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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Shear wave elastography prior to transjugular intrahepatic portosystemic shunt may predict the decrease in hepatic vein pressure gradient

Background: Transjugular intrahepatic portosystemic shunt (TIPS) is a procedure used to treat portal hypertension complications. Our aim was to evaluate liver and spleen stiffness measurement (LSM and SSM, respectively) changes using acoustic radiation force impulse imaging (ARFI) in comparison to Child–Pugh scores for predicting hepatic venous pressure gradient (HVPG) decreases after TIPS implantation. Methods: This prospective study included 31 consecutive clinically significant portal hypertension patients with TIPS indication. All patients received LSM and SSM before TIPS, at baseline, 2 days (follow-up 1) and 6 weeks (follow-up 2) post-implantation. HVPG was performed during the TIPS procedure. Results: The mean decrease in HVPG after TIPS was 63%. LSM and SSM decreased significantly between baseline and follow-up 2 (p < 0.001 and p < 0.001, respectively). At baseline, follow-up 1 and follow-up 2, significant correlations were detected between mean SSM and mean HVPG (p = 0.026; p = 0.018; p = 0.002, respectively). HVPG decreased to ≤ 10 mmHg in 61% of patients for which LSM, SSM, and Child–Pugh score were predictors (p = 0.033, p = 0.002 and p = 0.030, respectively). The area under the curve (AUC) for LSM, SSM, and Child–Pugh was 0.88, 0.90, and 0.84, respectively, with close sensitivity and specificity. SSM had the highest diagnostic accuracy for predicting an HVPG decrease to ≤ 10 mmHg in comparison to LSM and Child–Pugh score. Conclusion: Spleen stiffness is superior to liver stiffness and Child–Pugh score as a non-invasive surveillance tool for evaluating patients with clinically significant portal hypertension (HVPG ≥ 10 mmHg) prior to TIPS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

In-situ Investigation of Vanadium Ion Transport in Redox Flow Battery

We will show a new method to differentiate the vanadium transport from concentration gradient and that from electric field. Flow batteries with vanadium and iron redox couples as the electro-active species were employed to investigate the transport behavior of vanadium ions in the presence of electric field. It was shown that electric field accelerated the positive-to-negative and reduced the negative-to-positive vanadium ions transport in charge process and affected the vanadium ions transport in an opposite way in discharge process. In addition, a method was designed to differentiate the concentration gradient-driven vanadium ions diffusion and electric field-driven vanadium ions migration. Simplified mathematical model was established to simulate the vanadium ions transport in real charge-discharge operation of flow battery. The concentration gradient diffusion coefficients and electric-migration coefficients of V2+, V3+, VO2+, and VO2+ across Nafion membrane were obtained by fitting the experimental data.

25 ENERGY STORAGE↗
NASA NTRS1978

Inviscid, nonadiabatic flow fields over blunt, sonic corner bodies for outer planet entry conditions by a method of integral relations

An investigation has been made into the ability of a method of integral relations to calculate inviscid zero degree angle of attack, radiative heating distributions over blunt, sonic corner bodies for some representative outer planet entry conditions is investigated. Comparisons have been made with a more detailed numerical method, a time asymptotic technique, using the same equilibrium chemistry and radiation transport subroutines. An effort to produce a second order approximation (two-strip) method of integral relations code to aid in this investigation is also described and a modified two-strip routine is presented. Results indicate that the one-strip method of integral relations cannot be used to obtain accurate estimates of the radiative heating distribution because of its inability to resolve thermal gradients near the wall. The two-strip method can sometimes be used to improve these estimates; however, the two-strip method has only a small range of conditions over which it will yield significant improvement over the one-strip method.

Gnoffo, P. A.↗
NASA NTRS2018

Solution of Ordinary Differential Equations in Gradient-Based Multidisciplinary Design Optimization

A gradient-based approach to multidisciplinary design optimization enables efficient scalability to large numbers of design variables. However, the need for derivatives causes difficulties when integrating ordinary differential equations (ODEs) in models. To simplify this, we propose the use of the general linear methods framework, which unifies all Runge-Kutta and linear multistep methods. This approach enables rapid implementation of integration methods without the need to differentiate each one, even in a gradient-based optimization context. We also develop a new parallel time integration algorithm that enables vectorization across time steps. We present a set of benchmarking results using a stiff ODE, a non-stiff nonlinear ODE, and an orbital dynamics ODE, and compare integration methods. In a modular gradient-based multidisciplinary design optimization context, we find that the new parallel time integration algorithm with high-order implicit methods, especially Gauss-Legendre collocation, is the best choice for a broad range of problems.

Hwang, John T.↗
NASA NTRS2006

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Rai, Man Mohan↗
DOE OSTI2016

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Dot tracking methodology for background-oriented schlieren (BOS)

We propose here a dot tracking methodology for processing background-oriented schlieren (BOS) images. The technique improves the accuracy, precision and spatial resolution compared to conventional cross-correlation algorithms. Our methodology utilizes the prior information about the dot pattern such as the location, size and number of dots to provide near 100% yield even for high dot densities (20 dots/32 × 32 pixels) and is robust to image noise. Furthermore, we propose an improvement to the displacement estimation step in the tracking process, especially for noisy images, using a “correlation correction”, whereby we combine the spatial resolution benefit of the tracking method and the smoothing property of the correlation method to increase the dynamic range of the overall measurement process. We evaluate the performance of the method with synthetic BOS images of buoyancy-driven turbulence rendered using ray-tracing simulations, and experimental images of flow in the exit plane of a converging–diverging nozzle. The results show that the improved spatial resolution results in a better accuracy of the tracking method compared to correlation-based methods in regions with sharp displacement gradients, and the correlation correction step reduces the noise floor of the measurement, resulting in a fourfold improvement in the dynamic range.

42 ENGINEERING↗
DOE OSTI2018

High-Pressure and Temperature Dependence of Electronic, Magnetic, Elastic, Thermodynamic, and Transport Properties of Full-Heusler Alloys Co{sub 2}YIn (Y = Nb, Zr)

Structural, elastic, magnetic, and electronic properties of Co{sub 2}YIn (Y = Nb, Zr) have been investigated by means of the full-potential linearized augmented plane wave along with generalized gradient approximation + U/modified Becke-Johnson method in the stable Fm-3m phase. The optimized equilibrium lattice parameter in stable phase is 6.1744 Å for Co{sub 2}NbIn and 6.2824 Å for Co{sub 2}ZrIn. We have calculated the spin-resolved density of states for both the alloys to show that the ferromagnetic behavior of Co{sub 2}NbIn is due to the exchange splitting of Co and Nb atoms and due to Co and Zr in Co{sub 2}ZrIn. In spin-up configuration, there is absence of energy gap while an energy gap can be seen in spin-down state for both the alloys exhibiting a half-metallic character. Through the elastic constants and their discussions, we have predicted the elastic behavior of the materials. The quasi-harmonic approximations have been employed to study the pressure- and temperature-dependent thermodynamic properties. The Boltzmann theory is employed to investigate the electronic transport properties of these alloys. The defined material properties may pay as a prime stand for the application of material in spintronics and thermoelectrics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Investigation of Gravity Lanthanide Separation Chemistry

Lanthanides are common fission products and the ability to separate and quantify these elements is critical to rapid radiochemistry applications. Published lanthanide separations using Eichrom Ln Spec resin utilize an HCl gradient. Here it is shown that the efficacy and resolution of the separation is improved when a nitric acid gradient is used instead. The described method allows parallel processing of many samples in 1.5 hours followed by 60 minute counting for quantification of 9 isotopes of 7 lanthanide elements.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Design of an additively manufactured functionally graded material of 316 stainless steel and Ti-6Al-4V with Ni-20Cr, Cr, and V intermediate compositions

This study presents a method for designing a computationally informed gradient pathway to fabricate a functionally graded material (FGM) with terminal alloys of 316 stainless steel (SS316) and Ti-6Al-4V via directed energy deposition additive manufacturing with powder feedstock. In this work, the grading is accomplished through the introduction of intermediate elements and alloys (Ni-20Cr, Cr, and V) to avoid the brittle Fe-Ti intermetallic phases that form in the direct liquid phase joining of Ti-alloys and stainless steels. Using a combination of equilibrium calculations and Scheil-Gulliver simulations, a compositional pathway was designed to avoid deleterious phases. FGM samples were fabricated and experimentally characterized to determine the viability of the pathway. A change in phases from fcc to bcc was predicted to occur within the Ni-20Cr/Cr gradient region, and this was validated through experimental characterization. No detrimental phases (intermetallic, Laves, or σ phases) formed along the gradient path, demonstrating a successful computationally-informed design and fabrication of an FGM from SS316 to Ti-6Al-4V.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nonlinear sparse Bayesian learning for physics-based models

This paper addresses the issue of overfitting while calibrating unknown parameters of over-parameterized physics-based models with noisy and incomplete observations. Here, a semi-analytical Bayesian framework of nonlinear sparse Bayesian learning (NSBL) is proposed to identify sparsity among model parameters during Bayesian inversion. NSBL offers significant advantages over machine learning algorithm of sparse Bayesian learning (SBL) for physics-based models, such as 1) the likelihood function or the posterior parameter distribution is not required to be Gaussian, and 2) prior parameter knowledge is incorporated into sparse learning (i.e. not all parameters are treated as questionable). NSBL employs the concept of automatic relevance determination (ARD) to facilitate sparsity among questionable parameters through parameterized prior distributions. The analytical tractability of NSBL is enabled by employing Gaussian ARD priors and by building a Gaussian mixture-model approximation of the posterior parameter distribution that excludes the contribution of ARD priors. Subsequently, type-II maximum likelihood is executed using Newton's method whereby the evidence and its gradient and Hessian information are computed in a semi-analytical fashion. We show numerically and analytically that SBL is a special case of NSBL for linear regression models. Subsequently, a linear regression example involving multimodality in both parameter posterior pdf and model evidence is considered to demonstrate the performance of NSBL in cases where SBL is inapplicable. Next, NSBL is applied to identify sparsity among the damping coefficients of a mass-spring-damper model of a shear building frame. These numerical studies demonstrate the robustness and efficiency of NSBL in alleviating overfitting during Bayesian inversion of nonlinear physics-based models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Maximum energy product at elevated temperatures for hexagonal strontium ferrite (SrFe12O19) magnet

The electronic structure of hexagonal strontium ferrite (SrFe12O19) was calculated based on the density functional theory (DFT) and generalized gradient approximation (GGA). The GGA+U method was used to improve the description of localized Fe 3d electrons. Three different effective U (U-eff) values of 3.7, 7.0, and 10.3 eV were used to calculate three sets of exchange integrals for 21 excited states. We then calculated the temperature dependence of magnetic moments m(T) for the five sublattices (2a, 2b, 12k, 4f(1), and 4f(2)) using the exchange integrals. The m(T) of the five sublattices are inter related to the nearest neighbors, where the spins are mostly anti-ferromagnetically coupled. The five sublattice m(T) were used to ()brain the saturation magnetization M-s(T) of SrFe12O19, which is in good agreement with the experimental values. The temperature dependence of maximum energy product. ((BII)(max)(T)) was calculated using the calculated M-s(T). (C) 2013 Elsevier B.V. All rights reserved.

Park, J↗
DOE OSTI2015

Thermoelectric, electronic, optical and chemical bonding properties of Ba{sub 2}PrRuO{sub 6}: At temperature 7 K and 150 K

Highlights: • DFT-FPLAPW method used for calculating the electronic structure. • The Fermi surface of BPRO (7 K and 150 K) is also calculated. • The complex dielectric function has been calculated. • Thermoelectric properties were also calculated using BoltzTraP code. • Power factor shows that both compounds are good thermoelectric materials at 600 K. - Abstract: We present first principles calculations of the band structure, density of states, electronic charge density, Fermi surface and optical properties of Ba{sub 2}PrRuO{sub 6} single crystals at two different temperatures. The atomic positions were optimized by minimizing the forces acting on the atoms. We have employed the full potential linear augmented plane wave method within local density approximation, generalized gradient approximation and Engel–Vosko generalized gradient approximation to treat the exchange correlation potential. The calculation shows that the compound is superconductor with strong hybridization near the Fermi energy level. Fermi surface is composed of two sheets. The calculated electronic specific heat capacities indicate, very close agreement with the experimental one. The bonding features of the compounds are analyzed using the electronic charge density in the (1 0 0) and (0–10) crystallographic planes. The dispersion of the optical constants was calculated and discussed. The thermoelectric properties are also calculated using the BoltzTrap code.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Liquid-like solid-state diffusion of lithium ions in super-halide-rich argyrodite

The development of solid electrolytes with high ionic conductivity is essential for advancing safer, high-energy-density solid-state batteries, where lithium site distribution in the sublattice strongly affects ion transport. Here, we report a super-halide-rich argyrodite, Li 5.3 PS 4.3 Cl 1.7 , with remarkable room-temperature ionic conductivity (11.4 ± 0.7 mS cm -1 ) due to population of two additional interstitial lithium sites induced by vacancy redistribution. Prominent lithium density between lithium sites and elevated atomic displacement parameters indicate liquid-like diffusive behavior resembling sublattice melting. Combining electrochemical impedance spectroscopy, pulsed-field gradient NMR, and T 1 relaxation methods, we demonstrate that the augmented conductivity partly arises from a low energy barrier (0.08 eV) at the local scale, attributed to a three-site lithium distribution that drives correlated lithium dynamics. This work advances our understanding of the structure-dynamics interplay in super-halide-rich argyrodites, and highlighting their potential as solid-state battery electrolytes in cells with a coated single-crystal NMC82 cathode that achieve 170 mAh/g capacity at a 0.2 C rate .

25 ENERGY STORAGE↗
DOE OSTI2023

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Perfect Crystals: microgravity capillary counterdiffusion crystallization of human manganese superoxide dismutase for neutron crystallography

The NASA mission Perfect Crystals used the microgravity environment on the International Space Station (ISS) to grow crystals of human manganese superoxide dismutase (MnSOD)—an oxidoreductase critical for mitochondrial vitality and human health. The mission’s overarching aim is to perform neutron protein crystallography (NPC) on MnSOD to directly visualize proton positions and derive a chemical understanding of the concerted proton electron transfers performed by the enzyme. Large crystals that are perfect enough to diffract neutrons to sufficient resolution are essential for NPC. This combination, large and perfect, is hard to achieve on Earth due to gravity-induced convective mixing. Capillary counterdiffusion methods were developed that provided a gradient of conditions for crystal growth along with a built-in time delay that prevented premature crystallization before stowage on the ISS. Here, we report a highly successful and versatile crystallization system to grow a plethora of crystals for high-resolution NPC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗