SEARCH · Engineering Papers

Results for “Advanced Measurement Technique”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Rapid prediction of fuel research octane number and octane sensitivity using the AFIDA constant-volume combustion chamber

Current research octane number (RON) and motor octane number (MON) gasoline performance characterization techniques use dated, complex engine testing methodology and limit researchers’ ability to easily characterize small volumes of experimental fuels. A novel methodology is presented that correlates measured ignition delay (ID) time to RON in an Advanced Fuel Ignition Delay Analyzer (AFIDA) constant-volume combustion chamber device at a single pressure/temperature condition, with an r2 of 0.99 and standard error (SE) of 1.0. The correlation of the slope of the ID time between two additional temperature points to octane sensitivity (S) produces an r2 of 0.97 and SE of 0.69; however, fuels with S>12 are indistinguishable. These results are based on methodology calibration using 31 primary and toluene reference fuels containing 0%-40% ethanol with RON values ranging from 85 to 113. Validation of these methods using a 102-sample fuel matrix spanning an array of base fuels and additive chemistry designed to test the robust applicability of the method, along with pump gasoline and high-octane surrogate blend samples, demonstrates an r2 of 0.94 and SE of 1.3 for the RON correlation over all samples, whereas the equivalent S correlation produces an r2 of 0.78 and SE of 1.2 by excluding two additives, 3-pentanone and diisobutylene, which displayed poor S correlation results. This novel AFIDA analysis method can be performed in 1 h and with 40 mL of fuel, offering significant improvements in time and volume requirements over traditional techniques.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Enabling In-Core Strain Measurement: Printed Sensors for Advanced Reactors

Advances in nuclear energy systems require an improved understanding of fuels, cladding, and structural materials; however, the extreme environmental conditions at irradiation test facilities often limit mechanical property evaluation to post-irradiation examination. In-situ monitoring techniques can accelerate materials qualification by providing direct insight into real-time material behavior during irradiation testing. Additively manufactured (AM) strain gauges offer a compact, material-compatible alternative to commercial sensors, particularly where space constraints, attachment limitations, or material compatibility requirements complicate conventional sensor use. This poster presents an overview of the development and testing of AM strain gauges in nuclear-relevant environments, demonstrating their potential to enhance structural health monitoring capabilities during irradiation experiments.

36 - MATERIALS SCIENCE↗

DOE OSTI2023

Single-shot X-ray absorption spectroscopy at X-ray free electron lasers

X-ray Absorption Spectroscopy (XAS) is a widely used X-ray diagnostic method for studying electronic and structural properties of matter. At first glance, the relatively narrow bandwidth and the highly fluctuating spectral structure of X-ray Free Electron Lasers (XFEL) sources seem to require accumulation over many shots to achieve high data quality. To date the best approach to implementing XAS at XFEL facilities has been using monochromators to scan the photon energy across the desired spectral range. While this is possible for easily reproducible samples such as liquids, it is incompatible with many important systems. Here, we demonstrate collection of single-shot XAS spectra over 10s of eV using an XFEL source, with error bars of only a few percent. We additionally show how to extend this technique over wider spectral ranges towards Extended X-ray Absorption Fine Structure measurements, by concatenating a few tens of single-shot measurements. Our results pave the way for future XAS studies at XFELs, in particular those in the femtosecond regime. This advance is envisioned to be especially important for many transient processes that can only be initiated at lower repetition rates, for difficult to reproduce excitation conditions, or for rare samples, such as those encountered in high-energy density physics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network

Abstract Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.

Bowers, Charles F.↗

DOE OSTI2025

Quantum Gravity and Laser Interferometry: Towards Observable Predictions

Understanding quantum gravity remains one of the deepest challenges in modern physics, as direct experimental access to Planck-scale effects is beyond current technological reach. However, recent theoretical advances indicate that quantum fluctuations of spacetime may produce measurable effects in precision experiments, particularly near causal horizons. This opens new avenues for testing quantum gravity phenomena through high-precision measurement techniques. This dissertation develops multiple theoretical models to characterize these effects and examines their potential observational signatures in future gravitational wave interferometers. We begin by investigating the role of quantum fluctuations in near-horizon geometries through the lens of the AdS/CFT correspondence, which provides a powerful framework for understanding the interplay between quantum field theory and general relativity via holographic principles. By modeling stochastic energy-momentum sources in Rindler-AdS spacetime, we demonstrate that vacuum fluctuations transform the Einstein equations into a Langevin-type stochastic differential equation, leading to potentially observable fluctuations in photon traversal times. Extending this approach to Minkowski spacetime, we establish a correspondence between gravitational shockwaves and fluid dynamics, showing that near-horizon perturbations satisfy an equation analogous to that governing incompressible fluids, thereby reinforcing the membrane paradigm and hydrodynamic analogies in the context of the fluid/gravity duality. Furthermore, we construct the covariant phase space of a spherically symmetric causal diamond in Minkowski spacetime, identifying two fundamental charges that govern its evolution. These results provide a foundation for quantizing causal horizons and understanding their microscopic degrees of freedom. Building upon these theoretical developments, we further examine a related stochastic phenomenon: the gravitational wave memory background arising from the cumulative memory steps produced by supermassive black hole mergers. After reviewing the standard stochastic gravitational wave background, gravitational memory effects, and BMS symmetries, we model the stochastic memory background using a Brownian motion framework. We show that while the cumulative memory background initially appears above the sensitivity curve of space-based interferometers like LISA, the realistic subtraction of individually resolvable merger events substantially suppresses the residual signal, making its detection more challenging. This highlights the critical importance of source subtraction when evaluating the detectability of gravitational memory effects. By bridging fundamental theory with experimental prospects, this dissertation contributes to the ongoing effort to uncover the quantum nature of spacetime through precision measurement techniques. Whether through detecting quantum spacetime fluctuations, gravitational memory backgrounds, or probing the symmetries of causal horizons, the pursuit of observable quantum gravity phenomena continues to expand the frontiers of both theory and experiment.

Zhang, Yiwen [Caltech] (ORCID:0000000323559416)↗

DOE OSTI2023

Online detection of airborne nanoparticle composition with mass spectrometry: Recent advances, challenges, and opportunities

Airborne nanoparticles are receiving growing attention due to their impacts on human health, air quality, and climate. Additionally, there are increasing applications of aerosol-based nanotechnology. Real-time measurement of airborne nanoparticle composition is challenging due to their small masses and the difficulties in effectively separating them from larger particles. Custom-built instruments based on mass spectrometry have been developed for nanoparticle composition, including thermal desorption-based, laser-based, and solution-based techniques. They have significantly promoted the understanding of airborne nanoparticles in the last two decades. However, they are affected by molecular fragmentation, lack of mass-resolving power, incomplete species detection, and difficulties in directly comparing to gas-phase measurements. Recent developments in atmospheric mass spectrometry provide possible solutions. Here, this review presents the potential application of these new advances to real-time measurements of the molecular nanoparticle composition and discusses the associated challenges. Such measurements present opportunities for a more comprehensive view of airborne nanoparticles and their impacts.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

NLR CSP Optical Facilities: Illuminating the Path Forward Through Innovation and Impact: Agreement 38490

This initiative is a multi-faceted project at the National Laboratory of the Rockies (NLR) aimed at strengthening its Concentrating Solar Power (CSP) Optical Facilities to advance the development of low-cost, high-performance materials for solar and other applications. The project's strategy is built on three pillars: strategic stakeholder engagement, diligent facility maintenance and utilization, and the development of new research capabilities. The overarching goal is to ensure the facilities remain state-of-the-art resources for industry and academia, thereby accelerating the conversion of concentrated sunlight into energy. A key driver of the project is an international Advisory Board, which provides critical guidance on research priorities and industry needs, leading to new collaborations and secured funding. This external engagement, combined with proactive outreach to industry partners, ensures the lab's work remains aligned with real-world challenges, including materials durability and performance certification. Significant efforts in facility maintenance have addressed challenges with aging infrastructure. Notable achievements include the complete refurbishment of the hail-damaged Ultra-Accelerated Weathering System (UAWS) and the successful replacement of a failing 15-year-old Lambda 1050 spectrophotometer with a new-generation model, substantially upgrading material characterization capabilities. These maintenance activities were complemented by achieving a prestigious ISO 9001:2015 certification for the Advanced Optical Materials Labs, formally recognizing the quality and reliability of NLR's measurement capabilities. Despite these successes, challenges remain, including high demand for the High Flux Solar Furnace (HFSF) and intermittent failures of other key instruments. The project has delivered major advancements in research techniques and capabilities. At the Flatirons campus, a new indoor laboratory, was established to house advanced deflectometry and photogrammetry systems for heliostat characterization. For on-sun testing, a novel, actively cooled turning mirror was developed for the HFSF, enabling more realistic testing of particle receivers and components. A collaboration with Virginia Tech successfully demonstrated the high-temperature durability of a new solar absorber coating through extensive cyclic testing. Concurrently, new modeling took place to better predict material degradation on rough, fractal surfaces. In summary, this project has systematically enhanced NLR's CSP Optical Facilities through strategic upgrades, rigorous maintenance, and stakeholder-guided research. By overcoming equipment failures, budgetary constraints, and logistical hurdles, the project has reinforced NLR's role as a central hub for CSP innovation and materials testing. Future work will focus on securing diverse funding, expanding collaborations, and continuing to provide the critical infrastructure needed to accelerate the development and deployment of next-generation technologies.

14 SOLAR ENERGY↗

DOE OSTI2024

Technical note: Optimizing the in situ cosmogenic 36 Cl extraction and measurement workflow for geologic applications

Abstract. In situ cosmogenic 36Cl analysis by accelerator mass spectrometry (AMS) is routinely employed to date Quaternary surfaces and assess rates of landscape evolution. However, standard laboratory preparation procedures for 36Cl dating require the addition of large amounts of isotopically enriched chlorine spike solution; these solutions are expensive and increasingly difficult to acquire from commercial sources. In addition, the typical workflow for 36Cl dating involves measuring both 35Cl/37Cl and 36Cl/Cl concurrently on the high-energy (post-accelerator) end of the AMS system, but 35Cl/37Cl determinations using this technique can be complicated by isotope fractionation and system memory during measurement. The traditional workflow also does not provide 36Cl extraction laboratories with the data needed to calculate native Cl concentrations in advance of 36Cl/Cl measurements. In light of these concerns, we present an improved workflow for extracting and measuring chlorine in geologic materials. Our initial step is to characterize 35Cl/37Cl on sample aliquots of up to ∼1 g prepared in Ag(Cl, Br) matrices, which greatly reduces the amount of isotopically enriched spike solution required to measure native Cl content in each sample. To avoid potential issues with isotope fractionation through the accelerator, 35Cl/37Cl is measured on the low-energy, pre-accelerator end of the AMS line. Then, for 36Cl/Cl measurements, we extract Cl as AgCl or Ag(Cl, Br) in analytical batches with a consistent total Cl load across all samples; this step is intended to minimize source memory effects during 36Cl/Cl measurements and allows the preparation of AMS standards that are customized to match known Cl contents in the samples. To assess the efficacy of this extraction and measurement workflow, we compare chlorine isotope ratio measurements on seven geologic samples prepared using standard procedures and the updated workflow. Measurements of 35Cl/37Cl and 36Cl/Cl are consistent between the two workflows, and 35Cl/37Cl values measured using our methods have considerably higher precision than those measured following standard protocols. The chemical preparation and measurement workflow presented here (1) reduces the amount of isotopically enriched chlorine spike used per rock sample by up to 95 %; (2) identifies rocks with high native Cl concentrations, which may be lower priority for 36Cl surface exposure dating, at an early stage of analysis; and (3) allows laboratory users to maintain control over the total chlorine content within and across analytical batches. These methods can be incorporated into existing laboratory and AMS protocols for 36Cl analyses and will increase the accessibility of 36Cl dating for geologic applications.

58 GEOSCIENCES↗

DOE OSTI2023

Visualisation of sulphur on single fibre level for pulping industry

In the pulp and paper industry, about 5 Mt/y chemithermomechanical pulp (CTMP) are produced globally from softwood chips for production of carton board grades. For tailor making CTMP for this purpose, wood chips are impregnated with aqueous sodium sulphite for sulphonation of the wood lignin. When lignin is sulphonated, the defibration of wood into pulp becomes more selective, resulting in enhanced pulp properties. On a microscopic fibre scale, however, one could strongly assume that the sulphonation of the wood structure is very uneven due to its macroscale size of wood chips. If this is the case and the sulphonation could be done significantly more evenly, the CTMP process could be more efficient and produce pulp even better suited for carton boards. Therefore, the present study aimed to develop a technique based on X-ray fluorescence microscopy imaging (µXRF) for quantifying the sulphur distribution on CTMP wood fibres. Firstly, the feasibility of µXRF imaging for sulphur homogeneity measurements in wood fibres needs investigation. Therefore, clarification of which spatial and spectral resolution that allows visualization of sulphur impregnation into single wood fibres is needed. Measurements of single fibre imaging were carried out at the Argonne National Laboratory's Advanced Photon Source (APS) synchrotron facility. With a synchrotron beam using one micrometre scanning step, images of elemental mapping are acquired from CTMP samples diluted with non-sulphonated pulp under specified conditions. Since the measurements show significant differences between sulphonated and non-sulphonated fibres, and a significant peak concentration in the shell of the sulphonated fibres, the proposed technique is found to be feasible. The required spatial resolution of the µXRF imaging for an on-site CTMP sulphur homogeneity measurement setup is about 15 µm, and the homogeneity measured along the fibre shells is suggested to be used as the CTMP sulphonation measurement parameter.

CTMP↗

DOE OSTI2026

Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling

The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.

Digital twins↗

DOE OSTI2026

Toward a microscopic picture of hadronization and multi-parton processes

This project advanced the understanding of how quarks and gluons produced in high-energy collisions transform into the hadrons observed in particle detectors, a fundamental process known as quantum chromodynamics (QCD) hadronization. By combining theoretical calculations, quantum simulation methods, and modern AI techniques, the research developed new tools to study multi-parton dynamics and nonperturbative effects that are essential for interpreting data from current and future nuclear physics experiments. Key outcomes include new theoretical frameworks for jet and hadron measurements, pioneering quantum simulation algorithms for real-time dynamics in field theories, and the development of advanced machine-learning models, such as diffusion models and explainable classifiers, to simulate and analyze collider events. These results are directly relevant to experiments at Jefferson Lab, Brookhaven National Laboratory, and the future Electron-Ion Collider, and they also have a broader impact in areas such as quantum information science and data-driven modeling of complex systems. The project supported the training of graduate students and postdoctoral fellows and contributed to the broader scientific community through publications, workshops, and collaborative activities. Overall, this work provides new insights into the microscopic mechanisms of hadron formation and establishes a foundation for future studies at the intersection of nuclear physics, artificial intelligence, and quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Hydrogen and its detection in fusion and fission nuclear materials – a review

We report fusion and fission reactions are profoundly dependent on hydrogen for sustained reactions. Fusion is fueled by the isotopes of hydrogen, and predominantly hydrogen-based moderators slow fission neutrons to propagate chain reactions. Intentional tritium production in fusion and concomitant tritium production in fission reactors introduce challenges. The technology to efficiently extract, harvest, and quantify tritium in and from advanced fission molten salt coolants and fusion molten tritium breeder materials will require more pronounced research and development. Measuring and quantifying hydrogen is necessary in all areas of nuclear materials. Although many characterization techniques cannot directly detect hydrogen, numerous techniques provide the necessary information to understand the behavior of hydrogen in nuclear materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Development of a portable high-T c spherical neutron polarimetry device at the Oak Ridge National Laboratory

Spherical neutron polarimetry is a powerful polarized neutron scattering technique used to determine complex magnetic structures which are only partly accessible by other methods. This technique measures the full neutron polarization change upon scattering from a sample by fully decoupling the incoming and outgoing neutron polarization with a zero-field chamber placed at the sample position. Recent advancements and testing are presented for a new spherical neutron polarimetry device utilizing high-T c superconducting YBCO films, PHiTPAD, at the High Flux Isotope Reactor at Oak Ridge National Laboratory. Furthermore, we introduce a conceptual design that utilizes wavelength-independent adiabatic transitions to adapt spherical neutron polarimetry for use with pulsed neutron sources, thereby expanding its potential applications in neutron scattering research.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Non-Intrusive Measurements Using Femtosecond Laser in Atmospheric Pressure Plasma Jet

Femto-second (fs) lasers offer state-of-the art non-intrusive diagnostic capabilities in plasma flow environments. The diagnostics in plasma flows using a fs-laser that are relevant to this proposal include Two-photon Absorption Laser Induced Fluorescence (TALIF) for measuring the number density of an atomic species, and Femto-second Laser Electronic Excitation Tagging (FLEET) for performing velocity measurements in plasma flows. Various aspects of these diagnostics using fs-lasers are still an active domain of research. The TALIF technique has been used to measure the number density of atomic species in various plasma flow environments like atmospheric pressure plasma jet (APPJ) and arc-jet flows. This technique utilizes a fs-laser to excite an atomic species and the subsequent relaxation process through fluorescence is recorded to make measurements. A narrow band-pass filter is used to isolate the fluorescence signal that is in the infra-red regime. However, broad-band background emissions can potentially pass through the filter and induce noise in the TALIF signal. Particularly, arc-jet plasma flows inherently contain background emissions. In such plasma flow environments, in addition to the filter, the background-subtraction technique will play an important role in reducing the noise that is induced in the number density measurements using TALIF. The proposed research focuses on evaluating the effectiveness of a background-subtraction technique by measuring atomic oxygen using a femtosecond (fs) TALIF in APPJ with induced background emissions and comparing measurements obtained without inducing the emissions. Therefore, by quantifying the changes in the signal-to-noise ratio caused by a background-subtraction technique applied for APPJ, it will enable a better understanding of the uncertainties in the background subtracted fs-TALIF measurements for arc-jet plasma flows. In addition to this, FLEET will be used to obtain velocity in the APPJ, and the impact of background emissions will be analyzed. Under this effort, the experimenters and research personnel will gain the technical know-how in the operational and theoretical aspects of the advanced fs-laser-based diagnostic techniques. The knowledge gained in this process will then also directly be applied to a separate ONR funded project at the Aerodynamics Research Center (ARC), UT Arlington, related to arc-jet flow characterization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Top-down proteomics

Proteoforms arising from posttranslational modifications, genetic polymorphisms, and RNA splice variants, play a pivotal role as the key drivers in biology. Thus, a comprehensive understanding of proteoforms is essential for unraveling the intricacies of biological systems and bridging the gap between genotype and phenotype. By analyzing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and presents a next-generation approach for deciphering protein function, uncovering disease mechanisms, and advancing precision medicine. This Primer embarks on a journey into the world of TDP by encapsulating its historical context, underlying principles, recent advances, and an outlook on the future of TDP. The experimental section navigates instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques, and data collection. Results decipher raw data, visualize intact protein spectra, unravel data analysis, and explain proteoform identification, characterization, and quantitation, as well as statistical analysis. Various applications of TDP spanning the human proteoform project, biomedical, biopharmaceutical, and clinical applications are described. These are complemented by discussions on measurement reproducibility, limitations, and a forward-looking perspective outlining uncharted waters where the field can advance, and potential exciting future applications of TDP.

Roberts, David S.↗

DOE OSTI2023

Integrating LEO and GEO Observations: Toward Optimal Summertime Satellite Precipitation Retrieval

Abstract Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth’s hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net–like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net–like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net–like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs’ importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology.

Meteorology & Atmospheric Sciences↗

DOE OSTI2022

Analysis of Reactive Power Load Modeling Techniques for PV Impact Studies [Slides]

The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

14 SOLAR ENERGY↗