Big Data

The Gaussian process (GP) is a widely used method for analyzing large-scale data sets, including spatio-temporal measurements of nonlinear processes that are now commonplace in the environmental sciences. Traditional implementations of GPs involve stationary kernels (also termed covariance functions) that limit their flexibility, and exact methods for inference that prevent application to data sets with more than about 10,000 points. Modern approaches to address stationarity assumptions generally fail to accommodate large data sets, while all attempts to address scalability focus on approximating the Gaussian likelihood, which can involve subjectivity and lead to inaccuracies. In this work, we explicitly derive an alternative kernel that can discover and encode both sparsity and nonstationarity. We embed the kernel within a fully Bayesian GP model and leverage high-performance computing resources to enable the analysis of massive data sets. We demonstrate the favorable performance of our novel kernel relative to existing exact and approximate GP methods across a variety of synthetic data examples. Furthermore, we conduct space–time prediction based on more than 1 million measurements of daily maximum temperature and verify that our results outperform state-of-the-art methods in the Earth sciences. More broadly, having access to exact GPs that use ultra-scalable, sparsity-discovering, nonstationary kernels allows GP methods to truly compete with a wide variety of machine learning methods.

Gaussian processes↗
DOE OSTI2021

Sealing of fractures in a representative CO 2 reservoir caprock by migration of fines

The impact of fines migration on fracture transmissivity reduction was investigated by injecting a brine solution containing a suspension of 0.1 wt. % kaolinite particles with a mean particle size distribution of 9.6 µm through fractured shale core samples. The fractures had apertures estimated to be approximately 100 µm. A mass balance approach was used to determine the quantity of kaolinite that was deposited within the fractures (influent – effluent = amount deposited in fractures). Large fractions (44 to 90%) of the suspended kaolinite pumped through the fractures were deposited within the fractures. Based on fracture volumes estimated with X-ray computed tomography, it was determined that approximately 10 to 17% of the fracture volume was filled with kaolinite at the point when flow was completely restricted. These results indicate that 100 µm fractures in CO 2 reservoir caprocks could be sealed within hours if the brines passing through the fractures contain a proportional volume of particulates to the tests performed in this laboratory study.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Effect of XC functionals and dispersion corrections on the DFT‐computed structural and vibrational properties of SrCl 2 –NaCl and ZrF 4 –LiF

Density functional theory (DFT) calculations were performed to examine the impact of exchange–correlation (XC) functionals and van der Waals corrections (specifically the D3 method) on the structural and vibrational properties of the SrCl 2 –NaCl and ZrF 4 –LiF salt systems. Multiple XC functionals, including the local density approximation (LDA), the generalized gradient approximation using the Perdew–Burke–Ernzerhof (PBE) model, and its modified form suitable for solids (PBEsol), the dispersion-corrected PBE-D3 and PBEsol-D3, were considered. Of these functionals, LDA was found to exhibit the highest degree of error, while PBEsol and PBE-D3 displayed the least error. Underestimated lattice parameters compared with experimental values were observed to result in higher force constants, leading to an overprediction of vibrational frequencies. Conversely, an overestimation of lattice parameters was associated with lower vibrational frequencies. The methodology presented in this study yielded results that are in good agreement with experiment, irrespective of the method (finite differences vs. density functional perturbation theory) employed for calculating infrared and Raman spectra. It was further demonstrated that for alkali halides with weak Raman scattering, utilizing a supercell constructed from primitive cells better predicts Raman features than does the use of conventional cells.

Raman↗
DOE OSTI2025

Spatially Resolved Raman Spectroscopy of Thin Carbon Interphase in SiC Ceramic Matrix Composites

A dedicated analysis method is presented to extract the Raman spectrum of an interphase layer thinner than the laser spot size. We focused on spatial correlations between the contrast of optical micrographs and Raman hyperspectral data to predict the constituents of the mixed spectra measured near the interphase. By employing a mapping step size of 0.1 μm, the Raman spectrum of approximately 0.3-μm-thick carbon interphase in a SiC fiber-reinforced SiC matrix composite was extracted from data acquired with a theoretical spot size of about 0.7 μm. Notably, conventional chemometrics procedures were unable to isolate the interphase signal, instead producing a spectrum representing a mixture of interphase and matrix. This study used another composite with approximately 0.9-μm-thick interphase to validate the analysis method, enabling direct measurement of the interphase spectrum. The proposed Raman analysis method has advantages in specimen volume and turnaround time compared to traditional characterization methods, such as transmission electron microscopy. In conclusion, this study also evaluates the applicability of the analysis method to different composite materials and identifies key requirements of the measurements, including the ratio of interphase thickness to spot size and the homogeneity of the surrounding matrix.

ceramic matrix composite↗
DOE OSTI2023

Mitigating the allergenicity of peanut allergen Ara h 1 by cold atmospheric pressure argon plasma jet

Abstract BACKGROUND Peanut allergy is recognized as a major food allergy that triggers severe and even fatal symptoms. Avoidance of peanuts in the diet is the main option for current safety management. Processing techniques reducing peanut allergenicity are required to develop other options. Cold plasma is currently considered as a novel non‐thermal approach to alter protein structure and has the potential to alleviate immunoreactivity of protein allergen. RESULTS The application of a cold argon plasma jet to peanut protein extract could reduce the amount of a 64 kDa protein band corresponding to a major peanut allergen Ara h 1 using sodium dodecyl sulfate–polyacrylamide gel electrophoresis, but the overall protein size distribution did not change significantly. A decrease in peanut protein solubility was a possible cause that led to the loss of protein content in the soluble fraction. Immunoblotting and enzyme‐linked immunosorbent assay elucidated that the immunoreactivity of Ara h 1 was significantly decreased with the time treated with plasma. Ara h 1 antigenicity reduced by 38% after five scans (approximately 3 min) of cold argon plasma jet treatment, and the reduction was up to 66% after approximately 15 min of treatment. CONCLUSION The results indicate that cold argon plasma jet treatment could be a suitable platform for alleviating the immunoreactivity of peanut protein. This work demonstrates an efficient, compact, and rapid platform for mitigating the allergenicity of peanuts, and shows great potential for the plasma platform as a non‐thermal technique in the food industry. © 2023 Society of Chemical Industry.

Hsu, Fu‐Chiun↗
DOE OSTI2025

Comparison of removal and spatial mark‐resight models for estimating wild pig density

Density estimation is critical to effectively manage invasive species and elucidate areas of highest concern. For wild pigs (Sus scrofa), the ability to estimate density is complicated because of their variable home range sizes and social structure. Common methods for estimating density (e.g., mark-recapture) may be unsuitable in management applications because additional data needs to be collected before and after management. Removal models offer a suitable alternative to estimate density changes following management and can be applied broadly across areas where management of wild pigs is ongoing. We collected wild pig removal and camera trap data from 25 private properties ranging in size from approximately 0.5 km 2 to 95 km 2 across 3 ecoregions in South Carolina, USA, from 2020–2023. We compared factors affecting consistency and precision of property-level density estimates between removal and spatial mark-resight (SMR) models. In general, excluding 1 large outlier, density estimates from removal models were between 0.60 and 15.85 wild pigs/km 2 (median = 5.34) with a median coefficient of variation (CV) of 0.76 and 95% confidence intervals for the CV between 0.70 and 0.94. Similarly, excluding 1 large outlier, density estimates from SMR were between 0.22 and 30.97 wild pigs/km 2 (median = 5.48) with a median CV of 0.39 and 95% confidence intervals for the CV between 0.38 and 1.20. We found the precision of removal models was affected primarily by the number of wild pigs dispatched in the removal period (3 months) and the ecoregion in which they were removed. None of the covariates, including the number of recaptures (a corresponding measure of sample size), influenced precision of the SMR models, although recaptures did influence the density estimates. At the individual property level, density estimates from our 2 estimators were dissimilar from each other in approximately 80% of instances, although none of the covariates we examined influenced dissimilarity. Our results provide unique insight into how sample size affects density estimates using 2 common methods and into novel SMR models that incorporate both marked and unmarked detections. In addition, the density estimates in this study can be used as a reference for wild pig densities in common land cover types throughout the southeastern United States.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Uncertainty analysis of correlated parameters in automated reaction mechanism generation

Abstract Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values. Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow.

Gao, Connie W.↗
DOE OSTI2020

Application of radiochromic gel dosimetry to commissioning of a megavoltage research linear accelerator for small–field animal irradiation studies

To develop and implement an efficient and accurate commissioning procedure for small-field static beam animal irradiation studies on an MV research linear accelerator (Linatron-M9) using radiochromic gel dosimetry. The research linear accelerator (Linatron-M9) is a 9 MV linac with a static fixed collimator opening of 5.08 cm diameter. Lead collimators were manually placed to create smaller fields of 2 × 2 cm 2 , 1 × 1 cm 2 , and 0.5 × 0.5 cm 2 . Relative dosimetry measurements were performed, including profiles, percent depth dose (PDD) curves, beam divergence, and relative output factors using various dosimetry tools, including a small volume ionization chamber (A14), GAFCHROMIC™ EBT3 film, and Clearview gel dosimeters. The gel dosimeter was used to provide a 3D volumetric reference of the irradiated fields. The Linatron profiles and relative output factors were extracted at a reference depth of 2 cm with the output factor measured relative to the 2 × 2 cm 2 reference field. Absolute dosimetry was performed using A14 ionization chamber measurements, which were verified using a national standards laboratory remote dosimetry service. Absolute dosimetry measurements were confirmed within 1.4% (k = 2, 95% confidence = 5%). The relative output factor of the small fields measured with films and gels agreed with a maximum relative percent error difference between the two methods of 1.1 % for the 1 × 1 cm 2 field and 4.3 % for the 0.5 × 0.5 cm 2 field. These relative errors were primarily due to the variability in the collimator positioning. The measured beam profiles demonstrated excellent agreement for beam size (measured as FWHM), within approximately 0.8 mm (or less). Film measurements were more accurate in the penumbra region due to the film’s finer resolution compared with the gel dosimeter. Following the van Dyk criteria, the PDD values of the film and gel measurements agree within 11% in the buildup region starting from 0.5 cm depth and within 2.6 % beyond maximum dose and into the fall-off region for depths up to 5 cm. The 2D beam profile isodose lines agree within 0.5 mm in all regions for the 0.5 × 0.5 cm 2 and the 1 × 1 cm 2 fields and within 1 mm for the larger field of 2 × 2 cm 2 . The 2D PDD curves agree within approximately 2% of the maximum in the typical therapy region (1–4 cm) for the 1 × 1 cm 2 and 2 × 2 cm 2 and within 5% for the 0.5 × 0.5 cm 2 field. This work provides a commissioning process to measure the beam characteristics of a fixed beam MV accelerator with detailed dosimetric evaluation for its implementation in megavoltage small animal irradiation studies. Radiochromic gel dosimeters are efficient small-field relative dosimetry tools providing 3D dose measurements allowing for full representation of dose, dosimeter misalignment corrections and high reproducibility with low inter-dosimeter variability. Altogether, radiochromic gels are valuable for fast, full relative dosimetry commissioning in comparison to films for application in high-energy small-field animal irradiation studies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗