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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Connectivity, Centrality, and Bottleneckedness: On Graph Theoretic Methods for Power Systems

This report provides an introduction to selected graph theoretic topics with pertinence to the structural analysis of electric power grid and communication systems. We focus on methodologies for defining, scoring, and identifying connectivity, spectral, and bottleneckeness properties in graphs, as well as vertex and edge importance measures such as centrality. We apply these measures to power systems and communications graph data, discuss and visualize the results, and comment on computational aspects of these methods. We show that graph theoretic methods can provide useful insights into grid and communication network structure, leading to tools and methods that could be used by electric utility engineers to improve key grid and communication network characteristics, such as resilience and scalability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Bayesian chain graph models to characterize microbe-environment dynamics

Microbiome data require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is problematic given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence, thus agreeing with the experimenter's intuition on the average behavior of nodes under treatment. The solution to our model is sparse via the Bayesian linear regression (LASSO). In addition, we propose an adaptive extension so that different shrinkages can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting, and compositional responses via an appropriate hierarchical structure. We test the performance of our model in a variety of simulated datasets, thereby showing superior performance to state-of-the-art approaches. We further apply our model to human gut and soil microbial compositional datasets, and we highlight that CG-LASSO can estimate biologically meaningful network structures in the data.

compositional data↗
DOE OSTI2023

Reducing uncertainty of high-latitude ecosystem models through identification of key parameters

Abstract Climate change is having significant impacts on Earth’s ecosystems and carbon budgets, and in the Arctic may drive a shift from an historic carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic changes demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions. One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. We identify that mismatch for three TBMs, DVM-DOS-TEM, SIPNET and ED2, and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parametrization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, we develop a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes. We map model parameters to processes in the CLD and assess parameter vulnerability via the internal network structure. One important substructure, feed forward loops (FFLs), describe processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty. We find that the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, we identify the key parameters responsible for limited model accuracy. They should be prioritized for future data sampling to reduce model uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An All-Ceramic, Anisotropic, and Flexible Aerogel Insulation Material

To exploit the high-temperature superinsulation potential of anisotropic thermal management materials, the incorporation of ceramic aerogel into the aligned structural networks is indispensable. Yet, the long-standing obstacle to exploring ultralight superinsulation ceramic aerogels is the inaccessibility of its mechanical elasticity, stability, and anisotropic thermal insulation. Here, we report a recoverable, flexible ceramic fiber-aerogel composite with anisotropic lamellar structure, where the interfacial cross-linking between ceramic fiber and aerogel is important in its superinsulation performance. The resulting ultralight aerogel composite exhibits a density of 0.05 g/cm 3 , large strain recovery (over 50%), and low thermal conductivity (0.0224 W m –1 K –1 ), while its hydrophobicity is achieved by in situ trichlorosilane coating with the water contact angle of 135°. The hygroscopic tests of such aerogel composites demonstrate a reversible thermal insulation. The mechanical elasticity and stability of the anisotropic composites, with its soundproof performance, shed light on the low-cost superelastic aerogel manufacturing with scalability for energy saving building applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization

The rate of frontal ring-opening metathesis polymerization (FROMP) using the Grubbs generation II catalyst is impacted by both the concentration and choice of monomers and inhibitors, usually organophosphorus derivatives. Herein we report a data-science-driven workflow to evaluate how these factors impact both the rate of FROMP and how long the formulation of the mixture is stable (pot life). Using this workflow, we built a classification model using a single-node decision tree to determine how a simple phosphine structural descriptor (V bur-near ) can bin long versus short pot life. Additionally, we applied a nonlinear kernel ridge regression model to predict how the inhibitor and selection/concentration of comonomers impact the FROMP rate. Furthermore, the analysis provides selection criteria for material network structures that span from highly cross-linked thermosets to non-cross-linked thermoplastics as well as degradable and nondegradable materials.

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

Reactive Extrusion of Waste Plastics with Compatibilizer and Lightly Pyrolyzed Crumb Rubber for Asphalt Modification

Modifying asphalt is a potentially high-value application for reusing waste plastics because of the high-volume usage of asphalt in highway construction. However, simply blending hot plastics and asphalt encounters difficulties related to the poor solubility of polymers, which limits the formation of a swollen network with asphalt molecules. The polymer phases also tend to coalesce and separate from asphalt during high-temperature storage in static conditions. The present study developed an innovative process to stabilize waste plastics in asphalt and improve binder storage stability by using lightly pyrolyzed crumb rubber together with a chemical compatibilizer. Both polymers were extruded to produce a thermoplastic elastomer (TPE) for asphalt modification. The mechanical performance and chemical reactions of TPEs were characterized via tension test and Fourier transform infrared spectroscopy. The storage stability and rheological properties of modified binder blends were evaluated through laboratory asphalt stability test and dynamic shear rheometer test. Polymer phases and network structures were characterized through optical microscopy. It was found that the pyrolyzed and reactive extrusion process improved the rubber solubility and polymer interaction, and therefore the storage stability of modified binder blends. The co-existence of rigid plastic and soft rubbery regimes in an entangled network provided a promising pathway to improve the mechanical performance of asphalt binders in both high- and low-temperature domains.

Ma, Yuetan↗
DOE OSTI2023

Evidence for Lignin–Carbohydrate Complexes from Studies of Transgenic Switchgrass and a Model Lignin–Pectin Composite

Lignin–carbohydrate complexes (LCCs) form through interactions of lignin with plant cell wall polysaccharides and are thought to be a significant source of biomass recalcitrance. In this work, we investigated LCCs formed between lignin and pectin homogalacturonan (HG). The structural changes in HG deficient transgenic switchgrass (GAUT4-knockdown, GAUT4-KD) after hot water pretreatment were compared to wild-type plants using small-angle neutron scattering (SANS), which showed that there were ~2.2-fold more lignin aggregates in GAUT4-KD biomass compared to the wild type. This demonstrated that decreased pectin resulted in more lignin redistribution and suggested that interactions between lignin and HG restrict lignin mobility in plant cell walls. To better understand the types of interactions between lignin and pectin, a model composite was prepared by polymerizing either protiated or partially deuterated coniferyl alcohol to form a dehydrogenation polymer (DHP) in the presence of HG. Small-angle X-ray scattering (SAXS) showed that the DHP and HG form a highly interconnected network structure that is not observed in a physical mixture of the individual polymers. Contrast matching SANS revealed the structure of DHP and HG in the composite and showed that the HG forms a swollen interconnected polymer network (power-law exponent, P = 1.5) interspersed with DHP particles (radius of gyration, Rg, 264 Å) that are composed of solvent-accessible DHP polymers (P = 2.3). Fourier transform infrared spectroscopy showed a unique ester absorption band in the DHP/HG composites. Solid-state nuclear magnetic resonance (NMR) analysis also supports interactions between DHP and HG. Overall, this study provides new insights into the relationship between primary and secondary cell wall polymers during cell wall synthesis and how LCCs formed between pectin and lignin could represent a previously unrecognized source of biomass recalcitrance. This knowledge may help develop new approaches to modulate cell wall properties to improve biofuel and bioproduct production.

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

Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration

We perform a set of high-fidelity simulations of geochemical reactions within three-dimensional discrete fracture networks (DFN) and use various machine learning techniques to determine the primary factors controlling mineral dissolution. The DFN are partially filled with quartz that gradually dissolves until quasi-steady state conditions are reached. At this point, we measure the quartz remaining in each fracture within the domain as our primary quantity of interest. We observe that a primary sub-network of fractures exists, where the quartz has been fully dissolved out. This reduction in resistance to flow leads to increased flow channelization and reduced solute travel times. However, depending on the DFN topology and the rate of dissolution, we observe substantial variability in the volume of quartz remaining within fractures outside of the primary subnetwork. This variability indicates an interplay between the fracture network structure and geochemical reactions. We characterize the features controlling these processes by developing a machine learning framework to extract their relevant impact. Specifically, we use a combination of high-fidelity simulations with a graph-based approach to study geochemical reactive transport in a complex fracture network to determine the key features that control dissolution. We consider topological, geometric and hydrological features of the fracture network to predict the remaining quartz in quasi-steady state. We found that the dissolution reaction rate constant of quartz and the distance to the primary sub-network in the fracture network are the two most important features controlling the amount of quartz remaining. This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Resilience of the slow component in timescale separated synchronized oscillators

Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Resilience of the slow component in timescale-separated synchronized oscillators

Physiological networks are usually made of a large number of biological oscillators evolving on a multitude of different timescales. Phase oscillators are particularly useful in the modelling of the synchronization dynamics of such systems. If the coupling is strong enough compared to the heterogeneity of the internal parameters, synchronized states might emerge where phase oscillators start to behave coherently. Here, we focus on the case where synchronized oscillators are divided into a fast and a slow component so that the two subsets evolve on separated timescales. We assess the resilience of the slow component by, first, reducing the dynamics of the fast one using Mori-Zwanzig formalism. Second, we evaluate the variance of the phase deviations when the oscillators in the two components are subject to noise with possibly distinct correlation times. From the general expression for the variance, we consider specific network structures and show how the noise transmission between the fast and slow components is affected. Interestingly, we find that oscillators that are among the most robust when there is only a single timescale, might become the most vulnerable when the system undergoes a timescale separation. We also find that layered networks seem to be insensitive to such timescale separations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations

Physics-informed neural networks (PINNs) based machine learning is an emerging framework for solving nonlinear differential equations. However, due to the implicit regularity of neural network structure, PINNs can only find the flattest solution in most cases by minimizing the loss functions. In this paper, we combine PINNs with the homotopy continuation method, a classical numerical method to compute isolated roots of polynomial systems, and propose a new deep learning framework, named homotopy physics-informed neural networks (HomPINNs), for solving multiple solutions of nonlinear elliptic differential equations. The implementation of an HomPINN is a homotopy process that is composed of the training of a fully connected neural network, named the starting neural network, and training processes of several PINNs with different tracking parameters. The starting neural network is to approximate a starting function constructed by the trivial solutions, while other PINNs are to minimize the loss functions defined by boundary condition and homotopy functions, varying with different tracking parameters. These training processes are regraded as different steps of a homotopy process, and a PINN is initialized by the well-trained neural network of the previous step, while the first starting neural network is initialized using the default initialization method. Finally, several numerical examples are presented to show the efficiency of our proposed HomPINNs, including reaction-diffusion equations with a heart-shaped domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Organo‐Functionalized Lacunary Double Cubane‐Type Oxometallates: Synthesis, Structure, and Properties of [(M II Cl) 2 (V IV O) 2 {((HOCH 2 CH 2 )(H)N(CH 2 CH 2 O))(HN(CH 2 CH 2 O) 2 )} 2 ] (M=Co, Zn)

Abstract Organofunctionalized tetranuclear clusters [(M II Cl) 2 (V IV O) 2 {((HOCH 2 CH 2 )(H)N(CH 2 CH 2 O))(HN(CH 2 CH 2 O) 2 )} 2 ] (1, M=Co,2: M=Zn) containing an unprecedented oxometallacyclic {M 2 V 2 Cl 2 N 4 O 8 } (M=Co, Zn) framework have been prepared by solvothermal reactions. The new oxo‐alkoxide compounds were fully characterized by spectroscopic methods, magnetic susceptibility measurement, DFT and ab initio computational methods, and complete single‐crystal X‐ray diffraction structure analysis. The isostructural clusters are formed of edge‐sharing octahedral {VO 5 N} and trigonal bipyramidal {MO 3 NCl} units. Diethanolamine ligates the bimetallic lacunary double cubane core of1and2in an unusual two‐mode fashion, unobserved previously. In the crystalline state, the clusters of1and2are joined by hydrogen bonds to form a three‐dimensional network structure. Magnetic susceptibility data indicate weakly antiferromagnetic interactions between the vanadium centers [J iso (V IV −V IV )=−5.4(1); −3.9(2) cm −1 ], and inequivalent antiferromagnetic interactions between the cobalt and vanadium centers [J iso (V IV −Co II )=−12.6 and −7.5 cm −1 ] contained in1.

Chemistry↗
DOE OSTI2024

“Hybridizing heat-integrated 3D printed modules with mass manufacturable, low pressure drop fiber sorbents” (Final Report)

The overall objective of this proposal was to research, develop, and evaluate a modular direct air capture (DAC) system that is simple and scalable. This system was based on the adsorption of CO 2 into commercial polyamines supported by porous fiber sorbents, which can be produced at kilometer per hour scales using our pre-pilot spinning line. We have housed these fiber materials in novel 3D printed modular housing systems that provide easy-to-manufacture and localized heat integration and flow control. This hybridization of fiber sorbent technology with modular housing provided several advantages that led to lower air pressure drops, higher sorbent productivity, as well as ease of manufacturing and assembly unrivaled by existing and emerging heat integrated contactor designs. The modular housing was fabricated with the following features, at a minimum: (i) a tapered air intake to reduce pressure drop related to entrance effects, (ii) a structured network of heat transfer channels to enable localized cooling and heating during adsorption and desorption, respectively, and (iii) low pressure drop supports for the fiber sorbents. This hybrid manufacturing approach provides a facile method for taking fiber sorbents from lab scale to pilot scale as it dramatically simplifies the fabrication of heat integrated contactor structures.

42 ENGINEERING↗
DOE OSTI2022

Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Water Network Tool for Resilience (WNTR). User Manual, Version 0.2.3

The Water Network Tool for Resilience (WNTR, pronounced winter) is a Python package designed to simulate and analyze resilience of water distribution networks. Here, a network refers to the collection of pipes, pumps, valves, junctions, tanks, and reservoirs that make up a water distribution system. WNTR has an application programming interface (API) that is flexible and allows for changes to the network structure and operations, along with simulation of disruptive incidents and recovery actions. WNTR is based upon EPANET, which is a tool to simulate the movement and fate of drinking water constituents within distribution systems. Users are encouraged to be familiar with the use of EPANET and/or should have background knowledge in hydraulics and pressurized pipe network modeling before using WNTR. EPANET has a graphical user interface that might be a useful tool to facilitate the visualization of the network and the associated analysis results. Information on EPANET can be found at https://www.epa.gov/water-research/epanet. WNTR is compatible with EPANET 2.00.12 [Ross00]. In addition, users should have experience using Python, including the installation of additional Python packages. General information on Python can be found at https://www.python.org/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Unravelling the mechanism of pressure induced polyamorphic transition in an inorganic molecular glass

The atomic structure of a germanium doped phosphorous selenide glass of composition Ge 2.8 P 57.7 Se 39.5 is determined as a function of pressure from ambient to 24 GPa using Monte-Carlo simulations constrained by high energy x-ray scattering data. The ambient pressure structure consists primarily of P 4 Se 3 molecules and planar edge shared phosphorus rings, reminiscent of those found in red phosphorous as well as a small fraction of locally clustered corner-sharing GeSe 4 tetrahedra. This low-density amorphous phase transforms into a high-density amorphous phase at ~6.3 GPa. The high-pressure phase is characterized by an extended network structure. The polyamorphic transformation between these two phases involves opening of the P 3 ring at the base of the P 4 Se 3 molecules and subsequent reaction with red phosphorus type moieties to produce a cross linked structure. The compression mechanism of the low-density phase involves increased molecular packing, whereas that of the high pressure phase involves an increase in the nearest-neighbor coordination number while the bond angle distributions broaden and shift to smaller angles. The entropy and volume changes associated with this polyamorphic transformation are positive and negative, respectively, and consequently the corresponding Clapeyron slope for this transition would be negative. This result has far reaching implications in our current understanding of the thermodynamics of polyamorphic transitions in glasses and glass-forming liquids.

42 ENGINEERING↗
DOE OSTI2026

Recurrent convolutional neural networks for modeling nonadiabatic dynamics of quantum-classical systems

Recurrent neural networks (RNNs) have recently been extensively applied to model the time evolution in fluid dynamics, weather predictions, and even chaotic systems due to their ability to capture temporal dependencies and sequential patterns in data. Here we present an RNN model based on convolutional neural networks for modeling the nonlinear nonadiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The Physics-Aware Recurrent Convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. Here, we apply our RNN approach to learn the space-time evolution of a one-dimensional semiclassical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.

Holstein model↗
DOE OSTI2023

Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions

Machine learning (ML) models, and Long Short-Term Memory (LSTM) networks in particular, have demonstrated remarkable performance in streamflow prediction and are increasingly being used by the hydrological research community. However, most of these applications do not include uncertainty quantification (UQ). ML models are data driven and can suffer from large extrapolation errors when applied to changing climate/environmental conditions. UQ is required to quantify the influence of data noises on model predictions and avoid overconfident projections in extrapolation. In this work, we integrate a novel UQ method, called PI3NN, with LSTM networks for streamflow prediction. PI3NN calculates Prediction Intervals by training 3 Neural Networks. It can precisely quantify the predictive uncertainty caused by the data noise and identify out-of-distribution (OOD) data in a non-stationary condition to avoid overconfident predictions. We apply the PI3NN-LSTM method in the snow-dominant East River Watershed in the western US and in the rain-driven Walker Branch Watershed in the southeastern US. Results indicate that for the prediction data which have similar features as the training data, PI3NN precisely quantifies the predictive uncertainty with the desired confidence level; and for the OOD data where the LSTM network fails to make accurate predictions, PI3NN produces a reasonably large uncertainty indicating that the results are not trustworthy and should avoid overconfidence. PI3NN is computationally efficient, robust in performance, and generalizable to various network structures and data with no distributional assumptions. It can be broadly applied in ML-based hydrological simulations for credible prediction.

54 ENVIRONMENTAL SCIENCES↗