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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Analysis of phase stability and chemical segregation in the Mo-V alloys using a generalized embedded atom method potential

A new interatomic potential for the Mo-V system is introduced to facilitate the study of phase stability and mechanical properties at lower temperatures. This potential is based on a generalization of the embedded atom method and includes contributions from embedding energy, explicit two- and three-body interactions and nonlocal many-body interaction terms. The parameters of the potential are optimized by using data from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, such as elastic constants, equation of states, phonon dispersion curves, point defect properties and melting temperatures for different compositions. Even though our potential is trained on a small dataset, its accuracy is comparable to available machine learning potentials for Mo and V. Furthermore, our results show that an ordered B2 phase is stable at low temperatures in alloys containing 50% V, but the solid solution phase is stable above 800 K. However, such long-range ordering is not observed in V-rich or Mo-rich alloys. In addition, our results show that V segregates to dislocation cores and grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CoolPINNs: A physics-informed neural network modeling of active cooling in vascular systems

Emerging technologies like hypersonic aircraft, space exploration vehicles, and batteries avail fluid circulation in embedded microvasculatures for efficient thermal regulation. Modeling is vital during the design and operational phases of these engineered systems. However, many challenges exist in developing a modeling framework. What is lacking is an accurate framework that (i) captures sharp jumps in the thermal flux across complex vasculature layouts, (ii) deals with oblique derivatives (involving tangential and normal components), (iii) handles nonlinearity because of radiative heat transfer, (iv) provides a high-speed forecast for real-time monitoring, and (v) facilitates robust inverse modeling. Here, this paper addresses these challenges by availing the power of physics-informed neural networks (PINNs). We develop a fast, reliable, and accurate Scientific Machine Learning (SciML) framework for vascular-based thermal regulation—called CoolPINNs: a PINNs-based modeling framework for active cooling. The proposed mesh-less framework elegantly overcomes all the mentioned challenges. The significance of the reported research is multi-fold. First, the framework is valuable for real-time monitoring of thermal regulatory systems because of rapid forecasting. Second, researchers can address complex thermoregulation designs since the approach is meshless. Finally, the framework facilitates systematic parameter identification and inverse modeling studies, perhaps the most significant utility of the current framework.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nematoelasticity of hybrid molecular-colloidal liquid crystals

Colloidal rods immersed in a thermotropic liquid-crystalline solvent are at the basis of so-called hybrid liquid crystals, which are characterized by tunable nematic fluidity with symmetries ranging from conventional uniaxial nematic or antinematic to orthorhombic. Here, we provide a theoretical analysis of the elastic moduli of such systems by considering interactions between the individual rods with the embedding solvent through surface-anchoring forces, as well as steric and electrostatic interactions between the rods themselves. For uniaxial systems, the presence of colloidal rods generates a marked increase of the splay elasticity, which we found to be in quantitative agreement with experimental measurements. For orthorhombic hybrid liquid crystals, we provide estimates of all 12 elastic moduli and show that only a small subset of those elastic constants play a relevant role in describing the nematoelastic properties. The complexity and possibilities related to identifying the elastic moduli in experiments are briefly discussed.

42 ENGINEERING↗
DOE OSTI2020

Integrated Harsh Environment Gas / Temperature Wireless Microwave Acoustic Sensor System for Fossil Energy Applications

There is a significant need for sensors capable of detecting gases, such as H 2 , O 2 , NO x , SO x within harsh environments encountered in power plants, industrial manufacturing, oil and gas exploration, and aerospace applications. This project successfully demonstrated the use of wireless microwave acoustic sensor technology for the detection of gases (H 2 or O 2 ) from ambient temperatures up to 650°C. The work focused on langasite (LGS) based surface acoustic wave resonator (SAWR) sensors as the harsh-environment sensor platform and explored multiple combinations of high-temperature thin films and device structures which were used to increase the sensor platform stability and detection capability at temperatures in the operational range of 150°C to 700°C. Specific material configurations that were investigated include: yttria-stabilized-zirconia (YSZ) decorated with Pt nanoparticles, atomic layer deposited (ALD) Al 2 O 3 , palladium, and Pt/Al 2 O 3 co-deposited electrode alloys. Through the deposition of YSZ at temperatures as high as 850°C and the use of graded alloy concentrations of Pt in the fabrication of the Pt/Al 2 O 3 electrode structures, film stress problems were mitigated, and sensor operation and stability achieved. To test and evaluate SAWR sensor performance for the detection of H 2 and O 2 under the influence of temperature variations, a comprehensive gas sensor control system and test apparatus was created to operate within a laboratory box furnace-controlled environment. In addition to the advancement in thin film materials through the deposition and fabrication techniques mentioned above, the work characterized the performance of sensors containing these films in the presence of oxidizing and reducing gases between 25°C and 700°C. In particular, the work revealed that the exposure of the SAWR sensor surfaces to oxidizing environments significantly improve the sensor response to H 2 , whereas the exposure of the sensor to reducing environments at high temperatures (≈ 500°C) renders the sensor irresponsive to H 2 , requiring sensor surface treatment at high temperatures (above 500°C) to recover the responsiveness to H 2 . The SAWR sensors have been also tested for wireless operation and array operation using multiple orientations to resolve the detection of gases under temperature variations. The work developed at the University of Maine was aided by a collaboration with the NETL Research and Innovation Center, Pittsburgh, PA, where thin film materials and device structures fabricated at UMaine were tested and characterized using NETL gas reactors and surface analysis techniques. SAWR sensors fabricated at UMaine were exposed multiple times to temperatures up to 700°C and H 2 concentrations up to 100% in the NETL facilities to measure the sensor performance. Environetix Technologies Corporation, a UMaine harsh-environment sensor spin-off company, also provided support and assistance in sensor system testing and implementation. The sensor small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems and other aerospace or industrial settings that need to be interrogated wirelessly. The SAW platform is an attractive option for high-temperature harsh-environment gas sensing applications due to its inherent features, namely small size, capability of battery-free and wireless operation, and cost effective scale production using well-established production techniques from the semiconductor industry. The research findings achieved in this work, particularly advances regarding the fabrication and performance of the SAWR gas sensor platform, can be adapted and transferred to industrial power plants and other harsh environments.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2014

Calculation of nuclear spin-spin coupling constants using frozen density embedding

We present a method for a subsystem-based calculation of indirect nuclear spin-spin coupling tensors within the framework of current-spin-density-functional theory. Our approach is based on the frozen-density embedding scheme within density-functional theory and extends a previously reported subsystem-based approach for the calculation of nuclear magnetic resonance shielding tensors to magnetic fields which couple not only to orbital but also spin degrees of freedom. This leads to a formulation in which the electron density, the induced paramagnetic current, and the induced spin-magnetization density are calculated separately for the individual subsystems. This is particularly useful for the inclusion of environmental effects in the calculation of nuclear spin-spin coupling constants. Neglecting the induced paramagnetic current and spin-magnetization density in the environment due to the magnetic moments of the coupled nuclei leads to a very efficient method in which the computationally expensive response calculation has to be performed only for the subsystem of interest. We show that this approach leads to very good results for the calculation of solvent-induced shifts of nuclear spin-spin coupling constants in hydrogen-bonded systems. Also for systems with stronger interactions, frozen-density embedding performs remarkably well, given the approximate nature of currently available functionals for the non-additive kinetic energy. As an example we show results for methylmercury halides which exhibit an exceptionally large shift of the one-bond coupling constants between {sup 199}Hg and {sup 13}C upon coordination of dimethylsulfoxide solvent molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ab Initio Many Body Quantum Embedding and Local Correlation in Crystalline Materials using Interpolative Separable Density Fitting

We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit.

Chemical Physics (physics.chem-ph)↗
DOE OSTI2014

Dynamics of multiple viscoelastic carbon nanotube based nanocomposites with axial magnetic field

Nanocomposites and magnetic field effects on nanostructures have received great attention in recent years. A large amount of research work was focused on developing the proper theoretical framework for describing many physical effects appearing in structures on nanoscale level. Great step in this direction was successful application of nonlocal continuum field theory of Eringen. In the present paper, the free transverse vibration analysis is carried out for the system composed of multiple single walled carbon nanotubes (MSWCNT) embedded in a polymer matrix and under the influence of an axial magnetic field. Equivalent nonlocal model of MSWCNT is adopted as viscoelastically coupled multi-nanobeam system (MNBS) under the influence of longitudinal magnetic field. Governing equations of motion are derived using the Newton second low and nonlocal Rayleigh beam theory, which take into account small-scale effects, the effect of nanobeam angular acceleration, internal damping and Maxwell relation. Explicit expressions for complex natural frequency are derived based on the method of separation of variables and trigonometric method for the “Clamped-Chain” system. In addition, an analytical method is proposed in order to obtain asymptotic damped natural frequency and the critical damping ratio, which are independent of boundary conditions and a number of nanobeams in MNBS. The validity of obtained results is confirmed by comparing the results obtained for complex frequencies via trigonometric method with the results obtained by using numerical methods. The influence of the longitudinal magnetic field on the free vibration response of viscoelastically coupled MNBS is discussed in detail. In addition, numerical results are presented to point out the effects of the nonlocal parameter, internal damping, and parameters of viscoelastic medium on complex natural frequencies of the system. The results demonstrate the efficiency of the suggested methodology to find the closed form solutions for the free vibration response of multiple nanostructure systems under the influence of magnetic field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Development of a diversity and defense-in-depth strategy for the TerraPower TWR-P Advanced nuclear power plant - 380

A Diversity and Defense in Depth (D3) analysis was performed for the Traveling Wave Reactor, which is a Generation IV fast reactor design. The analysis demonstrated that the current I and C design for TWR-P provides sufficient diversity in the reactor shutdown system, the engineered safety features, and the post-accident monitoring system. In addition, an analysis was performed for the Embedded Digital Devices (EDD) to ensure a postulated Software Common Cause Failure (SWCCF) will not affect the safety functions of the TWR-P I and C system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Influence of CO 2 -Regenerative Film Properties in Enhancing C 2+ Products Selectivity While Mitigating CO 2 Crossover

Zero-gap anion-exchange membrane electrode assembly (AEMEA) electrolyzers operating in alkaline media face challenges such as CO 2 crossover and salting-out. The bipolar membrane electrode assembly (BPMEA) electrolyzer, using DI water as the electrolyte, addresses both CO 2 crossover and salting-out issues. As cations, which are crucial in stabilizing the CO 2 R intermediates, are absent in the electrolyte, cations embedded in the AEM play an important role in dictating the CO 2 R selectivity and activity in BPMEA systems. So far, no systematic study has been conducted on the influence of cations embedded in AEMs on CO 2 selectivity and activity in BPMEA systems. Moreover, BPMEA systems impose an additional challenge: low stability due to delamination of the bipolar membrane caused by CO 2 regeneration at the membrane-membrane interface. To enhance the stability of the electrolyzer, a simple yet highly reproducible strategy for coating a porous CO 2 regenerative film on smooth Nafion 117 is demonstrated in this work, along with a systematic study of four different commercially available AEMs, composed of different cations and cation densities, for use in combination with Nafion 117 and copper catalyst at the cathode. We found that the PiperION membrane delivers selectivity and activity comparable to those of alkali-metal cations, owing to the enhanced local electric field resulting from the combined effects of a high positive charge on the N atom of the piperidinium cation and the high ion-exchange capacity. Further, we studied the influence of the thickness of the PiperION porous layer over Nafion 117 on CO 2 R selectivity and found that 70 μm is the minimum thickness to achieve maximum C 2+ products selectivity, reduced the CO 2 crossover to 5% from 25% at 4 SCCM and 150 mA cm –2 , and was stable for operation beyond 100 h. The selectivity of this system, compared with AEMEA, and stability outperformed both AEMEA and BPMEA. This study helps design more effective BPMs to inhibit CO 2 crossover while enabling stable and selective electrochemical CO 2 reduction to C 2+ hydrocarbons.

Cations↗
DOE OSTI2023

Data assimilation in operator algebras

We develop an algebraic framework for sequential data assimilation of partially observed dynamical systems. In this framework, Bayesian data assimilation is embedded in a nonabelian operator algebra, which provides a representation of observables by multiplication operators and probability densities by density operators (quantum states). In the algebraic approach, the forecast step of data assimilation is represented by a quantum operation induced by the Koopman operator of the dynamical system. Moreover, the analysis step is described by a quantum effect, which generalizes the Bayesian observational update rule. Projecting this formulation to finite-dimensional matrix algebras leads to computational schemes that are i) automatically positivity-preserving and ii) amenable to consistent data-driven approximation using kernel methods for machine learning. Moreover, these methods are natural candidates for implementation on quantum computers. Applications to the Lorenz 96 multiscale system and the El Niño Southern Oscillation in a climate model show promising results in terms of forecast skill and uncertainty quantification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Learning emergent partial differential equations in a learned emergent space

We propose an approach to learn effective evolution equations for large systems of interacting agents. This is demonstrated on two examples, a well-studied system of coupled normal form oscillators and a biologically motivated example of coupled Hodgkin-Huxley-like neurons. For such types of systems there is no obvious space coordinate in which to learn effective evolution laws in the form of partial differential equations. In our approach, we accomplish this by learning embedding coordinates from the time series data of the system using manifold learning as a first step. In these emergent coordinates, we then show how one can learn effective partial differential equations, using neural networks, that do not only reproduce the dynamics of the oscillator ensemble, but also capture the collective bifurcations when system parameters vary. The proposed approach thus integrates the automatic, data-driven extraction of emergent space coordinates parametrizing the agent dynamics, with machine-learning assisted identification of an emergent PDE description of the dynamics in this parametrization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Submicron R{sub 2}Fe{sub 14}B particles

Mechanochemical synthesis of submicron R{sub 2}Fe{sub 14}B particles with R = Dy, Nd, Pr has been performed successfully via high energy ball milling of rare-earth oxides, iron oxide and boron oxide in the presence of a reducing agent (Ca) and a dispersant material (CaO), followed by annealing at 800 - 900 °C. In the R = Nd system, we were able to fabricate particles embedded in a CaO matrix with coercivity (H{sub c}) of 10.3 kOe after annealing at 900 °C for 5 min. After washing off the dispersant, the H{sub c} was decreased to below 1 kOe because of hydrogen absorption that leads to the formation of the hydrated R{sub 2}Fe{sub 14}BH{sub x} phase that has a lower anisotropy. Upon removal of the hydrogen the coercivity was increased to 3.3 kOe. The average size of the Nd{sub 2}Fe{sub 14}B particles increases from 100 nm in a sample synthesized at 800 °C to 158 nm at 900 °C. The isotropic Dy{sub 2}Fe{sub 14}B particles showed a higher coercivity of 21 kOe in washed samples after annealing at 900 °C for 5 min. An average size of 71 nm is measured in samples synthesized at 800 °C and 107 nm at 900 °C. Fitting the high field M(H) measurements in Nd{sub 2}Fe{sub 14}B to the law of approach to saturation gave values for the magnetocrystalline anisotropy for the washed sample 2.23 × 10{sup 7} erg/cm{sup 3} and for the vacuum annealed sample 4.15 × 10{sup 7} erg/cm{sup 3}, both of which are lower than the bulk values. This would explain the lower values of H{sub c} observed in the particles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Sentiment Analysis based Error Detection for Large-Scale Systems

Today's large-scale systems such as High Performance Computing (HPC) Systems are designed/utilized towards exascale computing, inevitably decreasing its reliability due to the increasing design complexity. HPC systems conduct extensive logging of their execution behaviour. In this paper, we leverage the inherent meaning behind the log messages and propose a novel sentiment analysis-based approach for the error detection in large-scale systems, by automatically mining the sentiments in the log messages. Our contributions are four-fold. (1) We develop a machine learning (ML) based approach to automatically build a sentiment lexicon, based on the system log message templates. (2) Using the sentiment lexicon, we develop an algorithm to detect system errors. (3) We develop an algorithm to identify the nodes and components with erroneous behaviors, based on sentiment polarity scores. (4) We evaluate our solution vs. other state-of-the-art machine/deep learning algorithms based on three representative supercomputers' system logs. Experiments show that our error detection algorithm can identify error messages with an average MCC score and f -score of 91% and 96% respectively, while state of the art ML/deep learning model (LSTM) obtains only 67% and 84%. To the best of our knowledge, this is the first work leveraging the sentiments embedded in log entries of large-scale systems for system health analysis.

error detection↗
DOE OSTI2019

Brain sensor and communication model using plasmonic microring antenna network

In this paper, the feasibility of using multi-plasmonic sensing probes embedded nanoring resonators within a microring resonator system for brain signal probing and the investigation proposed. The plasmonic sensor probes made of gold grating placed within the center nanorings, from which the sensor network of the coupling light from the multi-plasmonic probes to the main ring established. The coupling between light (photon) power and nano-gold grating generate the plasmonic polariton dipoles and propagate within the system. The whispering gallery mode generates at the center rings, which will couple into the sensor probes and the induced change in optical signals that can detect via the system ports. The coupling between the generated polaritons and external stimuli from the sub-brain cells will change the optical output signals, where the electro-optic signal conversion can be applied. In manipulation, the changes in the grating periods of the sensing probes will affect the changes in the output signals, which can distinguish by the sensing probe signal Bragg wavelengths. The optical filter applied at each of the device port to attenuate the output signal that can be applied for brain investigation. The simulation results have shown that the low output power can detect for sub-level investigation.

42 ENGINEERING↗
DOE OSTI2015

Exponential time-differencing with embedded Runge–Kutta adaptive step control

We have presented the first embedded Runge–Kutta exponential time-differencing (RKETD) methods of fourth order with third order embedding and fifth order with third order embedding for non-Rosenbrock type nonlinear systems. A procedure for constructing RKETD methods that accounts for both order conditions and stability is outlined. In our stability analysis, the fast time scale is represented by a full linear operator in contrast to particular scalar cases considered before. An effective time-stepping strategy based on reducing both ETD function evaluations and rejected steps is described. Comparisons of performance with adaptive-stepping integrating factor (IF) are carried out on a set of canonical partial differential equations: the shock-fronts of Burgers equation, interacting KdV solitons, KS controlled chaos, and critical collapse of two-dimensional NLS.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Exact kinetic energy enables accurate evaluation of weak interactions by the FDE-vdW method

The correlation energy of interaction is an elusive and sought-after interaction between molecular systems. By partitioning the response function of the system into subsystem contributions, the Frozen Density Embedding (FDE)-vdW method provides a computationally amenable nonlocal correlation functional based on the adiabatic connection fluctuation dissipation theorem applied to subsystem density functional theory. In reproducing potential energy surfaces of weakly interacting dimers, we show that FDE-vdW, either employing semilocal or exact nonadditive kinetic energy functionals, is in quantitative agreement with high-accuracy coupled cluster calculations (overall mean unsigned error of 0.5 kcal/mol). When employing the exact kinetic energy (which we term the Kohn-Sham (KS)-vdW method), the binding energies are generally closer to the benchmark, and the energy surfaces are also smoother.

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

Cryo-FIB for TEM Investigation of Soft Matter and Beam Sensitive Energy Materials

Primarily driven by structural biology, the rapid advances in cryogenic electron microscopy techniques are now being adopted and applied by materials scientists. Samples that inherently have electron transparency can be rapidly frozen (vitrified) in amorphous ice and imaged directly on a cryogenic transmission electron microscopy (cryo-TEM), however this is not the case for many important materials systems, which can consist of layered structures, embedded architectures, or be contained within a device. Cryogenic focused ion beam (cryo-FIB) lift-out procedures have recently been developed to extract intact regions and interfaces of interest, that can then be thinned to electron transparency and transferred to the cryo-TEM for characterization. Several detailed studies have been reported demonstrating the cryo-FIB lift-out procedure, however due to its relative infancy in materials science improvements are still required to ensure the technique becomes more accessible and routinely successful. Here, we review recent results on the preparation of cryo-TEM lamellae using cryo-FIB and show that the technique is broadly applicable to a range of soft matter and beam sensitive energy materials. We then present a tutorial that can guide the materials scientist through the cryo-FIB lift-out process, highlighting recent methodological advances that address the most common failure points of the technique, such as needle attachment, lift-out and transfer, and final thinning.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cooperative effects in thin dielectric layers: Long-range Dicke superradiance

The realization and control of collective quantum effects so far have predominantly focused on cold atomic ensembles. Quantum photonic platforms, with their engineered Green's functions and integration capability of advanced solid-state quantum emitters, provide opportunities to explore regimes of light-matter interaction beyond the scope of atomic systems. In this work, we demonstrate that embedding quantum emitters within a thin dielectric layer fundamentally alters their collective radiative behavior. The optical modes in the dielectric layer mediate long-range dipole-dipole interactions between emitters, enabling both total and directional superradiance between emitters separated by several wavelengths. Crucially, this mechanism supports Dicke superradiance even in parameter regimes where standard settings fail to support an interaction, unveiling a dimensionality-driven enhancement of cooperative effects. By bridging many-body quantum optics and photonic engineering, our work reveals a distinct interplay between surrounding dimensionality and collective quantum dynamics. Experimental realization of these predictions, readily achievable in solid-state quantum optics platforms, paves the way for scalable, directional quantum light sources and frontiers in many-body quantum optics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗