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At least 1,153 records · Page 64

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Combining Force Fields and Neural Networks for an Accurate Representation of Chemically Diverse Molecular Interactions

A key goal of molecular modeling is the accurate reproduction of the true quantum mechanical potential energy of arbitrary molecular ensembles with a tractable classical approximation. The challenges are that analytical expressions found in general purpose force fields struggle to faithfully represent the intermolecular quantum potential energy surface at close distances and in strong interaction regimes; that the more accurate neural network approximations do not capture crucial physics concepts, e.g., nonadditive inductive contributions and application of electric fields; and that the ultra-accurate narrowly targeted models have difficulty generalizing to the entire chemical space. We therefore designed a hybrid wide-coverage intermolecular interaction model consisting of an analytically polarizable force field combined with a short-range neural network correction for the total intermolecular interaction energy. Here, we describe the methodology and apply the model to accurately determine the properties of water, the free energy of solvation of neutral and charged molecules, and the binding free energy of ligands to proteins. The correction is subtyped for distinct chemical species to match the underlying force field, to segment and reduce the amount of quantum training data, and to increase accuracy and computational speed. For the systems considered, the hybrid ab initio parametrized Hamiltonian reproduces the two-body dimer quantum mechanics (QM) energies to within 0.03 kcal/mol and the nonadditive many-molecule contributions to within 2%. Simulations of molecular systems using this interaction model run at speeds of several nanoseconds per day.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2016

Biomechanical Modeling of Split-leg Squat and Heel Raise on the Hybrid Ultimate Lifting Kit (HULK)

Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and musculoskeletal forces that occur during exercises performed on the prototype devices. Computational models currently use OpenSim software, an open source code for musculoskeletal modeling, with biomechanical input data from subjects for estimation of muscle and joint loads. Subjects are instrumented with reflective markers for motion capture data collection while exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device. Ground reaction force data is collected with force plates under the feet and device loading is recorded through load cells internal to the HULK. This data is input into the OpenSim biomechanical model, which has been scaled to match the anthropometrics of the test subject, to calculate the loads on the body. Multiple exercises are performed and evaluated during a test session such as a full squat, single leg squat, heel raise and dead lift. Variables for these exercises include applied device load, narrow or wide foot stance, slow or fast cadence and the harness or long bar interface between the test subject and the device. Data from free weights are compared to the resistively loaded exercise device. The focus of this presentation is to summarize the results from the single-leg squat and heel raise exercises performed during three sessions occurring in 2015. Differences in loading configuration, cadence and stance produce differences in kinematics, joint toques and force and muscle forces.

computational modeling↗

NASA NTRS1987

Analysis of typical fault-tolerant architectures using HARP

Difficulties encountered in the modeling of fault-tolerant systems are discussed. The Hybrid Automated Reliability Predictor (HARP) approach to modeling fault-tolerant systems is described. The HARP is written in FORTRAN, consists of nearly 30,000 lines of codes and comments, and is based on behavioral decomposition. Using the behavioral decomposition, the dependability model is divided into fault-occurrence/repair and fault/error-handling models; the characteristics and combining of these two models are examined. Examples in which the HARP is applied to the modeling of some typical fault-tolerant systems, including a local-area network, two fault-tolerant computer systems, and a flight control system, are presented.

Bavuso, Salvatore J.↗

DOE OSTI2021

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Quarkyonic matter equation of state in beta-equilibrium

Quark matter may appear due to a hadronic-quark transition in the core of a hybrid star. Quarkyonic matter is an approach in which both quarks and nucleons appear as quasiparticles in a crossover transition, and provides an explicit realization of early ideas concerning quark matter (e.g., the MIT bag model). This description has recently been employed by McLerran and Reddy to model chargeless (pure neutron) matter with an approach that has the virtue that the speed of sound rises quickly at a neutron-quark transition so as to satisfy observational constraints on the neutron star maximum mass (≳ 2 $M_⊙$) and the radius of a 1.4 $M_⊙$ star ($R_{1.4}$ ≲ 13.5 km). Traditional models involving first-order transitions result in softer pressure-energy density relations that have difficulty satisfying these constraints except with very narrow choices of parameters. Here, we propose a variation of quarkyonic matter involving protons and leptons whose energy can be explicitly minimized to achieve both chemical and beta equilibrium, which cannot be done in the chargeless formulation. Quarkyonic stellar models are able to satisfy observed mass and radius constraints with a wide range of model parameters, avoiding the obligatory fine-tuning of conventional hybrid star models, including requiring the transition density to be very close to the nuclear saturation density. Our formulation fits experimental and theoretical properties of the nuclear symmetry energy and pure neutron matter, and contains as few as three free parameters. This makes it an ideal tool for the study of high-density matter that is an efficient alternative to piecewise polytrope or spectral decomposition methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS2014

Development of a Microphone Phased Array Capability for the Langley 14- by 22-Foot Subsonic Tunnel

A new aeroacoustic measurement capability has been developed for use in open-jet testing in the NASA Langley 14- by 22-Foot Subsonic Tunnel (14x22 tunnel). A suite of instruments has been developed to characterize noise source strengths, locations, and directivity for both semi-span and full-span test articles in the facility. The primary instrument of the suite is a fully traversable microphone phased array for identification of noise source locations and strengths on models. The array can be mounted in the ceiling or on either side of the facility test section to accommodate various test article configurations. Complementing the phased array is an ensemble of streamwise traversing microphones that can be placed around the test section at defined locations to conduct noise source directivity studies along both flyover and sideline axes. A customized data acquisition system has been developed for the instrumentation suite that allows for command and control of all aspects of the array and microphone hardware, and is coupled with a comprehensive data reduction system to generate information in near real time. This information includes such items as time histories and spectral data for individual microphones and groups of microphones, contour presentations of noise source locations and strengths, and hemispherical directivity data. The data acquisition system integrates with the 14x22 tunnel data system to allow real time capture of facility parameters during acquisition of microphone data. The design of the phased array system has been vetted via a theoretical performance analysis based on conventional monopole beamforming and DAMAS deconvolution. The performance analysis provides the ability to compute figures of merit for the array as well as characterize factors such as beamwidths, sidelobe levels, and source discrimination for the types of noise sources anticipated in the 14x22 tunnel. The full paper will summarize in detail the design of the instrumentation suite, the construction of the hardware system, and the results of the performance analysis. Although the instrumentation suite is designed to characterize noise for a variety of test articles in the 14x22 tunnel, this paper will concentrate on description of the instruments for two specific test campaigns in the facility, namely a full-span NASA Hybrid Wing Body (HWB) model entry and a semi-span Gulfstream aircraft model entry, tested in the facility in the winter of 2012 and spring of 2013, respectively.

Humphreys, William M.↗

NASA NTRS1994

Development and initial test of the University of Wisconsin global isentropic-sigma model

The description of a global version of the University of Wisconsin (UW) hybrid isentropic-sigma (theta-sigma) model and the results from an initial numerical weather prediction experiment are presented in this paper. The main objectives of this initial test are to (1) discuss theta-sigma model development and computer requirements, (2) demonstrate the ability of the UW theta-sigma model for global numerical weather prediction using realistic orography and parameterized physical processes, and (3) compare the transport of an inert trace constituent against a nominally 'identical' sigma coordinate model. Initial and verifying data for the 5-day simulations presented in this work were supplied by the Goddard Earth Observing System (GEOS-1) data assimilation system. The time period studied is 1-6 February 1985. This validation experiment demonstrates that the global UW theta-sigma model produces a realistic 5-day simulation of the mass and momentum distributions when compared to both the identical sigma model and GEOS-1 verification. Root-mean-square errors demonstrate that the theta-sigma model is slightly more accurate than the nominally identical sigma model with respect to standard synoptic variables. Of particular importance, the UW theta-sigma model displays a distinct advantage over the conventional sigma model with respect to the prognostic simulation of inert trace constituent transport in amplifying baroclinic waves of the extratropics. This is especially true in the upper troposphere and stratosphere where the spatial integrity and conservation of an inert trace constituent is severely compromised in the sigma model compared to the theta-sigma model.

Zapotocny, Tom H.↗

NASA NTRS2011

Lateral-Directional Parameter Estimation on the X-48B Aircraft Using an Abstracted, Multi-Objective Effector Model

The problem of parameter estimation on hybrid-wing-body aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aerodynamic control effectors that act in coplanar motion. This adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of flight and simulation data must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, time-decorrelation techniques are applied to a model structure selected through stepwise regression for simulated and flight-generated lateral-directional parameter estimation data. A virtual effector model that uses mathematical abstractions to describe the multi-axis effects of clamshell surfaces is developed and applied. Comparisons are made between time history reconstructions and observed data in order to assess the accuracy of the regression model. The Cram r-Rao lower bounds of the estimated parameters are used to assess the uncertainty of the regression model relative to alternative models. Stepwise regression was found to be a useful technique for lateral-directional model design for hybrid-wing-body aircraft, as suggested by available flight data. Based on the results of this study, linear regression parameter estimation methods using abstracted effectors are expected to perform well for hybrid-wing-body aircraft properly equipped for the task.

Ratnayake, Nalin A.↗

DOE OSTI2021

Land Surface Modeling 2.0 for agricultural climate change impact assessments

Focus Area: This white paper addresses DOE AI4ESP focus area #2 by providing a sketch blueprint for a next generation, hybrid AI/process-based global Land Surface Modeling (LSM) framework to improve projections of climate change impacts on the land surface system including agriculture and the hydrological cycle. Science Challenge: Climate change impacts on agriculture are highly uncertain: -50% to +150% global production changes for major grains under high-end climate change, for example. The core challenge in land surface system predictability is the large gap between the scale at which the relevant biological processes act and the scale at which the risks need to be assessed. Conventional empirical and process-based modeling approaches are insufficient. A new multi-scale modeling paradigm which employs AI/ML to learn from new streams of remote sensing data and targeted ’gene-to-global’ simulations is needed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Velocity-Space Hybridization of Direct Simulation Monte Carlo and a Quasi-Particle Boltzmann Solver

This paper presents a new method for modeling rarefied gas flows based on hybridization of direct simulation Monte Carlo (DSMC) and discrete velocity method (DVM)-based quasi-particle representations of the velocity distribution function. It is aimed at improving the resolution of the tails of the distribution function (compared with DSMC) and computational efficiency (compared with DVM). Details of the method, such as the collision algorithm and the particle merging scheme, are discussed. The hybrid approach is applied to the study of noise in a Maxwellian distribution, computation of electron-impact ionization rate coefficient, as well as numerical simulation of a supersonic Couette flow. The hybrid-based solver is compared with pure DSMC and DVM approaches in terms of accuracy, computational speed, and memory use. It is shown that such a hybrid approach can provide a lower computational cost than a pure DVM approach, while being able to retain accuracy in modeling high-velocity tails of the distribution function. For problems where trace species have a significant impact on the flow physics, the proposed method is shown to be capable of providing better computational efficiency and accuracy compared with standard fixed-weight DSMC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Hybrid Natural Gas Geothermal Combined Cycle Power Plant Analysis

Low temperature geothermal resources, including those associated with oil and gas production, are an underutilized source of low carbon energy. The present work investigates coupling of low-temperature geothermal resources with concentrating solar and/or natural gas energy sources to increase the number of locations at which power generation from low temperature geothermal resources would be technically and economically viable. Stand-alone and hybrid geothermal power cycles are simulated using SimTech IPSEpro process modeling software. Design point strategies for a hybrid power cycle that may operate with either a single heat source or two simultaneous heat input sources are considered. Additionally, off-design power plant operation is investigated to consider the impacts of the heat source availability and ambient temperature variations. The off-design analysis utilizes a modeling tool that predicts power plant performance at each time step as a function of topping cycle heat input (from gas or solar), bottoming cycle heat input (from geothermal), and ambient temperature. Addition of a steam topping cycle to an organic Rankine cycle geothermal power plant provides opportunities to increase the efficiency and power output relative to a stand-alone geothermal power plant. Additionally, use of the waste heat from gas turbine power generation in a geothermal bottoming cycle provides opportunities to increase the amount of power generation associated with each unit of carbon dioxide emitted. This paper will describe the hybrid plant configuration evaluated, discuss the predicted power cycle performance, and compare with stand-alone natural gas and geothermal power generation cases. The power cycle investigated is expected to be applicable for use with conventional hydrothermal resources as well as with geologic thermal energy storage applications and/or enhanced geothermal systems. The steam topping cycle could use a concentrated solar heat source for fully renewable hybrid plant configuration. A plant initially constructed with a natural gas topping cycle heat source could be converted to a solar heat source part way though the power cycle operational life to achieve life cycle carbon emission reductions.

concentrating solar power↗

DOE OSTI2024

Impact of grain boundary and surface diffusion on predicted fission gas bubble behavior and release in UO 2 fuel

In this work, we quantify the impact of grain boundary (GB) and surface diffusion on fission gas bubble evolution and fission gas release in UO 2 nuclear fuel using simulations with a hybrid phase field/cluster dynamics model. Here, we begin with a comprehensive literature review of uranium vacancy and xenon atom diffusivity in UO 2 through the bulk, along GBs, and along surfaces. In our model we represent fast GB and surface diffusion using a heterogeneous diffusivity that is a function of the order parameters that represent bubbles and grains. We find that the GB diffusivity directly impacts the rate of gas release via GB transport, and that the GB diffusivity is likely below 104 times the lower value from Olander and van Uffelen. We also find that the surface diffusivity impacts bubble coalescence and mobility, and that the bubble surface diffusivity is likely below 10 -4 times the value from Zhou and Olander.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Policy support and technology development trajectory for renewable natural gas in the U.S.

Renewable natural gas (RNG) is a clean alternative to fossil natural gas, which can be used as transportation fuel, among other applications. This study projects the development trajectory of RNG and evaluates its impacts on the future U.S. transportation market using a hybrid computable general equilibrium model. This analysis considers various factors and uncertainties affecting RNG production, such as technology development, market conditions, competition with other advanced biofuels, and national and state policies. In 2050, RNG production will grow to 2.7 billion gallons (10 billion liters), mostly from swine manure, under current policy provisions. This will lead to a reduction in greenhouse gas (GHG) emissions by 58.56 million metric tonne of CO 2e in 2050. Analysis of different technology cases finds RNG from animal manure to be predominant, while RNG from corn stover and cellulosic ethanol are less competitive. Furthermore, a high mandatory target of 1 billion gallons will drive RNG production higher by 8–18 %, while an extended 2 nd -generation biofuel production tax credit will mostly increase cellulosic ethanol production. The model also finds RNG production being affected by uncertainties in market conditions, such as GDP growth, fossil fuel prices, and oil and gas supply.

Biomethane↗

DOE OSTI2019

Combining anaerobic digestion and hydrothermal liquefaction in the conversion of dairy waste into energy: A techno economic model for New York state

There is increasing global interest and policies being enacted to lower greenhouse gas emissions, especially from the agricultural sector. In the U.S. for example, states with large dairy operations may combine proven manure valorization technologies, such as anaerobic digestion and hydrothermal liquefaction. Sustainable manure treatment would increase the recovery of energy and other useful co-products, namely biogas, biocrude oil and hydro-char as well as lower the environmental impacts. In this study, the economic feasibility of implementing a centralized bioenergy system in New York State was investigated. Here, the feasibility of this transformation depends on many factors, including capital costs, discount rates, and other financing arrangements, electricity selling prices, incentives and farm sizes and locations. For a large-scale implementation in New York State accounting for nearly 50% of the state’s dairy farms, our model of a distributed, hybrid anaerobic/hydrothermal system was shown to treat 590 million liters of wet manure per day, producing 607 million kWh of electricity, 162,000 L of biocrude oil and 117,000 kg of hydro-char per day. Electricity selling price is a critical factor. Increasing the electricity selling price from wholesale ($\$0.06$ /kWh) to retail ($\$0.18$ kWh) increased the net present value from $\$395$ million to $1.5 billion (considering a 40-year project lifetime).

42 ENGINEERING↗

DOE OSTI2024

DNA Origami Incorporated into Solid-State Nanopores Enables Enhanced Sensitivity for Precise Analysis of Protein Translocations

The rapidly advancing field of nanotechnology is driving the development of precise sensing methods at the nanoscale, with solid-state nanopores emerging as promising tools for biomolecular sensing. Here, this study investigates the increased sensitivity of solid-state nanopores achieved by integrating DNA origami structures, leading to the improved analysis of protein translocations. Using holo human serum transferrin (holo-hSTf) as a model protein, we compared hybrid nanopores incorporating DNA origami with open solid-state nanopores. Results show a significant enhancement in holo-hSTf detection sensitivity with DNA origami integration, suggesting a unique role of DNA interactions beyond confinement. This approach holds potential for ultrasensitive protein detection in biosensing applications, offering advancements in biomedical research and diagnostic tool development for diseases with low-abundance protein biomarkers. Further exploration of origami designs and nanopore configurations promises even greater sensitivity and versatility in the detection of a wider range of proteins, paving the way for advanced biosensing technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Electrolyte-Guided Design of Electroreductive CO Coupling on Copper Surfaces

Engineering the interfacial distribution of electrolytic ions can aid in modulating the electrocatalyst performance and efficiency. Using a hybrid quantum-classical modeling approach, we describe how predictive tuning of the solution microenvironment on copper can enhance the efficiency of CO 2 reduction (CO 2 R) to C 2 products. We elucidate how competing electrolyte constituents in mixed electrolyte solutions stimulate restructuring of the electrochemical double layer (EDL) and stabilize the OCCO* dimer (* denotes surface adsorbed), with predictions validated in flow reactors using copper gas diffusion electrodes (Cu-GDEs). Furthermore, our findings highlight how molecular-scale electrolyte engineering with informed models of the EDL can be leveraged to tailor CO 2 R activity and selectivity toward C 2 products.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Coherent electrical control of a single high-spin nucleus in silicon

Nuclear spins are highly coherent quantum objects. In large ensembles, their control and detection via magnetic resonance is widely exploited, for example, in chemistry, medicine, materials science and mining. Nuclear spins also featured in early proposals for solid-state quantum computers and demonstrations of quantum search and factoring algorithms. Scaling up such concepts requires controlling individual nuclei, which can be detected when coupled to an electron. However, the need to address the nuclei via oscillating magnetic fields complicates their integration in multi-spin nanoscale devices, because the field cannot be localized or screened. Control via electric fields would resolve this problem, but previous methods relied on transducing electric signals into magnetic fields via the electron–nuclear hyperfine interaction, which severely affects nuclear coherence. In this work, we demonstrate the coherent quantum control of a single 123 Sb (spin-7/2) nucleus using localized electric fields produced within a silicon nanoelectronic device. The method exploits an idea proposed in 1961 but not previously realized experimentally with a single nucleus. Our results are quantitatively supported by a microscopic theoretical model that reveals how the purely electrical modulation of the nuclear electric quadrupole interaction results in coherent nuclear spin transitions that are uniquely addressable owing to lattice strain. The spin dephasing time, 0.1 seconds, is orders of magnitude longer than those obtained by methods that require a coupled electron spin to achieve electrical driving. These results show that high-spin quadrupolar nuclei could be deployed as chaotic models, strain sensors and hybrid spin-mechanical quantum systems using all-electrical controls. Integrating electrically controllable nuclei with quantum dots could pave the way to scalable, nuclear- and electron-spin-based quantum computers in silicon that operate without the need for oscillating magnetic fields.

42 ENGINEERING↗