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DOE OSTI2021

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Effects of emitting surfaces and trapped ions on the sheath physics and current flow in multidimensional plasma systems

Recent one-dimensional simulations of planar sheaths with strong electron emission have shown that trapping of charge-exchange ions causes transitions from space-charge limited (SCL) to inverse sheaths. However, multidimensional emitting sheath phenomena with collisions remained unexplored, due in part to high computational cost. As such, we developed a novel continuum kinetic code to study the sheath physics, current flow and potential distributions in two-dimensional unmagnetized configurations with emitting surfaces. For small negatively biased thermionic cathodes in a plasma, the cathode sheath can exist in an equilibrium SCL state. The SCL sheath carries an immense density of trapped ions, neutralized by thermoelectrons, within the potential well of the virtual cathode. For further increases of emitted flux, the trapped ion cloud expands in space. The trapped ion space charge causes an increase of thermionic current far beyond the saturation limit predicted by conventional collisionless SCL sheath models without ion trapping. For sufficiently strong emission, the trapped ion cloud consumes the entire 2D plasma domain, forming a mode with globally confined ions and an inverse sheath at the cathode. In situations where the emitted flux is fixed and the bias is swept (e.g. emissive probe), the trapped ions cause a large thermionic current to escape for all biases below the plasma potential. Strong suppression of the thermionic emission, required for the probe to float, only occurs when the probe is above the plasma potential.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

MEASUREMENT OF THE GENERALIZED POLARIZABILITIES OF THE PROTON IN VIRTUAL COMPTON SCATTERING

Understanding how the visible matter in the universe arises from its elementary quark and gluon constituents is a central question for science. The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton?s bound state. A fundamental property of the proton involves the system?s response to an external electromagnetic (EM) field. It is characterized by the EM polarizabilities that describe how easily the charge and magnetization distributions inside the system are distorted by the EM field. When the polarizabilities are generalized to finite momentum transfer, their Fourier transform can map out the spatial distribution of the polarization densities in a proton subject to an EM field. This thesis focuses on the measurement of the proton generalized polarizabilities (GPs) at low four-momentum transfer in experimental Hall C at the Thomas Jefferson National Accelerator Facility (TJNAF). Among the six independent GPs, we will focus on the electric (aE1) and the magnetic (bM1) scalar GPs. The GPs can be accessed through measurements of the Virtual Compton Scattering reaction, by replacing the incoming real photon of the Compton scattering process with a space-like virtual photon. The outgoing real photon provides the EM perturbation to the system. In addition, the Dispersion Relation Formalism is used for generalized polarizability extraction. In this work, the two scalar GPs measured with unprecedented precision, and the measurements help explore a momentum transfer region where an anomalous enhancement of the electric GP that contradicts the predictions of nuclear theory has been observed.

Li, Ruonan↗

DOE OSTI2024

Primordial black hole versus inflaton

We compare the dark matter (DM) production processes and its parameters space in the background of reheating obtained from two chief systems in the early Universe: the inflaton ϕ and the primordial black holes (PBHs). We concentrated on the mechanism where DMs are universally produced only from the PBH decay and the generation of the standard model plasma from both inflaton and PBHs. Whereas the distribution of primordial black holes behaves like dust, the inflaton phenomenology depends strongly on its equation of state after the inflationary phase, which in turn is conditioned by the nature of the potential V ( ϕ ) . Depending upon the initial mass and population of PBHs, a large range of DM mass is shown to be viable if reheating is controlled by PBHs itself. Inflaton-dominated reheating is observed to further widen such possibilities depending on the initial population of black holes and its mass as well as the coupling of the inflaton to the standard model sector. Published by the American Physical Society 2024

Haque, Md Riajul (ORCID:0000000266184899)↗

DOE OSTI2022

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Computational framework for modeling of physical process

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

Chen, Xiao↗

DOE OSTI2024

Computational framework for modeling of physical process

Chen, Xiao↗

DOE OSTI2017

Reconstructing Solar Particle Event Spectra from Absorbed Dose Measurements

The space radiation environment is unlike anything encountered on Earth. It is dominated by heavy charged particles consisting of highly charged nuclei with atomic numbers ranging from 1 to 94 with a broad spectrum of kinetic energies. The three primary sources of radiation in space are trapped belts, galactic cosmic rays (GCRs), and solar particle events (SPEs). The trapped belts are only of concern near bodies with significant magnetic fields and GCR flux spectra are relatively well-predicted using models that account for solar modulation of the local interstellar spectrum. SPEs, which consist primarily of protons, are difficult to predict in terms of occurrence, duration, intensity, and energy spectrum. SPE intensity and energy spectrum are critical for understanding radiation exposure-related health effects for astronauts in space. The National Aeronautics and Space Administration (NASA) relies on data from the Geostationary Operational Environmental Satellite series to characterize intensities and energy spectra of SPEs near Earth. However, these data are not applicable for spacecraft in other regions of the solar system. To understand the radiation environment for astronauts embarking on travel beyond Earth orbit, NASA is developing the Hybrid Electronic Radiation Assessor (HERA), a distributed dosimetry system that employs a solid-state silicon sensor coupled with the Timepix readout chip. The pixelated nature of the detector permits detailed analyses of charged particle tracks which can then be used to characterize the fluence spectrum at the location of the detector. Despite the unprecedented level of detail regarding the radiation environment provided by these detectors, knowledge of the fluence spectrum at all locations within the vehicle, specifically inside of astronauts, is desired to fully characterize the associated risk of harmful health effects. Radiation transport of the external radiation environment, considering the shielding provided by the vehicle, other objects, and even astronauts, is performed to obtain this information. Thus, there is clearly a need to define the external radiation environment using measurements performed within the vehicle. The number of detectors that may be flown as a part of HERA is limited by logistics, including data, power, and mass restrictions. The current configuration for crewed flight consists of six detectors at various locations with in the Orion Multi-Purpose Crew Vehicle (MPCV). It follows that an external proton fluence spectrum can be characterized with at most six energy bins with traditional matrix inversion techniques. The purpose of the present study is to determine whether it is possible to reconstruct SPE spectra with greater resolution from absorbed dose measurements and knowledge of the response of individual detectors using linear programming.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Chapter 8 - Testing and Evaluation Methods for Protection Systems

In this chapter, we aim to present different approaches used formally and informally for the evaluation of protection devices and schemes. This chapter starts with presenting background on generalization of protection device components, followed by the resources necessary to evaluate the protection system. Further, this chapter presents the different approaches used in protection evaluation with their advantages and disadvantages. Finally, this chapter closes by presenting background on technology readiness level metrics. Technology readiness level metric is borrowed from the space industry for the purpose of comparing the maturity of the different protection technologies and using the appropriate evaluation methods from the list presented here.

power systems↗

DOE OSTI2021

Visual Tool for Assessing Stability of DER Configurations on Three-Phase Radial Networks

Here, we present a method and tool for evaluating the placement of Distributed Energy Resources (DER) on distribution circuits in order to control voltages and power flows. Our previous work described Phasor-Based Control (PBC), a novel control framework where DERs inject real and reactive power to track voltage magnitude and phase angle targets. Here, we employ linearized power flow equations and integral controllers to develop a linear state space model for PBC acting on a three-phase unbalanced network. We use this model to evaluate whether a given inverter-based DER configuration admits a stable set of controller gains, which cannot be done by analyzing controllability nor by using the Lyapunov equation. Instead, we sample over a parameter space to identify a stable set of controller gains. Our stability analysis requires only a line impedance model and does not entail simulating the system or solving an optimization problem. We incorporate this assessment into a publicly available visualization tool and demonstrate three processes for evaluating many control configurations on the IEEE 123-node test feeder (123NF).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Soft Nanoconfinement Nucleates and Stabilizes Ultrasmall Amorphous Calcium Carbonate from Aggregation

Organisms use soft confinement structures, such as vesicles and compartments, to direct the nucleation of calcium carbonate (CaCO 3 ) and its subsequent processes during biomineralization. Despite recent efforts elucidating confinement’s effects on CaCO 3 polymorph selection, we still poorly understand how the size and distribution of CaCO 3 are controlled within soft confinement. Here, using a size-controlled nanoemulsions system made from isooctane, Span 80, Tween 80, and aqueous solutions, we studied CaCO 3 formation in soft confinement. Small angle X-ray scattering (SAXS) confirmed that a 72 nm aqueous core in nanoemulsions served as the confined space for CaCO 3 formation. Unlike the ~ 50 nm CaCO 3 particles that formed in the unconfined solution, small angle neutron scattering (SANS) and transmission electron microscope (TEM) showed that ultrasmall and amorphous calcium carbonate precipitated within soft confinement and did not exhibit any aggregation/coalescence of nanoparticles even after 24 hrs of reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Active–Source Seismic Tomography at the Brady Geothermal Field, Nevada, with Dense Nodal and Fiber–Optic Seismic Arrays

We deployed a dense seismic array to image the shallow structure in the injection area of the Brady Hot Springs geothermal power plant in western Nevada. The array was composed of 238 three-component, 5 Hz nodal instruments, 8700 m of distributed acoustic sensing (DAS) fiber-optic cable (FOC) installed horizontally in surface trenches, and 400 m of FOC installed vertically in a borehole. The geophone array had about 60 m instrument spacing in the target zone, whereas DAS channel separations were about 1 m with an averaging (gauge) length of 10 m. The acquisition systems provided 15 days of continuous records, including active-source and ambient noise signals. A large vibroseis truck was operated at 196 locations, exciting a swept-frequency signal from 5 to 80 Hz over 20 s using three vibration modes (vertical, longitudinal, and transverse), with three sweeps per mode at each site. Sweeps were repeated up to four times at each site during four different stages of power plant operation: normal operation, shutdown, high and oscillatory injection and production, and normal operation. After removal of the sweep signal from the raw data, the first P-wave arrivals were automatically picked using a combination of methods. Here, the travel times were then used to invert for the 3D P-wave velocity structure. Models with 100 m horizontal and 20–50 m vertical node spacing were obtained, covering an area 2000 m by 1300 m, with acceptable resolution extending to about 250 m below surface. The travel-time data were fit to a root mean square (rms) misfit of 31 ms, close to our estimated picking uncertainty. Lateral boundaries between high and low velocity zones agree relatively well with the location of local faults from previous studies, and low near-surface velocities are associated with faults and fumarole locations. A sharp increase in velocity from < 1500 to > 2000 m/s at approximately 50 m below the ground surface in many parts of the study area may indicate a shallower water table than expected for the region.

15 GEOTHERMAL ENERGY↗

DOE OSTI2016

Quantifying the impact of sub-grid surface wind variability on sea salt and dust emissions in CAM5

This paper evaluates the impact of sub-grid variability of surface wind on sea salt and dust emissions in the Community Atmosphere Model version 5 (CAM5). The basic strategy is to calculate emission fluxes multiple times, using different wind speed samples of a Weibull probability distribution derived from model-predicted grid-box mean quantities. In order to derive the Weibull distribution, the sub-grid standard deviation of surface wind speed is estimated by taking into account four mechanisms: turbulence under neutral and stable conditions, dry convective eddies, moist convective eddies over the ocean, and air motions induced by mesoscale systems and fine-scale topography over land. The contributions of turbulence and dry convective eddy are parameterized using schemes from the literature. Wind variabilities caused by moist convective eddies and fine-scale topography are estimated using empirical relationships derived from an operational weather analysis data set at 15 km resolution. The estimated sub-grid standard deviations of surface wind speed agree well with reference results derived from 1 year of global weather analysis at 15 km resolution and from two regional model simulations with 3 km grid spacing. The wind-distribution-based emission calculations are implemented in CAM5. In terms of computational cost, the increase in total simulation time turns out to be less than 3 %. Simulations at 2° resolution indicate that sub-grid wind variability has relatively small impacts (about 7 % increase) on the global annual mean emission of sea salt aerosols, but considerable influence on the emission of dust. Among the considered mechanisms, dry convective eddies and mesoscale flows associated with topography are major causes of dust emission enhancement. With all the four mechanisms included and without additional adjustment of uncertain parameters in the model, the simulated global and annual mean dust emission increase by about 50 % compared to the default model. By tuning the globally constant dust emission scale factor, the global annual mean dust emission, aerosol optical depth, and top-of-atmosphere radiative fluxes can be adjusted to the level of the default model, but the frequency distribution of dust emission changes, with more contribution from weaker wind events and less contribution from stronger wind events. Lastly, in Africa and Asia, the overall frequencies of occurrence of dust emissions increase, and the seasonal variations are enhanced, while the geographical patterns of the emission frequency show little change.

58 GEOSCIENCES↗

DOE OSTI2016

Fullrmc, a rigid body reverse monte carlo modeling package enabled with machine learning and artificial intelligence

Here, a new Reverse Monte Carlo (RMC) package ‘fullrmc’ for atomic or rigid body and molecular, amorphous or crystalline materials is presented. fullrmc main purpose is to provide a fully modular, fast and flexible software, thoroughly documented, complex molecules enabled, written in a modern programming language (python, cython ,C and C++ when performance is needed) and complying to modern programming practices. fullrmc approach in solving an atomic or molecular structure is different from existing RMC algorithms and software. In a nutshell, traditional RMC methods and software randomly adjust atom positions until the whole system has the greatest consistency with a set of experimental data. In contrast, fullrmc applies smart moves endorsed with reinforcement machine learning to groups of atoms. While fullrmc allows running traditional RMC modelling, the uniqueness of this approach resides in its ability to customize grouping atoms in any convenient way with no additional programming efforts and to apply smart and more physically meaningful moves to the defined groups of atoms. Also fullrmc provides a unique way with almost no additional computational cost to recur a group’s selection, allowing the system to go out of local minimas by refining a group’s position or exploring through and beyond not allowed positions and energy barriers the unrestricted three dimensional space around a group.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Predicting the Galactic population of free-floating planets from realistic initial conditions

ABSTRACT We present the first prediction for the mass distribution function of Galactic free-floating planets (FFPs) that aims to accurately include the relative contributions of multiple formation pathways and stellar populations. We derive our predicted distribution from dedicated simulations of planet birth, growth, migration, and ejection around circumbinary systems and extend these results to also include the contributions from single and wide binary systems. Our resulting FFP mass distribution shows several distinct features, including a strong peak at ${\sim} 8\, {\rm M}_{\oplus }$ arising from the transition between pebble and gas accretion regimes and a trough at ${\sim} 1\, {\rm M}_{\oplus }$ due to the shift in the dominant ejection process from planet–planet scattering to ejection through interactions with stars in circumbinary systems. We find that interactions with the central binary in close circumbinary systems are likely the dominant progenitor for FFPs more massive than Earth, leading to a steep power-law dependence in mass that agrees well with existing observations. In contrast, we find planet–planet scattering events in single and wide binary systems likely produce the majority of planets at Mars mass and below, resulting in a shallower power-law dependence. Our results suggest that existing extrapolations into the sub-terrestrial mass range may significantly overestimate the true FFP abundance. The features we predict in the mass distribution of FFPs will be detectable by upcoming space-based microlensing surveys and, if observed, will provide key insight into the origins of FFPs and the environments in which they form.

Coleman, Gavin A. L. (ORCID:0000000151118963)↗

DOE OSTI2021

Quantifying Risk in an Uncertain Future: The Evolution of Resource Adequacy

As our power grids transition toward a decarbonized energy mix, ensuring reliability and provision of grid services remains paramount. The power system has always been heavily influenced by the weather - extreme temperatures determine the timing of peak demand, winter cold snaps can limit natural gas supply, gas turbine reliability and output are affected by ambient conditions, and hydro output varies seasonally and annually. However, as the grid increasingly relies on variable renewable energy (VRE), like wind and solar, the attention to reliability and weather conditions is increasingly important. The implications of changing reliability are large. The Electric Reliability Council of Texas (ERCOT) rolling blackouts from earlier this year impacted millions of people across the state and could be seen from space (Figure 1).

hydraulic turbines↗

DOE OSTI2026

Reconstruction of Six-Dimensional Phase Space

A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.

Shirlee, Jermain [Fermilab]↗

DOE OSTI2023

Scrambling and quantum chaos indicators from long-time properties of operator distributions

Scrambling is a key concept in the analysis of nonequilibrium properties of quantum many-body systems. Most studies focus on its characterization via out-of-time-ordered correlation (OTOC) functions, particularly through the early-time decay of the OTOC. However, scrambling is a complex process which involves operator spreading and operator entanglement, and a full characterization requires one to access more refined information on the operator dynamics at several timescales. In this work we analyze operator scrambling by expanding the target operator in a complete basis and studying the structure of the expansion coefficients treated as a coarse-grained probability distribution in the space of operators. Here we study different features of this distribution, such as its mean, variance, and participation ratio, for the Ising model with longitudinal and transverse fields, kicked collective spin models, and random circuit models. We show that the long-time properties of the operator distribution display common features across these cases and discuss how these properties can be used as a proxy for the onset of quantum chaos. Finally, we discuss the connection with OTOCs and analyze the cost of probing the operator distribution experimentally using these correlation functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗