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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

MATLAB/Simulink Framework for Modeling Complex Coolant Flow Configurations of Advanced Automotive Thermal Management Systems

The National Renewable Energy Laboratory's (NREL's) CoolSim MATLAB/Simulink modeling framework was extended by including a newly developed coolant loop solution method aimed at reducing the simulation effort for arbitrarily complex thermal management systems. The new approach does not require the user to identify specific coolant loops and their flow. The user only needs to connect the fluid network elements in a manner consistent with the desired schematic. Using the new solution method, a model of NREL's advanced combined coolant loop system for electric vehicles was created that reflected the test system architecture. This system was built using components provided by the MAHLE Group and included both air conditioning and heat pump modes. Validation with test bench data and verification with the previous solution method were performed for 10 operating points spanning a range of ambient temperatures between -2 degrees C and 43 degrees C. The largest root mean square difference between pressure, temperature, energy and mass flow rate data and simulation results was less than 7%.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

Joint neutron crystallographic and NMR solution studies of Tyr residue ionization and hydrogen bonding: Implications for enzyme-mediated proton transfer

Proton transfer is a fundamental mechanism at the core of many enzyme-catalyzed reactions. It is also exquisitely sensitive to a number of factors, including pH, electrostatics, proper active-site geometry, and chemistry. Carbonic anhydrase has evolved a fast and efficient way to conduct protons through a combination of hydrophilic amino acid side chains that coordinate a highly ordered H-bonded water network. This study uses a powerful approach, combining NMR solution studies with neutron protein crystallography, to determine the effect of pH and divalent cations on key residues involved in proton transfer in human carbonic anhydrase. Lastly, the results have broad implications for our understanding of proton transfer and how subtle changes in ionization and H-bonding interactions can modulate enzyme catalysis.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Stabilization of a locally minimal forest

The method of partial stabilization of locally minimal networks, which was invented by Ivanov and Tuzhilin to construct examples of shortest trees with given topology, is developed. According to this method, boundary vertices of degree 2 are not added to all edges of the original locally minimal tree, but only to some of them. The problem of partial stabilization of locally minimal trees in a finite-dimensional Euclidean space is solved completely in the paper, that is, without any restrictions imposed on the number of edges remaining free of subdivision. A criterion for the realizability of such stabilization is established. In addition, the general problem of searching for the shortest forest connecting a finite family of boundary compact sets in an arbitrary metric space is formalized; it is shown that such forests exist for any family of compact sets if and only if for any finite subset of the ambient space there exists a shortest tree connecting it. The theory developed here allows us to establish further generalizations of the stabilization theorem both for arbitrary metric spaces and for metric spaces with some special properties. Bibliography: 10 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD

Here, the Deep Operator Network (DeepONet) is a neural network architecture used to approximate operators, including the solution operator of parametric PDEs. DeepONets have shown remarkable approximation ability. However, the performance of DeepONets deteriorates when the training data is polluted with noise, a scenario that occurs in practice. To handle noisy data, we propose a Bayesian DeepONet based on replica exchange Langevin diffusion (reLD). Replica exchange uses two particles. The first particle trains a DeepONet to exploit the loss landscape and make predictions. The other particle trains a different DeepONet to explore the loss landscape and escape local minima via swapping. Compared to DeepONets trained with state-of-the-art gradient-based algorithms (e.g., Adam), the proposed Bayesian DeepONet greatly improves the training convergence for noisy scenarios and accurately estimates the uncertainty. To further reduce the high computational cost of the reLD training of DeepONets, we propose (1) an accelerated training framework that exploits the DeepONet's architecture to reduce its computational cost up to 25% without compromising performance and (2) a transfer learning strategy that accelerates training DeepONets for PDEs with different parameter values. Finally, we illustrate the effectiveness of the proposed Bayesian DeepONet using four parametric PDE problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

MEDUSA - An overset grid flow solver for network-based parallel computer systems

Continuing improvement in processing speed has made it feasible to solve the Reynolds-Averaged Navier-Stokes equations for simple three-dimensional flows on advanced workstations. Combining multiple workstations into a network-based heterogeneous parallel computer allows the application of programming principles learned on MIMD (Multiple Instruction Multiple Data) distributed memory parallel computers to the solution of larger problems. An overset-grid flow solution code has been developed which uses a cluster of workstations as a network-based parallel computer. Inter-process communication is provided by the Parallel Virtual Machine (PVM) software. Solution speed equivalent to one-third of a Cray-YMP processor has been achieved from a cluster of nine commonly used engineering workstation processors. Load imbalance and communication overhead are the principal impediments to parallel efficiency in this application.

Smith, Merritt H.↗
NASA NTRS1983

Use of a personal computer for dynamical engineering illustrations in a classroom and over an instructional TV network

A personal computer has been used to illustrate physical phenomena and problem solution techniques in engineering classes. According to student evaluations, instruction of concepts was greatly improved through the use of these illustrations. This paper describes the class of phenomena that can be effectively illustrated, the techniques used to create these illustrations, and the techniques used to display the illustrations in regular classrooms and over an instructional TV network. The features of a personal computer required to apply these techniques are listed. The capabilities of some present personal computers are discussed and a forecast of the capabilities of future personal computers is presented.

Watson, V. R.↗
DOE OSTI2022

Fully Conjugated Poly(phthalocyanine) Scaffolds Derived from a Mechanochemical Approach Towards Enhanced Energy Storage

Phthalocyanines (Pc)-derived materials represent an attractive category of porous organic scaffolds featured by extensive π-conjugated networks, but their construction is still limited to the solution-based pathways, producing materials with inferior conductivity and porosity. Herein, a mechanochemistry-driven approach was developed leveraging the on-surface polymerization of aromatic nitrile monomers with ortho-positioned dicyano groups in the presence of metal catalysts (magnesium, zinc, or aluminum) under neat and ambient conditions. Diverse Pc-functionalized conjugated porous networks (Pc-CPNs) were obtained featured by extensively and fully π-conjugated skeletons, high surface areas, and hierarchical porosities. The monomers in this mechanochemical approach could be extended to those difficult to be handled in solution-based procedures. In this work, the Pc-CPNs displayed attractive electrochemical performance as supercapacitor and anodes in batteries, together with superb long-term stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Gray-Box Approach for Thermal Modelling of Buildings for Applications in District Heating and Cooling Networks

District-heating-and-cooling (DHC) systems are a proven energy solution that has been deployed for many years in a growing number of urban areas worldwide. They comprise a variety of technologies that seek to develop synergies between the production and supply of heat, cooling, domestic hot water and electricity. Although the benefits of DHC systems are significant and have been widely acclaimed, yet the full potential of modern DHC systems remains largely untapped. There are several opportunities for development of energy efficient DHC systems, which will enable the effective exploitation of alternative renewable resources, waste heat recovery, etc., in order to increase the overall efficiency and facilitate the transition towards the next generation of DHC systems. This motivated the need for modelling these complex systems. Large-scale modelling of DHC-networks is challenging, as it has several components such as buildings, pipes, valves, heating source, etc., interacting with each other. In this paper, we focus on building modelling. In particular, we present a gray-box methodology for thermal modelling of buildings. Gray-box modelling is a hybrid of data driven and physics based models where, coefficients of the equations from physics based models are learned using data. This approach allows us to capture the dynamics of the buildings more effectively as compared to pure data driven approach. Additionally, this approach results in a simpler models as compared to pure physics based models. We first develop the individual components of the building such as temperature evolution, flow controller, etc. These individual models are then integrated in to the complete gray-box model for the building. The model is validated using data collected from one of the buildings at Lule{\aa}, a city on the coast of northern Sweden.

Saurav, Kumar↗
DOE OSTI2013

Irregular activity arises as a natural consequence of synaptic inhibition

Irregular neuronal activity is observed in a variety of brain regions and states. This work illustrates a novel mechanism by which irregular activity naturally emerges in two-cell neuronal networks featuring coupling by synaptic inhibition. We introduce a one-dimensional map that captures the irregular activity occurring in our simulations of conductance-based differential equations and mathematically analyze the instability of fixed points corresponding to synchronous and antiphase spiking for this map. We find that the irregular solutions that arise exhibit expansion, contraction, and folding in phase space, as expected in chaotic dynamics. Our analysis shows that these features are produced from the interplay of synaptic inhibition with sodium, potassium, and leak currents in a conductance-based framework and provides precise conditions on parameters that ensure that irregular activity will occur. In particular, the temporal details of spiking dynamics must be present for a model to exhibit this irregularity mechanism and must be considered analytically to capture these effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Nonlinear manifold-based reduced order model

Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-dominated flow phenomena such as in traffic flow, atmospheric flows, and air flow over vehicles, a low-dimensional linear subspace poorly approximates the solution. To address cases such as these, we have developed a fast and accurate physicsinformed neural network ROM, namely nonlinear manifold ROM (NM-ROM), which can better approximate high-fidelity model solutions with a smaller latent space dimension than the LSROMs. Our method takes advantage of the existing numerical methods that are used to solve the corresponding full order models. The efficiency is achieved by developing a hyper-reduction technique in the context of the NM-ROM. Numerical results show that neural networks can learn a more efficient latent space representation on advection-dominated data from 1D and 2D Burgers’ equations. A speedup of up to 2.6 for 1D Burgers’ and a speedup of 11.7 for 2D Burgers’ equations are achieved with an appropriate treatment of the nonlinear terms through a hyper-reduction technique. Finally, a posteriori error bounds for the NM-ROMs are derived that take account of the hyper-reduced operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2022

Friction stir welding and self-ion irradiation effects on microstructure and mechanical properties changes within oxide dispersion strengthened steel $\mathrm{MA956}$

We report the joining process for oxide dispersion strengthened (ODS) alloys remains a key challenge facing the nuclear community. The microstructure and mechanical properties were characterized in the base material and friction stir welded ODS MA956 irradiated with 5 MeV Fe 2+ ions from 400 to 500°C up to 25 dpa. Nanoindentation was performed to assess changes in hardness and yield stress, and the dispersed barrier hardening (DBH) model was applied to described results. A combination of scanning transmission electron microscopy (STEM) and atom probe tomography (APT) were used to assess evolution of the microstructure including dispersoids, network dislocations and dislocation loops, nanoclusters, and solid solution concentrations. Overall, softening was observed as a result of increased dose, which was exacerbated at 500°C. The formation and coarsening of new dispersoids was noted while nanoclusters tended to dissolve in the base material, and were not observed in the stir zone. Solute nanocluster evolution was identified as a primary driver of the changes in mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Effect of the pre-existing carbides on the grain boundary network during grain boundary engineering in a nickel based alloy

Grain boundary engineering was carried out on an aging-treated nickel based Alloy 690, which has precipitated carbides at grain boundaries. Electron backscatter diffraction technique was used to investigate the grain boundary networks. Results show that, compared with the solution-annealed samples, the aging-treated samples with pre-existing carbides at grain boundaries need longer duration or higher temperature during annealing after low-strain tensile deformation for forming high proportion of low-Σ coincidence site lattice grain boundaries (more than 75%). The reason is that the primary recrystallization is inhibited or retarded owing to that the pre-existing carbides are barriers to grain boundaries migration. - Highlights: • Study of GBE as function of pre-existing GB carbides, tensile strain and annealing • Recrystallization of GBE is inhibited or retarded by the pre-existing carbides. • Retained carbides after annealing show the original GB positions. • More than 80% of special GBs were formed after the modification of GBE processing. • Multiple twinning during recrystallization is the key process of GBE.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Carbon nanotube enables quantum leap in oil recovery

Highlights: {yields} A novel sealing material based on a CNT-rubber composite was developed for use in oilfield. {yields} Solved the critical issues of dispersion and bonding on CNT-rubber composites. {yields} Clarified the mechanism of nano network reinforcement using test data. -- Abstract: A novel sealing material based on a CNT-rubber composite was developed for use in producing oil from deep, hot reservoirs. Fully dispersed and better bondings are two critical advances that enhance its mechanical properties. Authors solved the critical issues and clarified the mechanism of nano network reinforcement using test data. The resulting sealing solution underwent field tests around the world, and it marks a rare success story for the use of nanotechnology in the oilfield.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

Silver-Loaded Xerogel Nanostructures for Iodine Capture: A Comparison of Thiolated versus Unthiolated Sorbents

Here this paper describes the development and provides comparisons of thiolated (-SH) and unthiolated Ag-Al-Si-O xerogels for iodine gas capture. These xerogels were produced from alkoxides and then heat-treated at 350 °C to provide mechanical strength for subsequent processing steps. Then, a portion of the xerogels was thiolated using (3-mercaptopropyl)trimethoxysilane. Next, thiolated and unthiolated batches were ion-exchanged in AgNO 3 solutions where Ag + replaced Na + in the gel network on a near 1:1 molar basis. Subsamples of the Ag-exchanged xerogels were subjected to a reduction step in H 2 /Ar to convert Ag + to Ag 0 where the rest of the Ag-exchanged (Ag + ) were not reduced. X-ray diffraction, X-ray photoelectron spectroscopy, and transmission electron microscopy revealed nanoscale Ag 0 in the Ag + samples despite no active reduction where actively reduced samples had bimodal Ag 0 distribution of ~2-3 nm hexagonal and ~6-7 nm cubic crystallites. Synchrotron X-ray absorption spectroscopy was used to assess the oxidization states of Ag, S, and I within the different xerogel samples. The specific surface areas of the base xerogels decreased as subsequent treatments were performed on the as-made samples, albeit the decreases were smaller than aerogel equivalents of these samples from a previous study. All iodine-loaded Ag-based samples showed a mixture of β-AgI and γ-AgI. Comparisons of iodine-loading results with other Ag-based iodine sorbents show that the thiolated Ag 0 -xerogels in this work have one of the highest iodine-loading capacities (q e ) reported to date in saturated conditions with the thiolated Ag 0 -xerogel showing 522 mg iodine per g of the sorbent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Capturing intrinsic site-dependent spectral signatures and lifetimes of isolated OH oscillators in extended water networks

The extremely broad infrared spectrum of water in the OH stretching region is a manifestation of how profoundly a water molecule is distorted when embedded in its extended hydrogen-bonding network. Many effects contribute to this breadth in solution at room temperature, which raises the question as to what the spectrum of a single OH oscillator would be in the absence of thermal fluctuations and coupling to nearby OH groups. We report the intrinsic spectral responses of isolated OH oscillators embedded in two cold (~20 K), hydrogen-bonded water cages adopted by the Cs + ·(HDO)(D 2 O) 19 and D 3 O + ·(HDO)(D 2 O) 19 clusters. Most OH oscillators yield single, isolated features that occur with linewidths that increase approximately linearly with their redshifts. Oscillators near 3,400 cm -1 , however, occur with a second feature, which indicates that OH stretch excitation of these molecules drives low-frequency, phonon-type motions of the cage. Finally, the excited state lifetimes inferred from the broadening are considered in the context of fluctuations in the local electric fields that are available even at low temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗