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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparative Study of High-Resolution LysB29(Nε-myristoyl) des(B30) Insulin Structures Display Novel Dynamic Causal Interrelations in Monomeric-Dimeric Motions

The treatment of insulin-dependent diabetes mellitus is characterized by artificial supplementation of pancreatic β-cell ability to regulate sugar levels in the blood. Even though various insulin analogs are crucial for reasonable glycemic control, understanding the dynamic mechanism of the insulin analogs may help to improve the best-protracted insulin analog to assist people with type 1 diabetes (T1D) to live comfortably while maintaining tight glycemic control. Here, we present the high-resolution crystal structure of NN304, known as insulin detemir, to 1.7 Å resolution at cryogenic temperature. We computationally further investigated our crystal structure’s monomeric-dimeric conformation and dynamic profile by comparing it with a previously available detemir structure (PDB ID: 1XDA). Our structure (PDB ID: 8HGZ), obtained at elevated pH, provides electrostatically triggered minor movements in the equilibrium between alternate conformational substates compared to the previous structure, suggesting it might induce an intermediate state in the dissociation pathway of the insulin detemir’s hexamer:dihexamer equilibrium. Supplemented with orientational cross-correlation analysis by a Gaussian network model (GNM), this alternate oligomeric conformation offers the distinct cooperative motions originated by loose coupling of distant conformational substates of a protracted insulin analog that has not been previously observed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Towards replacing physical testing of granular materials with a Topology-based Model

In the study of packed granular materials, the performance of a sample (e.g., the detonation of a high-energy explosive) often correlates to measurements of a fluid flowing through it. The “effective surface area,” the surface area accessible to the airflow, is typically measured using a permeametry apparatus that relates the flow conductance to the permeable surface area via the Carman-Kozeny equation. This equation allows calculating the flow rate of a fluid flowing through the granules packed in the sample for a given pressure drop. However, Carman-Kozeny makes inherent assumptions about tunnel shapes and flow paths that may not accurately hold in situations where the particles possess a wide distribution in shapes, sizes, and aspect ratios, as is true with many powdered systems of technological and commercial interest. To address this challenge, we replicate these measurements virtually on micro-CT images of the powdered material, introducing a new Pore Network Model based on the skeleton of the Morse-Smale complex. Pores are identified as basins of the complex, their incidence encodes adjacency, and the conductivity of the capillary between them is computed from the cross-section at their interface. We build and solve a resistive network to compute an approximate laminar fluid flow through the pore structure. Here, we provide two means of estimating flow-permeable surface area: (i) by direct computation of conductivity, and (ii) by identifying dead-ends in the flow coupled with isosurface extraction and the application of the Carman-Kozeny equation, with the aim of establishing consistency over a range of particle shapes, sizes, porosity levels, and void distribution patterns.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantifying T Cell Cross-Reactivity: Influenza and Coronaviruses

If viral strains are sufficiently similar in their immunodominant epitopes, then populations of cross-reactive T cells may be boosted by exposure to one strain and provide protection against infection by another at a later date. This type of pre-existing immunity may be important in the adaptive immune response to influenza and to coronaviruses. Patterns of recognition of epitopes by T cell clonotypes (a set of cells sharing the same T cell receptor) are represented as edges on a bipartite network. We describe different methods of constructing bipartite networks that exhibit cross-reactivity, and the dynamics of the T cell repertoire in conditions of homeostasis, infection and re-infection. Cross-reactivity may arise simply by chance, or because immunodominant epitopes of different strains are structurally similar. We introduce a circular space of epitopes, so that T cell cross-reactivity is a quantitative measure of the overlap between clonotypes that recognize similar (that is, close in epitope space) epitopes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

How production networks amplify economic growth

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

A Simple Comparison of Biochemical Systems Theory and Metabolic Control Analysis

This paper explores some basic concepts of Biochemical Systems Theory (BST) and Metabolic Control Analysis (MCA), two frameworks developed to understand the behavior of biochemical networks. Initially introduced by Savageau, BST focuses on system stability and employs power laws in modeling biochemical systems. On the other hand, MCA, pioneered by authors such as Kacser and Burns and Heinrich and Rapoport, emphasizes linearization of the governing equations and describes relationships (known as theorems) between different measures. Despite apparent differences, both frameworks are shown to be equivalent in many respects. Through a simple example of a linear chain, the paper demonstrates how BST and MCA yield identical results when analyzing steady-state behavior and logarithmic gains within biochemical pathways. This comparative analysis highlights the interchangeability of concepts such as kinetic orders, elasticities and other logarithmic gains.

FOS: Biological sciences↗
DOE OSTI2021

Long-term excess nitrogen fertilizer increases sensitivity of soil microbial community to seasonal change revealed by ecological network and metagenome analyses

Nitrogen (N) fertilizer has often been generously applied to increase crop biomass yield. Although the influences of inorganic N fertilizer on soil microbial communities have been widely studied, the effect of N fertilizer on microbial co-occurrence networks and its metagenome is largely unknown. Further, seasonal changes in microbial community responses to N addition have rarely been reported. In this study, three N fertilizer rates (0, 56, 196 kg N/ha) were applied annually in switchgrass (Panicum virgatum L.) grown for bioenergy production in the upper Midwest, USA. The soil microbiome was affected by both fertilizer and low pH in the 7th year of fertilization treatments. The microbial community structures were relatively stable during the growing season for each N fertilizer rate. However, the excess N fertilizer (196N) increased the seasonal variation of bacterial and fungal communities. Network analysis showed that the microbial interactions at the 196N treatment were more intense, with decreased bacteria-fungal interactions compared to 56N and 0N. Furthermore, this suggests that the microbial community became more sensitive to environmental change under the influence of long-term excess N fertilizer. Metagenomic analysis showed that the long-term excess N fertilizer promoted many metabolic processes, especially carbohydrate and amino acid related metabolism and Archaea mediated ammonia oxidation. However, N fertilizer also reduced many other traits, especially N 2 fixation and signal transduction, the latter of which may contribute to the decreased interactions between bacteria and fungi.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian-Motivated Probabilistic Model of Hurricane-Induced Multimechanism Flood Hazards

Multimechanism floods (MMFs) are caused by the simultaneous occurrence of more than one flood mechanism such as storm surge, precipitation, tides, and waves. MMFs can lead to more severe or differing impacts than single-mechanism floods. As a result, comprehensive risk assessments require the ability to assess the multivariate probabilistic behaviors of hazards from MMFs. Here this study introduces a novel Bayesian-motivated approach for the probabilistic assessment of hurricane-induced hazards from the combination of the surge, precipitation, tides, and river antecedent flow. A Bayesian network (BN) is developed to capture the physical (conditional) relationship between variables and facilitate the generation of a hazard curve for river discharge that captures the contributions from multiple flood drivers. A case study located along the Delaware River is used to illustrate the proposed approach. Five computationally efficient representative predictive models are developed to estimate the conditional distributions required for the BN as a means of demonstrating the overall framework. The predictive models used in this study act as placeholders and can be replaced with more sophisticated and high-fidelity models depending on the desired accuracy level. While the predictive models are intended to be representative and illustrative, the model performance is evaluated using three historical storms that affected the area. Overall, the proposed framework is shown to be transparent, effective, and adaptable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Numerical Solution of the Steady-State Network Flow Equations for a Non-Ideal Gas

Herein we formulate a steady-state network flow problem for non-ideal gas that relates injection rates and nodal pressures in the network to flows in pipes. For this problem, we present and prove a theorem on uniqueness of generalized solution for a broad class of non-ideal pressure-density relations that satisfy a monotonicity property. Further, we develop a Newton-Raphson algorithm for numerical solution of the steady-state problem, which is made possible by a systematic non-dimensionalization of the equations. The developed algorithm has been extensively tested on benchmark instances and shown to converge robustly to a generalized solution. Previous results [1]-[4], indicate that the steady-state network flow equations for an ideal gas are difficult to solve by the Newton-Raphson method because of its extreme sensitivity to the initial guess. In contrast, we find that non-dimensionalization of the steady-state problem is key to robust convergence of the Newton-Raphson method. We identify criteria based on the uniqueness of solutions under which the existence of a non-physical generalized solution found by a non-linear solver implies non-existence of a physical solution, i.e., infeasibility of the problem. Finally, we compare pressure and flow solutions based on ideal and non-ideal equations of state to demonstrate the need to apply the latter in practice. The solver developed in this article is open-source and is made available for both the academic and research communities as well as the industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) Using Passive Microwave and Infrared Data

Recent developments in “headline-making” deep neural networks (DNNs), specifically convolutional neural networks (CNNs), along with advancements in computational power, open great opportunities to integrate massive amounts of real-time observations to characterize spatiotemporal structures of surface precipitation. This study aims to develop a CNN algorithm, named Deep Neural Network High Spatiotemporal Resolution Precipitation Estimation (Deep-STEP), that ingests direct satellite passive microwave (PMW) brightness temperatures (Tbs) at emission and scattering frequencies combined with infrared (IR) Tbs from geostationary satellites and surface information to automatically extract geospatial features related to the precipitable clouds. These features allow the end-to-end Deep-STEP algorithm to instantaneously map surface precipitation intensities with a spatial resolution of 4 km. The main advantages of Deep-STEP, as compared to current state-of-the-art techniques, are 1) it learns and estimates complex precipitation systems directly from raw measurements in near–real time, 2) it uses the automatic spatial neighborhood feature extraction approach, and 3) it fuses coarse-resolution PMW footprints with IR images to reliably retrieve surface precipitation at a high spatial resolution. We anticipate our proposed DNN algorithm to be a starting point for more sophisticated and efficient precipitation retrieval systems in terms of accuracy, fine spatial pattern detection skills, and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

BNF Radar b1 Data Processing Report: Spring 2025

The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) User Facility supports atmospheric science through an integrated network of fixed and mobile observatories. These facilities collect continuous and campaign-based observations of atmospheric properties, with the goal of improving the representation of clouds, aerosols, precipitation, and radiation in Earth system models. The Bankhead National Forest (BNF) site, established as an ARM Mobile Facility (AMF) on 1 October 2024, is situated in a forested region of northern Alabama. Its strategic location in a southeastern U.S. environment characterized by complex terrain, diverse land cover, and frequent convective storms provides a valuable opportunity to examine coupled land-atmosphere processes under natural variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hands-On Computer Science: The Array of Things Experimental Urban Instrument

Chicago's Array of Things (AoT) project is aptly described as a technology experiment or a "smart city" prototype. The concept of such an extensible "instrument" arose within a larger translational research vision applying computer science and engineering research for the multidimensional benefit of people and communities in cities. The AoT project hypothesized that wireless intelligent sensor networks could enable both quantitative social science and urban monitoring while also stimulating youth interest in science and technology. Successful deployment of such sensor networks could provide open data from urban measurements not only in support of diverse research questions-in environmental dynamics, urban architecture, engineering, and social sciences-but also informing community groups and city planners. Further, the AoT project and its successor SAGE project are a computer science and engineering experiment, but its success is inextricably tied to community engagement and experiential education. Simply put, community acceptance is a prerequisite to installing and testing the instrument.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model

In this work, a hybrid method coupling a pseudo-potential lattice Boltzmann model (LBM) and a pore network model (PNM) to simulate drying in porous media is proposed. Based on the watershed method, the porous medium is firstly decomposed into pore regions. According to the liquid–vapor phase distribution at a given time, the pore regions are further divided into four pore types, namely two-phase pores where a liquid–vapor interface exists, buffer pores next to the two-phase pores, single-liquid and single-vapor phase pores. The pseudo-potential LBM is used in the two-phase and buffer pores to simulate liquid drying and track the movement of the interfaces, while the single-phase PNM simulations are conducted in the buffer and single-phase pores to simulate vapor or liquid flow. LBM and PNM are coupled in the buffer pores through exchange of boundary information. The hybrid method is applied to simulate liquid drying in a porous medium. The whole-domain LBM simulation is considered as the reference solution to validate the hybrid method. Liquid saturation variation during the drying process and detailed phase and pressure distributions obtained by the two methods match quite well, demonstrating the accuracy of the hybrid method. For the specific case studied, the hybrid method saves more than 60% computational time compared to the whole-domain LBM simulation. In addition, the speedup of the hybrid method becomes more significant for a larger computational domain. In summary, the hybrid method developed in this work combines the accuracy of LBM and the efficiency of PNM to simulate drying in porous media at pore scale and can lead to significant reduction of computation time, thus allowing the pore-scale consideration of drying in larger porous systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Stronger fertilization effects on aboveground versus belowground plant properties across nine U.S. grasslands

Abstract Increased nutrient inputs due to anthropogenic activity are expected to increase primary productivity across terrestrial ecosystems, but changes in allocation aboveground versus belowground with nutrient addition have different implications for soil carbon (C) storage. Thus, given that roots are major contributors to soil C storage, understanding belowground net primary productivity (BNPP) and biomass responses to changes in nutrient availability is essential to predicting carbon–climate feedbacks in the context of interacting global environmental changes. To address this knowledge gap, we tested whether a decade of nitrogen (N) and phosphorus (P) fertilization consistently influenced aboveground and belowground biomass and productivity at nine grassland sites spanning a wide range of climatic and edaphic conditions in the continental United States. Fertilization effects were strong aboveground, with both N and P addition stimulating aboveground biomass at nearly all sites (by 30% and 36%, respectively, on average). P addition consistently increased root production (by 15% on average), whereas other belowground responses to fertilization were more variable, ranging from positive to negative across sites. Site‐specific responses to P were not predicted by the measured covariates. Atmospheric N deposition mediated the effect of N fertilization on root biomass and turnover. Specifically, atmospheric N deposition was positively correlated with root turnover rates, and this relationship was amplified with N addition. Nitrogen addition increased root biomass at sites with low N deposition but decreased it at sites with high N deposition. Overall, these results suggest that the effects of nutrient supply on belowground plant properties are context dependent, particularly with regard to background N supply rates, demonstrating that site conditions must be considered when predicting how grassland ecosystems will respond to increased nutrient loading from anthropogenic activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Impact of artificial topological changes on flow and transport through fractured media due to mesh resolution

Abstract We performed a set of numerical simulations to characterize the interplay of fracture network topology, upscaling, and mesh refinement on flow and transport properties in fractured porous media. We generated a set of generic three-dimensional discrete fracture networks at various densities, where the radii of the fractures were sampled from a truncated power-law distribution, and whose parameters were loosely based on field site characterizations. We also considered five network densities, which were defined using a dimensionless version of density based on percolation theory. Once the networks were generated, we upscaled them into a single continuum model using the upscaled discrete fracture matrix model presented by Sweeney et al. (2019). We considered steady, isothermal pressure-driven flow through each domain and then simulated conservative, decaying, and adsorbing tracers using a pulse injection into the domain. For each simulation, we calculated the effective permeability and solute breakthrough curves as quantities of interest to compare between network realizations. We found that selecting a mesh resolution such that the global topology of the upscaled mesh matches the fracture network is essential. If the upscaled mesh has a connected pathway of fracture (higher permeability) cells but the fracture network does not, then the estimates for effective permeability and solute breakthrough will be incorrect. False connections cannot be eliminated entirely, but they can be managed by choosing appropriate mesh resolution and refinement for a given network. Adopting octree meshing to obtain sufficient levels of refinement leads to fewer computational cells (up to a 90% reduction in overall cell count) when compared to using a uniform resolution grid and can result in a more accurate continuum representation of the true fracture network.

58 GEOSCIENCES↗