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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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DOE OSTI2017

Systematic parameter inference in stochastic mesoscopic modeling

We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are “sparse”. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Memory-optimized shift operator alternating direction implicit finite difference time domain method for plasma

Through introducing the alternating direction implicit (ADI) technique and the memory-optimized algorithm to the shift operator (SO) finite difference time domain (FDTD) method, the memory-optimized SO-ADI FDTD for nonmagnetized collisional plasma is proposed and the corresponding formulae of the proposed method for programming are deduced. In order to further the computational efficiency, the iteration method rather than Gauss elimination method is employed to solve the equation set in the derivation of the formulae. Complicated transformations and convolutions are avoided in the proposed method compared with the Z transforms (ZT) ADI FDTD method and the piecewise linear JE recursive convolution (PLJERC) ADI FDTD method. The numerical dispersion of the SO-ADI FDTD method with different plasma frequencies and electron collision frequencies is analyzed and the appropriate ratio of grid size to the minimum wavelength is given. The accuracy of the proposed method is validated by the reflection coefficient test on a nonmagnetized collisional plasma sheet. The testing results show that the proposed method is advantageous for improving computational efficiency and saving computer memory. The reflection coefficient of a perfect electric conductor (PEC) sheet covered by multilayer plasma and the RCS of the objects coated by plasma are calculated by the proposed method and the simulation results are analyzed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows

In the present work, a general formulation is proposed to implement the contact angle boundary conditions for the second-order Phase-Field models, which is applicable to N-phase (N ≥ 2) moving contact line problems. To remedy the issue of mass change due to the contact angle boundary condition, a source term or Lagrange multiplier is added to the original second-order Phase-Field models, which is determined by the consistent and conservative volume distribution algorithm so that the summation of the order parameters and the consistency of reduction are not influenced. To physically couple the proposed formulation to the hydrodynamics, especially for large-density-ratio problems, the consistent formulation is employed. The reduction-consistent conservative Allen-Cahn models are chosen as examples to illustrate the application of the proposed formulation. The numerical scheme that preserves the consistency and conservation of the proposed formulation is employed to demonstrate its effectiveness. Results produced by the proposed formulation are in good agreement with the exact and/or asymptotic solutions. The proposed method captures complex dynamics of moving contact line problems having large density ratios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2019

Nuclear Data Sheets for A=266,270,274,278,282,286,290,294,298☆

Spectroscopic information such as production, identification, half-lives, decay modes and possible excited states for experimentally known nuclides of mass numbers 266, 270, 274, 278, 282, 286, 290, 294, and 298 are presented together with the recommended values, superseding information and data in the previous ENSDF and NDS evaluation by 2005Gu33. No nuclides have yet been identified for A=302. In the last 14 years, large amounts of new and definitive data on the superheavy nuclides (SHN) have become available, thus changing almost entirely the landscape of nuclear data in this mass region, as also indicated by a number of recent review articles: 2017Og01, 2016Ho09, 2016Ho06 (for fission barriers), 2015Og05, 2015Og07, 2015Mo25, 2015OgZX, 2013Th02, and 2011Og07. See 2016Ka49 for IUPAC technical discussions for the discovery of Z=117 (Ts), Z=115 (Mc), Z=113 (Nh), 2001Ka70 and 2003Ka71 for Z=110-112, 2011Ba54 for Z≥113, 2009Ba62 for Z=112, and 2016Ka50 for Z=118 (Og). A special issue of Nuclear Physics A444 (2015) is devoted to research on superheavy elements (SHE) with 27 articles. In particular, see article by 2015Ko20 on mass spectrometric searches for superheavy elements in terrestrial matter. See also Proceedings of Nobel Symposium NS160 ‘Chemistry and Physics of Heavy and Superheavy Elements’ published in Eur. Phys. Jour. Web of Conferences 131 (2016), in particular 2016UtZZ, 2016DmZZ and 2016HoZY. See also 2016DuZX for future prospects of discovery of elements beyond Z=118 A=266: {sup 266}Db, {sup 266}Sg, {sup 266}Bh, {sup 266}Hs and {sup 266}Mt are the experimentally identified nuclides with A=266. Identification of {sup 266}Lr from α decay of {sup 270}Db has been proposed by 2014Kh04 from experiments at GSI, but complete details and analyses of all the four decay chains reported in this experiment have not yet been published, and discussion in 2015Og05 review article considering their work from Dubna (2013Og04, 2011Og04) and from GSI (2014Kh04) still concluded that {sup 270}Db decayed dominantly by SF mode, in contrast with dominant α decay mode proposed by 2014Kh04. Experiments carried out at FLNR-JINR-Dubna in collaboration with LLNL and ORNL, GSI-SHIP facility, RIKEN, and LBNL facilities: {sup 266}Db from the α-decay of {sup 282}Nh in two correlated decay chains at Dubna; {sup 266}Sg as α-daughter of {sup 270}Hs in 12 correlated decay chains at Dubna and GSI; {sup 266}Bh in α decay of {sup 278}Nh in three correlated decay chains observed at RIKEN, and also directly with one event in {sup 249}Bk({sup 22}Ne,5n) at LBNL, and with four events in {sup 243}Am({sup 26}Mg,3n) at HIRFL-Lanzhou; {sup 266}Hs as α-daughter of {sup 270}Ds at GSI in two different experiments, six correlated decays in the first, and 25 decay chains in the second experiment, the analysis of which has not been fully reported as yet; and {sup 266}Mt directly in {sup 209}Bi({sup 58}Fe,n) reaction at GSI in two different experiments, observing three events in the first experiment and 12 events in the second, also produced in {sup 208}Pb({sup 59}Co,n) reaction at LBNL, observing five correlated decay chains. See also 2000Ho27 for discussion on {sup 266}Sg and {sup 266}Mt. A=270: {sup 270}Db, {sup 270}Bh, {sup 270}Hs, {sup 270}Mt and {sup 270}Ds are the experimentally identified nuclides with A=270 are presented. Experiments carried out at FLNR-JINR-Dubna in collaboration with LLNL and ORNL, GSI-SHIP facility, and RIKEN: {sup 270}Db from the decay of {sup 294}Ts in six correlated decay chains at Dubna and GSI; {sup 270}Bh as α great-granddaughter of {sup 282}Nh in two correlated decay chains observed at Dubna; {sup 270}Hs directly in {sup 248}Cm({sup 26}Mg,4n) reaction at GSI, and in {sup 226}Ra({sup 48}Ca,4n) reaction at Dubna; {sup 270}Mt as granddaughter of {sup 278}Ts in three correlated decay chains at RIKEN; and {sup 270}Ds directly in {sup 207}Pb({sup 64}Ni,n) at GSI in 33 correlated decay chains. A=274: {sup 274}Bh, {sup 274}Mt and {sup 274}Rg are the experimentally identified nuclides with A=274. Search for {sup 274}Ds in {sup 238}U({sup 40}Ar, 4n),E=5.7 MeV/nucleon reaction and subsequent α decays at GSI (1990Sc11) proved negative. Experiments carried out at FLNR-JINR-Dubna in collaboration with LLNL and ORNL, GSI-SHIP facility, and RIKEN: {sup 274}Bh from the decay of {sup 294}Ts in six correlated decay chains at Dubna and GSI, {sup 274}Mt produced as α daughter of {sup 282}Nh in two correlated decay chains observed at Dubna, and {sup 274}Rg as α daughter of {sup 278}Nh produced in three correlated decay chains at RIKEN. A=278: {sup 278}Mt, {sup 278}Rg and {sup 278}Nh are the experimentally identified nuclides with A=278. A very tentative evidence is provided for {sup 278}Bh and {sup 278}Hs from a chain originally observed and assigned to {sup 289}Fl by 1999Og10, but later reassigned by 2004Og10 to {sup 290}Fl, and further discussed in detail by 2016Ho09, where, based on systematics of α decays and SF half-lives, {sup 290}Fl is proposed to decay via ε mode to {sup 290}Nh, which then decays by an α chain, ending in {sup 278}Bh that decays by SF mode. Experiments carried out at FLNR-JINR-Dubna in collaboration with LLNL and ORNL labs in the USA, GSI-SHIP facility, and RIKEN: {sup 278}Mt from the decay of {sup 294}Ts in six correlated decay chains at Dubna and GSI, {sup 278}Rg produced as α daughter of {sup 282}Nh in two correlated decay chains observed at Dubna, and {sup 278}Nh produced directly in three correlated decay chains at RIKEN. A=282: {sup 282}Rg, {sup 282}Cn and {sup 282}Nh are the experimentally identified nuclides with A=282. A very tentative evidence is provided for {sup 282}Mt and {sup 282}Ds from a chain originally observed and assigned to {sup 289}Fl by 1999Og10, but later reassigned by 2004Og10 to {sup 290}Fl, and further discussed in detail by 2016Ho09, where, based on systematics of α decays and SF half-lives, {sup 290}Fl is proposed to decay via ε mode to {sup 290}Nh, which then decays by an α chain, ending in {sup 278}Bh that decays by SF mode. Experiments carried out at FLNR-Dubna, in collaboration with LLNL and ORNL labs in the USA, and at GSI-SHIP facility confirm the identification of these isotopes, {sup 282}Rg in α decay chain of {sup 294}Ts in six correlated decay chains at Dubna and GSI, {sup 282}Cn produced in four ways at Dubna: independently in one correlated decay chain, as α daughter of {sup 286}Lv in 11 correlated decay chains, as α grand-daughter of {sup 290}Lv in 12 correlated decay chains, as α great-grand daughter of {sup 294}Og in four correlated decay chains, and {sup 282}Nh produced at Dubna directly in two correlated decay chains. A=286: {sup 286}Nh and {sup 286}Fl are the only experimentally identified nuclides with A=286. A very tentative evidence is provided for {sup 286}Rg and {sup 286}Cn from a chain originally observed and assigned to {sup 289}Fl by 1999Og10, but later reassigned by 2004Og10 to {sup 290}Fl, and further discussed in detail by 2016Ho09, where, based on systematics of α decays and SF half-lives, {sup 290}Fl is proposed to decay via ε mode to {sup 290}Nh, which then decays by an α chain, ending in {sup 278}Bh that decays by SF mode. 2017Ka66 in experiments at RIKEN using GARIS separator interpret one correlated decay chain in three different ways, one involving possible production of {sup 294}Lv in {sup 248}Cm({sup 48}Ca,2n),E=261.6 MeV, and α decay to {sup 290}Fl which further decays to {sup 286}Cn. Experiments carried out at FLNR-Dubna, in collaboration with LLNL and ORNL labs in the USA, and at GSI-SHIP facility confirm the identification of these isotopes, {sup 286}Nh as α grand-daughter of {sup 294}Ts in six correlated decay chains, and {sup 286}Fl produced in three ways: independently in 11 correlated decay chains, as α daughter of {sup 290}Lv in 12 correlated decay chains, and as α grand-daughter of {sup 294}Og in four correlated decay chains. A=290: {sup 290}Mc and {sup 290}Lv are the only experimentally identified nuclides with A=290. A tentative evidence is provided for {sup 290}Nh and {sup 290}Fl from a chain originally observed and assigned to {sup 289}Fl by 1999Og10, but later reassigned by 2004Og10 to {sup 290}Fl, and further discussed in detail by 2016Ho09, where, based on systematics of α decays and SF half-lives, {sup 290}Fl is proposed to decay via ε mode to {sup 290}Nh, which then decays by an α chain, ending in {sup 278}Bh that decays by SF mode. 2017Ka66 in experiments at RIKEN using GARIS separator interpret one correlated decay chain in three different ways, one involving possible production of {sup 294}Lv in {sup 248}Cm({sup 48}Ca,2n),E=261.6 MeV, and α decay to {sup 290}Fl. Experiments carried out at FLNR-Dubna, in collaboration with LLNL and ORNL labs in the USA, and at GSI-SHIP facility confirm the identification of these isotopes, {sup 290}Mc as α daughter of {sup 294}Ts in six correlated decay chains, and {sup 290}Lv produced in two ways, independently in 14 correlated decay chains, and as α daughter of {sup 294}Og in four correlated decay chains. A=294: {sup 294}Ts and {sup 294}Og are the only experimentally identified nuclides with A=294. Experiments carried out at FLNR-Dubna, in collaboration with LLNL and ORNL labs in the USA, and at GSI-SHIP facility confirm the identification of these isotopes, with a total of six EVR-α-SF correlated decay chains observed for {sup 294}Ts and four for {sup 294}Og. Tentative identification of {sup 294}Lv is provided by 2017Ka66 from the observation one correlated event using GARIS-RIKEN facility, where this event is interpreted in three possible ways, two interpretations lead to production of {sup 293}Lv and successive odd-A nuclides of {sup 289}Fl, {sup 285}Cn and {sup 281}Ds, whereas the third prediction starts with the production of {sup 294}Lv and successive {sup 290}Fl and {sup 286}Cn nuclides. Search for {sup 294}Rg in natural gold materials (2011De03), and for {sup 294}Ds, {sup 294}Fl and {sup 294}Mc in natural Pt, Pb and Bi samples (2011De21) using accelerator mass spectroscopy (AMS) proved negative, with extremely low upper limits established. Also 1980St05 did not see any evidence for {sup 294}Ds in natural Pt sample using AMS. A=298: Search for {sup 298}120 through fusion-evaporation reaction at Dubna, and for {sup 298}Fl and {sup 298}Mc in natural Pt, Pb and Bi samples using accelerator mass spectroscopy (AMS) proved negative. Tentative assignment of three α-α-α decay chains by 2016Ho09 to {sup 299}120 in {sup 248}Cm({sup 54}Cr,3n){sup 299}120 at GSI was refuted by 2017He11, assigning these correlations to random events, instead. A=302: Fluorescent x rays from Z=120 element were observed by 2012Fr03 from compound nucleus {sup 302}120 produced in {sup 238}U({sup 64}Ni,X),E=6.6 MeV/nucleon at GANIL, and x-ray yields were measured, together with minimum average time deduced from x-ray multiplicity. The compound nucleus could decay by 3n- or 4n-channels to produce {sup 299}120 or {sup 298}120. 2012He05 and 2016Ho09 also produced {sup 302}120 compound nucleus in {sup 248}Cm({sup 54}Cr, xn){sup 302}120{sup *},E=6.035 MeV/nucleon; and {sup 238}U({sup 64}Ni,xn){sup 302}120{sup *},E=5.53 MeV/nucleon reactions using UNILAC at GSI. Three α-α-α correlated decays from {sup 248}Cm({sup 54}Cr,xn){sup 302}120{sup *} reaction were tentatively assigned by 2016Ho09 to {sup 299}120 decay, however, a detailed analysis by 2017He11 refuted this claim, ascribing these events to random sequences. There are no data tables for A=302.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

DeepONet-grid-UQ: A trustworthy deep operator framework for predicting the power grid’s post-fault trajectories

This paper proposes a novel data-driven method for the reliable prediction of the power grid’s post-fault trajectories, i.e., the power grid’s dynamic response after a disturbance or fault. Here, the proposed method is based on the recently proposed concept of Deep Operator Networks (DeepONets). Unlike traditional neural networks that learn to approximate functions, DeepONets are designed to approximate nonlinear operators, i.e., mappings between infinite-dimensional spaces. Under this operator framework, we design a novel and efficient DeepONet that (i) takes as inputs the trajectories collected before and during the fault and (ii) outputs the predicted post-fault trajectories. In addition, we endow our method with the much-needed ability to balance efficiency with reliable/trustworthy predictions via uncertainty quantification. To this end, we propose and compare two novel methods that enable quantifying the predictive uncertainty. First, we propose a Bayesian DeepONet (B-DeepONet) that uses stochastic gradient Hamiltonian Monte-Carlo to sample from the posterior distribution of the DeepONet trainable parameters. Then, we design a Probabilistic DeepONet (Prob-DeepONet) that uses a probabilistic training strategy to enable quantifying uncertainty at virtually no extra computational cost. Finally, we validate the proposed methods’ predictive power and uncertainty quantification capability using the New York-New England power grid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗
DOE OSTI2019

A Real-Time Vehicle-Specific Eco-Routing Model for On-Board Navigation Applications Capturing Transient Vehicle Behavior

The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Acoustic emission Bayesian source location: Onset time challenge

Robust identification of the most accurate observed input data among a pool of observations is key in modeling and decision making. A statistically biased observed measurement deteriorates the predictive power of a model and affects decision-making ability based on the prediction of the model. When two competing methods of measurement are available, such as methods which identify arrival times in acoustic emission (AE) signals, a principal question is whether one of the two obtained datasets, or a combination of the two, should be used later on, for example, to localize an AE source. This question becomes more important when collecting not repeatable data such as AE signals created by a propagating crack. Here this paper considers an inverse source location problem in a concrete block to address the mentioned issue, a proposed methodology which also has wider application in competitive data selection. Elastic energy released by an AE event, such as a propagating crack, is recorded by acoustic emission data acquisition system. The onset time of AE signals is often used to locate the source of the event, and its accuracy directly affects the precision of source identification. This research proposes an innovative approach to select the most probable onset time obtained from two automatic picker methods. The proposed method selects the most probable onset times, which are observed by each picker for each sensor, in a probabilistic fashion. To validate the proposed method, the most accurate onset time observed by each picker is identified by visual inspection and is compared with the one is selected by the proposed method. Finally, the dataset is used for source location identification. Results show that picked onset times determined by the proposed method generate more accurate source identification when compared with coordinates obtained using each dataset individually.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Energy Transduction in Nitrogenase

Nitrogenase is a complicated two-component enzyme system that uses ATP binding and hydrolysis energy to achieve one of the most difficult chemical reactions in nature, the reduction of N2 to NH3. One component of the Mo-based nitrogenase system, Fe protein, delivers electrons one at a time to the second component, the catalytic MoFe protein. This process occurs through a series of synchronized events collectively called the “Fe protein cycle”. Elucidating details of the events associated with this cycle has constituted an important challenge in understanding the nitrogenase mechanism. Electron delivery is a multistep process involving three metal clusters with intra- and interprotein events. It is proposed that the first electron transfer event is a gated intraprotein transfer of one electron from the MoFe protein P-cluster to the FeMo cofactor. Measurement of the effect of osmotic pressure on the rate of this electron transfer process revealed that it is gated by protein conformational changes. This first electron transfer is activated by binding of the Fe protein containing two bound ATP molecules. The mechanism of how this protein-protein association triggers electron transfer remains unknown. The second electron transfer event is proposed to be a rapid interprotein “backfill” with transfer of one electron from the reduced Fe protein 4Fe-4S cluster to the oxidized P-cluster. In this way, electron delivery can be viewed as a case of “deficit spending”. Such a deficit-spending electron transfer process can be envisioned as a way to achieve one-direction electron flow, limiting the potential for back electron flow. Hydrolysis of two ATP molecules associated with the Fe protein occurs after the electron transfer and therefore is not used to directly drive the electron transfer. Rather, ATP hydrolysis is proposed to contribute to relaxation of the “activated” conformational state associated with the ATP form of the complex, with the free energy from ATP hydrolysis being used to pay back energy associated with component protein association and electron transfer. Release of inorganic phosphate (Pi) and protein-protein dissociation follow electron transfer and ATP hydrolysis. The ratelimiting step for the Fe protein cycle is not dissociation of the two proteins, as previously believed, but rather is release of Pi after ATP hydrolysis, which is then followed by rapid protein-protein complex dissociation. Nitrogenase is composed of two catalytic halves that do not function independently but rather exhibit anticooperative nuclear motion in which electron transfer in one-half of the complex partially inhibits electron transfer and ATP hydrolysis in the other half. Calculations indicated the existence of anticooperative interactions across the entire nitrogenase complex, suggesting a mechanism for the control of events on opposite ends of this large complex. The mechanistic necessity for this anticooperative process remains unknown. This Account presents a working model for how all of these processes work together in the nitrogenase “machine” to transduce the energy from ATP binding and hydrolysis to drive N2 reduction.

Seefeldt, Lance C.↗
DOE OSTI2020

Gas Production from Hot Water Circulation through Hydraulic Fractures in Methane Hydrate-Bearing Sediments: THC-Coupled Simulation of Production Mechanisms

Methane hydrates, widely found in permafrost and deep marine sediments, have great potential as a future energy 6 source. Conventional production schemes perform poorly for challenging hydrate reservoirs with low permeability. We propose an 7 efficient production scheme by combining hydraulic fracturing from horizontal wells and hot water circulation through fractures. A 8 fully coupled thermo-hydro-chemical (THC) model is developed to simulate the key physical processes during gas production from 9 a hydrate reservoir representative of typical geological settings in Shenhu, South China Sea. We found that the gas production 10 process has two distinct stages divided by thermal breakthrough: a relatively short prebreakthrough stage and a postbreakthrough 11 stage yielding stable gas production. Heat advection along and near the hydraulic fracture dominates the prebreakthrough stage, 12 whereas conduction-driven thermal recovery in the volume around fractures dominates the postbreakthrough stage. We identified 13 that the steady-state injection temperature has a strong effect on the performance of the proposed scheme while the fluid mass 14 circulation rate has a moderate impact beyond a threshold. The proposed scheme proves to be efficient and robust over a range of 15 reservoir conditions with respect to initial hydrate saturation and intrinsic permeability, including their spatial heterogeneities, 16 thereby offering a promising solution for challenging reservoir conditions.

gas hydrate, fracture flow, gas production, numeri↗
DOE OSTI2021

Multiobjective Modular Biorefinery Configuration under Uncertainty

With increasing interest in using biomass as a raw material for fuel, the development of biorefineries is an active area of research. However, increasing market competition, uncertainty, and environmental concerns are a few of the challenges that need to be addressed. In this work, mathematical optimization formulations are proposed to address these challenges simultaneously. A multiobjective deterministic optimization framework is proposed to address economic and environmental objectives. A two-stage stochastic optimization framework is proposed to account for uncertainties in material flow and product yields. To model the environmental objective, an environmental risk metric is proposed. Finally, as modularization is gaining popularity in the process industry due to its ability to save capital cost because of standardization and reduce time to market, a formulation for simultaneously achieving modular process design and biorefinery configuration is proposed. Furthermore, the results demonstrate the efficacy of the proposed approach.

09 BIOMASS FUELS↗
DOE OSTI2017

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comes at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.

IDREAM, K-Edge, theoretical, computational, electr↗
DOE OSTI2018

Short-Term Precipitation Forecast Based on the PERSIANN System and LSTM Recurrent Neural Networks

Short-term Quantitative Precipitation Forecasting is important for flood forecasting, early flood warning, and natural hazard management. This study proposes a precipitation forecast model by extrapolating Cloud-Top Brightness Temperature (CTBT) using advanced Deep Neural Networks, and applying the forecasted CTBT into an effective rainfall retrieval algorithm to obtain the Short-term Quantitative Precipitation Forecasting (0–6 hr). To achieve such tasks, we propose a Long Short-Term Memory (LSTM) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), respectively. The precipitation forecasts obtained from our proposed framework, (i.e., LSTM combined with PERSIANN) are compared with a Recurrent Neural Network (RNN), Persistency method, and Farneback optical flow each combined with PERSIANN algorithm and the numerical model results from the first version of Rapid Refresh (RAPv1.0) over three regions in the United States, including the states of Oregon, Oklahoma, and Florida. We report our experiments indicate better statistics, such as correlation coefficient and root-mean-square error, for the CTBT forecasts from the proposed LSTM compared to the RNN, Persistency, and the Farneback method. The precipitation forecasts from the proposed LSTM and PERSIANN framework has demonstrated better statistics compared to the RAPv1.0 numerical forecasts and PERSIANN estimations from RNN, Persistency, and Farneback projections in terms of Probability of Detection, False Alarm Ratio, Critical Success Index, correlation coefficient, and root-mean-square error, especially in predicting the convective rainfalls. The proposed method shows superior capabilities in short-term forecasting over compared methods, and has the potential to be implemented globally as an alternative short-term forecast product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Novel Relative Permeability Model for Gas and Water Flow in Hydrate-Bearing Sediments With Laboratory and Field-Scale Application

In a producing gas hydrate reservoir the effective porosity available for fluid flow constantly changes with dissociation of gas hydrate. Therefore, accurate prediction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) that were developed for conventional oil and gas reservoirs would require empirical parameters to be calibrated at various S h over its range of variation, but such calibrations are precluded because of lack of experimental relative permeability data. This study proposes a new relative permeability model for gas hydrate-bearing media that is a function of maximum capillary pressure, capillary entry pressure, pore size distribution index, residual saturations, hydrate saturation, and four other constants. The three novel features of the proposed model are: (i) requires fitting its six empirical parameters only once using experimental data from any single S h , and the same set of empirical parameters predict relative permeability at all S h , (ii) includes the effect of capillarity, and (iii) includes the effect of pore-size distribution. From practical standpoint, the model can be used to simulate multiphase flow in gas hydrate-bearing sediments where the proposed relative permeability can account for the evolving hydrate saturation. The proposed model is implemented in a numerical simulator and the wall time required to perform simulations using the proposed model is shown to be similar to the time it takes to run same simulations with the B-C model. The proposed model is a step forward towards achieving the goal of physically accurate modeling of multiphase flow for gas hydrate-bearing sediments that accounts for the effect of gas hydrate saturation change on relative permeability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Three‐phase probabilistic load flow in radial and meshed distribution networks

With the introduction of higher levels of renewables and demand response programs, traditional deterministic power system tools fall short of expectations. Probabilistic load flow (PLF) takes into account the inconsistency or the unknown loads, and generation in the fundamental load flow analysis. This study proposes a PLF solution for both balanced and unbalanced, radial and weakly meshed networks without explicitly using the Y ‐bus matrix. It allows for discrete probability density functions as input variables without having to assume a predefined distribution. The nodal voltages and the power flows can be calculated independently from one another. The proposed method is applied to the IEEE 123 Node Test Feeder and the IEEE 13 Node Test Feeder in both its original radial configuration and a modified mesh configuration, including a load replaced with a wind turbine. The results are validated by comparison of the proposed method's solutions to those obtained using cumulants and Monte Carlo simulation. The proposed PLF method provides an accurate and practical way for finding the solution to stochastic problems occurring in power distribution systems allowing for real‐system data to be analysed.

Melhorn, Alexander C.↗
DOE OSTI2019

Hybrid approach for estimating dynamic states of synchronous generators

Accurate and robust estimation of dynamic states is essential for monitoring and controlling a power grid. To achieve higher accuracy and robustness, this paper proposes a hybrid estimation approach to estimate the dynamic states of synchronous generators using multiple models, classified into a set of local models and a wide-area model. The proposed estimation approach, first, estimates dynamic states using the extended Kalman filter (EKF) based on a local model and a wide-area model. Then, at each time step, probability indexes, which quantify the likelihood of both local and wide-area models, are determined using hypothesis testing based on measurements innovations. Finally, using the weighted fix approach, the states estimated from the local and wide-area models are combined based on their probability indexes. Simulation studies using the two-area four-machine system shows that the proposed approach can improve estimation accuracy and increase robustness against model errors.

Akhlaghi, Shahrokh↗