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DOE OSTI2022

Precise ROCOF estimation algorithm for low inertia power grids

The precise estimation of Rate Of Change Of Frequencies (ROCOFs) in a generation trip event can be helpful on power system inertia estimation, fast system response, and accurate event capturing. However, the ROCOF estimations from the existing Synchronized Measurement Devices (SMDs) are usually simply calculated by the finite difference between two adjacent frequency measurement points. The ineluctable noises, disturbances, and spikes from real-world frequency measurements can bring large dynamics to the ROCOF estimation and thus can result in an inaccurate estimation of the initial ROCOF. This issue becomes more serious when the target power grid has low inertia, and a large amount of distributed energy sources are deployed. To address this issue, a precise ROCOF estimation algorithm is designed based on least square method with flexible window size. In addition, a median filter is also designed and applied on the frequency measurements before using the proposed algorithm. The window size and thresholds in the proposed algorithm are determined with historical event data analysis. The proposed algorithm is deployed in a low cost, flexible, and distribution level universal grid analyzer (UGA) platform. In conclusion, multiple experiments are conducted in both a laboratory and the Hawaiian Islands to verify the effectiveness of the proposed algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Dataset for "Enrichment of phosphates, lead, and mixed soil-organic particles in INPs at the Southern Great Plains site"

Ice nucleating particles (INPs) are rare particles that initiate primary ice formation, a critical step required for subsequent important cloud microphysical processes that ultimately govern cloud phase and cloud radiative properties. Laboratory studies have found that organic-rich dusts, such as those found in soils, are more efficient INPs compared to mineral dust. However, the atmospheric relevance of these organic-rich dusts are not well understood, particularly in regions with significant agricultural activity. The Agricultural Ice nuclei at the Southern Great Plains field campaign (AGINSGP) was conducted in rural Oklahoma to investigate how soil dusts contribute to INP populations in the Great Plains. We present chemical characterization of ambient and ice crystal residual particles from a single day of sampling using single particle mass spectrometry (SPMS) and scanning microscopy. Ambient particle concentrations were primarily carbonaceous or secondary aerosol, while dust particles were enhanced in the residual particles. Dust particles measured during residual sampling contained elevated signals for phosphate ($^{63}$PO$_2^-$ and $^{79}$PO$_3^-$) and lead ($^{208}$Pb$_2^+$). Nitrate was slightly depleted relative to ambient dust, and strong sulfate signals were not seen in the residual particles measured by the miniSPLAT. The residual and ambient particles measured by the miniSPLAT were approximately the same size, though this may be a reflection of the SPMS size-dependent transmission efficiency and not that of physical truth. This study shows that organic-rich soils may be important contributors to the ambient INP population in agricultural regions.

Cornwell, Gavin C↗

NASA NTRS1990

FIRE extended time/limited area observations at Palisades, New York

Downwelling shortwave and longwave irradiation are being continuously monitored at Palisades, New York as part of the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Extended Time/Limited Area Initiative. In addition, fisheye (180 degree) sky photographs are taken at the times of NOAA 9 and LANDSAT satellite overpasses on select days, particularly when cirrus clouds are present. Measurements of incoming shortwave (0.28 to 2.80 microns) hemispheric and diffuse, hemispheric near infrared (0.7 to 2.80 microns), and downwelling hemispheric infrared (4.0 to 50.0 microns) irradiation have been made from a rooftop location on the grounds of the Lamont-Doherty Geological Observatory since December 1986. The three Eppley Precision Spectral Pyranometers and the Eppley Pyrgeometer used to measure these variables were calibrated with Colorado State University instruments at Madison, Wisconsin as part of the FIRE Intensive Laboratory. Pyrgeometer output contains an adjustment for body temperature but not for dome temperature. Data are transmitted to a Campbell CR-21 Digital Recorder, where one minute averages of ten second samples are stored and subsequently dumped to a cassette recorder. Using a Campbell C-20 Cassette Interface, these data are transferred to an Apple Macintosh computer for analysis and for archiving on floppy disks. In addition to the raw irradiances collected, variables derived from these data are generated and stored. These include: the ratio of near infrared irradiation to visible irradiation and the fraction of the full shortwave irradiation which is diffuse; and will soon include: shortwave transmissivity and optical depth in the shortwave. Sky photographs are taken with an Olympus OM2-N 35 mm camera and are timed to be coincident with overpassing NOAA and LANDSAT satellites. Palisades is within the field of view of the NOAA 9 daily in the middle to late afternoon. The satellite viewing angle is within 45 degrees of nadir over Palisades on approximately half of the passes.

Robinson, David A.↗

NASA NTRS1996

Research concerning the net flux of radiation in the atmosphere of Jupiter

The plan of the NFR (Net Flux of Radiation) team is for the data from the two solar channels (B and E) of NFR to be reduced with the goal of determining the solar heating rate. In order to determine the solar heating rate from the NFR measurements, effects due to the instrument's spatial and spectral response functions, to the temperature variation of the instrument (and associated drift of calibration), to the setting sun, and to the rotation of the probe (initially at a rate comparable to the NFR sampling frequency), all must be well modelled. In the past year, a forward modeling routine was created to simulate NFR data return in the B and E channels. The effects of varying parameters describing the atmospheric model (such as cloud location and thickness) and the descent profile (such as rotation rate) were investigated and an inversion routine was developed. For the forward modeling, existing radiative transfer codes were used to determine intensity fields within the Jovian atmosphere. A routine was developed to determine instantaneous instrument response by integrating the intensity field over the instrument response functions. A second routine was developed to determine the actual output of the NFR by integrating along an arbitrary descent trajectory. Near the top of the atmosphere, the upflux data alone are used to constrain the cloud structure of he atmosphere. To accomplish this, models are used to describe the variation in up flux between consecutive measurements in terms of variations of cloud opacity and variations in known parameters such as the solar zenith angle. This allows us to develop a zero-order model of cloud structure. Lower in the atmosphere, at levels where there is little or no azimuthal structure to the net flux measurements, both the up flux and net flux are used to derive layer transmission and reflection functions, which then determine layer opacity and single scattering albedo. A preliminary analysis of the data began in December 1995. In these data we could see the rapid oscillations expected at the beginning of the data due to probe rotation and the sun passing through the edge of the field of view. In addition, the time when this oscillation stopped was clearly visible. This sets the rough optical depth above the probe at this time.

Tomasko, M. G.↗

DOE OSTI2025

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Predicting the Seawater Chemistry of an Ocean World Using Machine Learning on Isotopic Measurements of Volatile CO2

Introduction: Given the long time intervals required for data transmission to and from ocean worlds targets, low bandwidth for data transmission, time required for data processing and analysis, and potentially extreme radiation environments (e.g., Europa), it is clear that ocean worlds missions will need more autonomous flight instruments and software in order to achieve established science goals. Protracted time intervals for data analysis (e.g., Europa Lander) strongly motivates the development of rapid, consistent and streamlined methods for interpreting data from flight mass spectrometers to e.g., determine how mass spectra from a plume or surface liquid/ice relates to the surface/subsurface. Since mass spectrometry also has the potential to correctly identify biosignatures[1], it is imperative that such methods for interpreting data are consistent and accurate. We used 848 isotope ratio mass spectra from laboratory analyses of CO2 that interacted with ocean worlds-relevant seawaters as a ‘training’ dataset for ‘unsupervised’ machine learning. In unsupervised learning, characteristics of the data are not labeled or linked, and any similarities found only result from the neural network. CO2 isotopologues analyzed for this dataset mimic the remote measurements of CO2 by a flight mass spectrometer, and are detailed in Theiling [2]. From this dataset, we used measured features of the spectra, such as retention time, intensity, and (isotopologue) mass ratios as inputs for our autoencoder neural network. Our neural network was trained to find similarities in these and other spectral features for seawaters of a particular composition and amount of initial CO2. Successful training then created an output of these similarities for various seawaters, which included MgSO4, Na2SO4, NaCl, MgCl2, KCl, and NaHCO3, and combinations of these salts. We then applied dimensionality reduction techniques such as Principal Component Analysis (PCA), T-Distributed Stochastic Neighbor Embedding (TSNE), and Uniform Manifold Approximation and Projection (UMAP) to demonstrate latent data features as a two-dimensional projection in a unitless, high-dimensional space. In this projection, a data point represents the combined effect of spectral features such as intensity, retention time, and isotope ratio. Our initial UMAP demonstrates data clustering (organization of the data by the neural network) based on the amount of CO2 that had initially interacted with each seawater. Further training using more ‘supervised’ learning techniques demonstrate strong clustering of preliminary data based on initial CO2 concentration, seawater chemical composition, and ionic strength (salinity). Our preliminary work therefore suggests that machine learning has the potential to identify compositional variants of an ocean world seawater based on mass spectra from volatile CO2 measurements. Acknowledgments: This work was funded through a Strategic Task Group at NASA Goddard Space Flight Center. The training dataset was collected through funding from the Oklahoma Space Grant Consortium. References: [1] Pappalardo, R. et al. (2013) Astrobiology, 13, 740–773. [2] Theiling (2020) Icarus, 114216.

Europa↗

NASA NTRSDate not supplied

Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection

High-fidelity measurements of magnetic fields and other observed properties, such as energetic particle fluxes, are a necessary component to our understanding of the highly dynamic near-Earth space environment. As our desire to study smaller-scale phenomena such as shocks and dipolorizations has increased, we have been driven to take and telemeter measurements at higher cadences. Unfortunately, many missions are unable to downlink all their captured data due to the well-known data transmission bottleneck at the DSN. These missions must then prioritize their high-cadence data such that the most scientifically useful intervals are transmitted. One simple prioritization technique uses the spacecraft position to telemeter data from only the region of interest. Although easy to implement, this method does not leverage the available scientific data and can omit intervals of useful scientific data when they lie outside the region of interest. The Magnetospheric Multiscale Mission (MMS) uses mission-specific parameterization of several data products to automatically prioritize scientifically useful intervals. Then, MMS verifies the automatically selected intervals by having a domain expert manually select intervals for downlink. The overall complexity required by this technique make it prohibitive for deployment on low-cost platforms (i.e., CubeSats) or on future missions featuring large constellations of satellites such as the Geospace Dynamics Constellation (GDC). We present preliminary results for a simple, generic, and data-driven method of downlink prioritization for magnetic field (and other) measurements. Specifically, Principal Components Analysis (PCA) and One-Class Support Vector Machines (OC-SVMs) are used to detect intervals containing anomalous activity, which can then be prioritized for subsequent downlink. The computational simplicity of this algorithm makes it an excellent candidate for implementation on spaceflight hardware, as well as provide generalizability to a broad range of missions and data products. Initial analysis of this technique has been performed using magnetic field measurements from the Magnetospheric Multiscale Mission and CASSIOP, where it automatically identified scientifically interesting intervals containing Alfvén waves and EMIC activity.

Matthew G. Finley↗

NASA NTRSDate not supplied

Enabling Intelligent Data Downlink Prioritization of In-Situ Observations through Generalizable and Computationally Inexpensive Anomaly Detection

Matthew G. Finley↗

DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2014

Micro-Spec: an Integrated, Direct-Detection Spectrometer for Far-Infrared and Submillimeter Astronomy

The far-infrared and submillimeter portions of the electromagnetic spectrum provide a unique view of the astrophysical processes present in the early universe. Our ability to fully explore this rich spectral region has been limited, however, by the size and cost of the cryogenic spectrometers required to carry out such measurements. Micro-Spec (u-Spec) is a high-sensitivity, direct-detection spectrometer concept working in the 450-1000 micromillimeter wavelength range which will enable a wide range of flight missions that would otherwise be challenging due to the large size of current instruments with the required spectral resolution and sensitivity. The spectrometer design utilizes two internal antenna arrays, one for transmitting and one for receiving, superconducting microstrip transmission lines for power division and phase delay, and an array of microwave kinetic inductance detectors (MKIDs) to achieve these goals. The instrument will be integrated on a approximately 10 square cm silicon chip and can therefore become an important capability under the low background conditions accessible via space and high-altitude borne platforms. In this paper, an optical design methodology for Micro-Spec is presented, with particular attention given to its twodimensional diffractive region, where the light of different wavelengths is focused on the different detectors. The method is based on the maximization of the instrument resolving power and minimization of the RMS phase error on the instrument focal plane. This two-step optimization can generate geometrical configurations given specific requirements on spectrometer size, operating spectral range and performance. A point design with resolving power of 257, an RMS phase error less than 0.1 radians and four stigmatic points was developed for initial demonstration and will be the basis of future instruments with resolving power up to about 1200.

microwave kinetic inductance detectors (MKIDs)↗

DOE OSTI2021

Formation and surface melting of nanoparticle superlattices in a solution

The wisdom in the saying of “There are no two snowflakes alike” lies in the importance of history or kinetic pathways in the phase transitions of solids. Likewise, “artificial solids,” namely superlattices consisting of functional nanoparticles, have lattice size, surface morphology, crystallinity, symmetry, and structural reconfiguration (for example, transition into a disordered state) highly dependent on the kinetic pathways as the nanoparticles interact with each other in solution [1]. Great progresses have been made in understanding the formation pathways of superlattices using liquid-phase transmission electron microscopy (TEM) [2-4]. For example, by tracking single nanoparticle’s trajectories, especially aided by U-net neural network-based machine learning, previous studies mapped the fundamental nanoparticle interactions at nanometer resolution [5]. Nonclassical, two-step nucleation pathway has also been elucidated in the system of nanoprisms, by optimizing protocols such as loading nanoparticle suspensions over the supersaturation threshold and minimizing particle‒substrate interaction [2]. Surface morphologies or exposed facets of superlattices have been shown to follow the principles of Wulff construction rule, where the facet-dependent surface energy can be measured based on the capillary wave theory [4]. However, the reverse process of crystallization of superlattices, the conversion from crystalline to disordered state, has been much less explored. On one hand, the melting of nanoparticle superlattices can provide a preferred pathway to induce structural reorganization or shuffling of building blocks for them to transform into different types of crystal structures. On the other hand, understanding nanoscale superlattice melting and comparing such behaviors with the prevailing surface melting theories developed for atomic/molecular solids can provide a potent way to engineer phase transitions of supra- and hierarchical structures constructed from nanoscale entities (e.g., DNA-coated nanoparticles, proteins), for their applications in reprogrammable and switchable materials with multifunctional properties [6, 7]. The experimental challenges to observe melting of superlattices are twofold. Practically it is difficult to load the initial superlattice form, in an intact manner, into the highly confined liquid-phase TEM chamber for in-situ observation. Here, the triggering of melting also needs meticulous manipulation of nanoparticle concentration, interparticle interaction, and solution environment.

Kim, Ahyoung↗

NASA NTRS2015

Preliminary Statistics from the NASA Alphasat Beacon Receiver in Milan, Italy

NASA Glenn Research Center (GRC) and the Politecnico di Milano (POLIMI) have initiated a joint propagation campaign within the framework of the Alphasat propagation experiment to characterize rain attenuation, scintillation, and gaseous absorption effects of the atmosphere in the 40 GHz band. NASA GRC has developed and installed a KQ-band (20-40 GHz) beacon receiver at the POLIMI campus in Milan, Italy, which receives the 20-40 GHz signals broadcast from the Alphasat Aldo Paraboni TDP5 beacon payload. The primary goal of these measurements is to develop a physical model to improve predictions of communications systems performance within the Q-band. Herein, we provide an overview of the design and data calibration procedure, and present 6 months of preliminary statistics of the NASA propagation terminal, which has been installed and operating in Milan since May 2014. The Q-band receiver has demonstrated a dynamic range of 40 dB at an 8-Hz sampling rate. A weather station with an optical disdrometer is also installed to characterize rain drop size distribution for correlation with physical based models

Radio Transmission↗

NASA NTRS1994

Measurement Capabilities of Single-Pulse Planar Doppler Velocimetry

Preliminary investigations are described of a method that is capable of measuring instantaneous, 3-D, velocity vectors everywhere in a light sheet generated by a pulsed laser. The technique, here called Planar Doppler Velocimetry (PDV), is a variation of a new concept for velocity measurements that was called Doppler Global Velocimetry (DGV) in its original disclosure. The concept relies on the use of a narrowband laser and measurements of the Doppler shift of scattered light from particles moving with a flow. The Doppler shift is recorded as a variation in transmission through a sharp-edged spectral filter provided by iodine vapor in a cell. Entire fields of velocity can be determined by using a solid-state camera to record the intensity variations throughout the field of view. However, the implementation of DGV has been centered principally on the use of high power, continuous-wave, ion lasers and measurement times that are determined by the 30-ms framing times of standard video cameras. Hence, they provide velocity fields that are averaged in time at least over that period. On the other hand, the PDV concept described in this presentation incorporates a high energy, repetitively pulsed, Nd-YAG laser that is injection-seeded to make it narrowband and then frequency-doubled to provide light at frequencies absorbed by the iodine vapor. The duration of each pulse is less than 10 nanoseconds. When used in combination with nonstandard, scientific quality, solid state cameras, a sequence of images can be obtained that provides instantaneous velocity vectors everywhere in the field of view. The investigations described in this paper include an accurate characterization of the iodine cell spectral behavior and its influence on the PDV measurements, a derivation of the PDV signal analysis requirements, and the unique aspects of the pulsed laser behavior related to this application. In addition, PDV measurements are to be demonstrated using data from a rotating wheel target and from the flow of a subsonic jet. Initially, single optical fiber light collection and photomultiplier detectors will be substituted for solid state cameras. Those results will allow the determination of the fundamental limitations of the PDV technique without the complications of image acquisition and processing. They will then be used to provide an analysis of the measurement capabilities of PDV both in small aerodynamic research wind tunnels and in large wind tunnels designed for production airframe and propulsion testing. Future plans include the implementation of solid state cameras and the development of the required image acquisition and processing software. Eventually, the PDV technique will be applied to an aerodynamic research program related to transonic wing flutter.

McKenzie, Robert L.↗

DOE OSTI2024

BENEFIT with Northeastern University: HVAC Hardware-in-the-Loop Experimental Testing of a Heat Pump and Air Conditioner

This dataset includes HVAC Hardware-in-the-Loop (HIL) experimental results for a single stage, SEER 16, HSPF 9.5, 3-ton single-speed air source heat pump with 15 kW of backup auxiliary heating tested in both cooling and heating mode, and a two stage, SEER 21, 2-ton central air conditioner tested in cooling mode for a set of outdoor temperatures and indoor setpoint temperatures. In addition to these tests, experimental tests focused on the operation of auxiliary heating for the heat pump for winter condition were also conducted. The laboratory experiments for transient testing of the heat pump and air conditioner were conducted using the two HIL systems in the Systems Performance Laboratory (SPL) at NREL’s Energy Systems Integration Facility (ESIF). Further information on laboratory design and capabilities of the SPL along with the architecture of HVAC HIL system can be found in: Sparn, B. F. 2018. Laboratory Resources and Techniques to Evaluate Smart Home Technology (No. NREL/CP-5500-71696). National Renewable Energy Laboratory (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy18osti/71696.pdf and the experimental setup and validation of HVAC HIL platform can be found in: Ramaraj, S. and Sparn, B. 2022. Validation of HVAC Hardware-In-the-Loop Simulation for Advanced Control Strategies in Smart Homes (No. NREL/CP-5500-82562). National Renewable Energy Lab (NREL), Golden, CO (United States). https://www.nrel.gov/docs/fy22osti/82562.pdf. These experimental results can be used to validate how we currently model the cycling behavior of heat pumps and air conditioners. Additionally, many demand response programs implement heat pump and air conditioner control by changing the thermostat set point – these data may also be used to verify our models for heat pump and air conditioner demand response control are implemented correctly. The Test_Matrix file describes all the indoor and outdoor test conditions for heat pump and air conditioner and the file names of data sets include information about the test conditions. A wide range of outdoor air temperatures were chosen to accommodate summer and winter conditions. In addition to operating the HVAC equipment with different outdoor temperatures, we also operate the system with different indoor temperature set points to represent different grid signals or different operating conditions. For cooling conditions, the baseline set point is 72°F. To represent Load Up signals, the setpoint is changed to 68°F. The Load Shed set point is 76°F. For heating conditions, the baseline set point was assumed to be 68°F. The Load add set point is 72°F and the Load shed set point is 64°F. The starting indoor temperature for cooling conditions was set ~2°F above the indoor setpoint temperature so that the equipment turned on quickly. Similarly, the initial indoor temperature was set ~2°F lower than setpoint for heating mode tests to ensure that heating began quickly. The return air temperature was assumed to be equal to the indoor setpoint temperature in all cases. The experimental data are sampled at 1-second intervals. The data from ecobee thermostat at 5-minute interval are resampled and added to the corresponding file. The content of each data set is as follows: • T_Return (C): Measured return air temperature [C] • T_Return_SP (C): Return air temperature setpoint from E+ model, sent to HIL [C] • T_Supply (C): Measured supply air temperature at evaporator outlet [C] • T_Outdoor (C): Measured outdoor air temperature [C] • T_Outdoor_SP (C): Outdoor air temperature setpoint from weather file, sent to HIL [C] • T_Indoor (C): Measured indoor air temperature [C] • T_Indoor_SP (C): Indoor air temperature setpoint from E+ model, sent to HIL [C] • Outdoor Unit Power (W): Measured power of the outdoor unit [W] • Indoor Unit Power (W): Measured power of the indoor unit [W] • Evaporator Airflow Rate (CFM): Measured evaporator or indoor unit airflow rate sent to E+ model [CFM] • Cooling/Heating Capacity (kW): Calculated cooling/heating capacity sent to E+ model [kW] • T_SP_Thermostat (C): Thermostat cooling/heating setpoint temperature [C] • T_Indoor_Thermostat (C): Thermostat indoor air temperature [C]

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Coalescence of GaP on V-Groove Si

In recent years, better understanding and control over the formation of crystalline defects during the direct epitaxy of III-V semiconductors on Si substrates via metal organic vapor phase epitaxy (MOVPE) has enabled large gains in III-V-on-Si solar cell efficiency, pointing to pathway to lower-cost, high-performance III-V solar cells. However, such results have only been achieved on costly chemo-mechanically-polished (CMP) Si wafers. The use of V-groove nanopatterned Si substrates has demonstrated similarly high-crystalline-quality III-V-on-Si epitaxy, but also can be combined with lower-cost polishing techniques. Although they offer a potential cost advantage, growth on V-groove substrates adds challenges not present for epitaxy on planar wafers (the III-V material must be coalesced into a thin film after an initial nucleation stage). MOVPE growth conditions that promote highly facet-selective lateral growth needed for coalescence are generally actively avoided for conventional thin fim growth, so growth conditions need to be re-developed for V-groove-based epitaxy. Additionally, coalescence add complexity to the dislocation dynamics related to lattice relaxation, so strategies used to keep threading dislocation density (TDD) low on planar substrates need to be re-tuned for V-grooves. We have studied the morphological evolution and dislocation dynamics of GaP grown on V-groove Si by MOVPE. Growth conditions of V/III=5,000 and Tg=800 degrees C were uniquely found to produce extremely smooth coalesced thin films, with an RMS roughness of 0.2 nm measured by AFM. Additionally, at this growth condition, we identified two regimes of growth determined by the width of the SiNx cap (a remnant of the nanopatterning process) at the top of the V-grooves. For narrow caps, the GaP coalesces into a thin film, and for wide caps, the GaP evolves into {1 1 1}-faceted diamonds that do not coalesce. We suggest the influence of Si from the sidewalls of the V-grooves on the surface reconstruction of the GaP as the mechanism for this effect, with reflection difference spectroscopy (RDS) and Si doping experiments supporting this theory. In addition to morphology, the dislocation dynamics of the system were studied with electron channeling contrast imaging (ECCI) and transmission electron microscopy (TEM). The TDD of the coalesced GaP films was found to be 5 x 10^7 cm^-2 after coalescence via ECCI, a level still too high for high-quality solar cells. However, misfit dislocations crossing multiple grooves greater than 20 micrometers long were observed in ECCI, suggesting that the V-grooves do not block dislocation glide. TEM prior to and after coalescence was used to distinguish between dislocation creation driven by growth conditions and coalescence. Finally, strategies to reduce the dislocation density to levels acceptable for solar cells will be discussed.

GaP↗

DOE OSTI2022

End-to-end Analytics for Grid Arch Design & All-hazard Assessment

Resiliency, reliability, and security of the next-generation smart grid depend upon leveraging advanced communication and computing technologies, integrating them with physical power systems, and developing real-time, fast, data-based applications to help in wide-area monitoring and control of the grid. Using a high sampling data rate from phasor measurement units (PMUs) to develop applications has opened the door to achieving the next-generation grid requirements. The North American Synchrophasor Initiative Network (NASPlnet) was developed in 2007-09 to create a standard and guide for PMU data exchanges. With the advancement in both networking and grid requirements, it is necessary to evaluate the performance of different NASPInet versions and their impact on applications. Therefore, we need a cyber-power cosimulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents a cyber-physical co-simulation testbed using NS3 to model the communication network, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application based on PMU data in an Institute of Electrical and Electronics Engineers 39-bus test system is presented using this co-simulation testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Calibration and Validation of the SHERLOC Instrument Operating in Jezero Crater, Mars

The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemistry (SHERLOC) is an instrument onboard the Mars 2020 Perseverance rover. It consists of a spectrometer that measures deep ultraviolet (DUV) resonance Raman and native fluorescence photons generated through surface interactions with a 248.6 nm pulsed laser. Two microscopic imagers, the Autofocus Context Imager (ACI) and the Wide-Angle Topographic Sensor for Operations and eNgineering (WATSON), provide high-resolution context images of SHERLOC targets. The ACI is co-boresighted with the spectrometer. To ensure proper calibration, the SHERLOC calibration target (SCT) is mounted on the front of the rover and consists of ten different materials. The SCT was designed, fabricated, assembled, and tested by Jacobs Technology, Inc., and NASA Johnson Space Center. The dimensions of the calibration target housing are 150 x 89 x 33 mm and it weighs ~437 g. The ten targets are arranged in two rows; the first six are hard targets (AlGaN for Raman and fluorescence, diffuse transmission target, a slice of the SaU008 Mars meteorite, an intensity maze, and polycarbonate over geocache coin) and the remaining four are soft-goods targets (Vectran, Ortho-Fabric, Teflon, and nGimat-coated Teflon). The hard targets calibrate the spectrometer’s Raman and fluorescence spectral accuracy, ambient light reflection, and Raman response curve, while the soft-goods targets are spacesuit materials that function as human exploration targets, some of which are also used for spectral calibration. SHERLOC also has an internal calibration target consisting of AlGaN on sapphire (275 nm) located inside the ACI opaque dust cover to ensure proper instrument functioning between calibration target analyses. Initial SHERLOC, ACI, and WATSON calibration and validation on Mars was performed using the internal calibration target on sols 59, 83, 98, and 141; WATSON imaging of the SCT on sols 26 and 62, and SHERLOC spectroscopy and ACI imaging of the SCT on sol 59. These early data provide initial insights into instrument performance and the stability and degradation of the calibration target materials relative to the Martian surface and dust environment. Early observations also have implications for future astronaut spacesuit materials.

Trevor G Graff↗

DOE OSTI2026

Melting of charge density waves in low dimensions

Charge density waves (CDWs) are collective electronic states that can reshape and melt, even while confined within a rigid atomic crystal. In two dimensions, melting is predicted to be distinct, proceeding through partially ordered nematic and hexatic states that are neither liquid nor crystal. Here, we measure and explain how continuous, hexatic melting of incommensurate CDWs occurs in low-dimensional materials. As a CDW is thermally excited, disorder emerges progressively—initially through smooth elastic deformations that modulate the local wavelength and subsequently via the nucleation of topological defects. Experimentally, we track three hallmark signatures of CDW melting—azimuthal superlattice peak broadening, wavevector contraction, and integrated intensity decay.

42 ENGINEERING↗