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

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DOE OSTI2022

Robust Scheduling of Microgrids Considering Unintentional Islanding Conditions

This paper proposes a robust scheduling model for microgrids considering the stochastic unintentional islanding conditions. The proposed model minimizes the total operating cost of the microgrid by efficiently coordinating the supply of power from local distributed energy resources and the main grid. To capture the prevailing uncertainties in renewable generation and demand as well as unintentional islanding conditions, a two-stage adaptive robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (C&CG) method is used to solve the problem in an iterative manner. The solution of the proposed scheduling model ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, two micro-turbines, a diesel generator and a battery demonstrate the effectiveness of the proposed approach.

Liu, Guodong↗
DOE OSTI2021

Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems

This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. Additionally, the developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Tilted lidar profiling: Development and testing of a novel scanning strategy for inhomogeneous flows

The most common profiling techniques for the atmospheric boundary layer based on a monostatic Doppler wind lidar rely on the assumption of horizontal homogeneity of the flow. This assumption breaks down in the presence of either natural or human-made obstructions that can generate significant flow distortions. The need to deploy ground-based lidars near operating wind turbines for the American WAKE experimeNt (AWAKEN) spurred a search for novel profiling techniques that could avoid the influence of the flow modifications caused by the wind farms. With this goal in mind, two well-established profiling scanning strategies have been retrofitted to scan in a tilted fashion and steer the beams away from the more severely inhomogeneous region of the flow. Results from a field test at the National Renewable Energy Laboratory's 135-m meteorological tower show that the accuracy of the horizontal mean flow reconstruction is insensitive to the tilt of the scan, although higher-order wind statistics are severely deteriorated at extreme tilts mainly due to geometrical error amplification. A numerical study of the AWAKEN domain based on the Weather Research and Forecasting Model and large-eddy simulation are also conducted to test the effectiveness of tilted profiling. It is shown that a threefold reduction of the error on inflow mean wind speed can be achieved for a lidar placed at the base of the turbine using tilted profiling.

17 WIND ENERGY↗
DOE OSTI2023

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2025

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Bayesian inference↗
DOE OSTI2025

A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring

We introduce a novel digital twin (DT) framework for the predictive maintenance of long-term physical systems. Using monitoring tire health as an application, we show how the DT framework can be used to enhance automotive safety and efficiency, and how the technical challenges can be overcome using a three-step approach. First, to manage the data complexity over a long operation span, we employ data reduction techniques to concisely represent physical tires using historical performance and usage data. Relying on these data, for fast real-time prediction, we train a transformer-based model offline on our concise dataset to predict future tire health over time, represented as remaining casing potential (RCP). Based on our architecture, our model quantifies both epistemic and aleatoric uncertainties, providing reliable confidence intervals around predicted RCP. Second, to incorporate real-time data, we update the predictive model in the DT framework, ensuring its accuracy throughout its lifespan with the aid of hybrid modeling and the use of the discrepancy function. Third, to assist decision-making in predictive maintenance, we implement a tire state decision algorithm, which strategically determines the optimal timing for tire replacement based on RCP forecasted by our transformer model. This approach ensures that our DT accurately predicts system health, continually refines its digital representation, and supports predictive maintenance decisions. Furthermore, our framework effectively embodies a physical system, leveraging big data and machine learning (ML) for predictive maintenance, model updates, and decision-making.

advanced computing infrastructure↗
DOE OSTI2020

Dynamic Distribution System Restoration Strategy for Resilience Enhancement: Preprint

In electric power distribution systems, distributed energy re-sources (DERs) can act as controllable power sources and support utility operators to minimize power outages after ex-treme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influ-ences of extreme events and the capabilities of DERs are dy-namic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availabil-ity of DERs. In this paper, we propose a new dynamic distribu-tion system restoration strategy to enhance system resilience against potential hazards. An efficient reconfiguration algo-rithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER opera-tion setpoints based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Comparison of Observations and Predictions of Daytime Planetary-Boundary-Layer Heights and Surface Meteorological Variables in the Columbia River Gorge and Basin During the Second Wind Forecast Improvement Project

The second Wind Forecast Improvement Project (WFIP2) is an 18-month field campaign in the Pacific Northwest U.S.A., whose goal is to improve the accuracy of numerical-weather-prediction forecasts in complex terrain. The WFIP2 campaign involved the deployment of a large suite of in situ and remote sensing instrumentation, including eight 915-MHz wind-profiling radars, and surface meteorological stations. The evolution and annual variability of the daytime convective planetary-boundary-layer (PBL) height is investigated using the wind-profiling radars. Three models with different horizontal grid spacing are evaluated: the Rapid Refresh, the High-Resolution Rapid Refresh, and its nested version. The results are used to assess errors in the prediction of PBL height within the experimental and control versions of the models, with the experimental versions including changes and additions to the model parametrizations developed during the field campaign, and the control version using the parametrizations present in the National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction operational version of the models at the start of the project. Results show that the high-resolution models outperform the low-resolution versions, the experimental versions perform better compared with the control versions, model PBL height estimations are more accurate on cloud-free days, and model estimates of the PBL height growth rate are more accurate than model estimates of the rate of decay. Finally, using surface sensors, we assess surface meteorological variables, finding improved surface irradiance and, to a lesser extent, improved 2-m temperature in the experimental version of the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

New Projections for Dark Matter Searches with Paleo-Detectors

Paleo-detectors are a proposed experimental technique to search for dark matter (DM). In lieu of the conventional approach of operating a tonne-scale real-time detector to search for DM-induced nuclear recoils, paleo-detectors take advantage of small samples of naturally occurring rocks on Earth that have been deep underground (≳5 km), accumulating nuclear damage tracks from recoiling nuclei for O(1)Gyr. Modern microscopy techniques promise the capability to read out nuclear damage tracks with nanometer resolution in macroscopic samples. Thanks to their O(1)Gyr integration times, paleo-detectors could constitute nuclear recoil detectors with keV recoil energy thresholds and 100 kilotonne-yr exposures. This combination would allow paleo-detectors to probe DM-nucleon cross sections orders of magnitude below existing upper limits from conventional direct detection experiments. In this article, we use improved background modeling and a new spectral analysis technique to update the sensitivity forecast for paleo-detectors. We demonstrate the robustness of the sensitivity forecast to the (lack of) ancillary measurements of the age of the samples and the parameters controlling the backgrounds, systematic mismodeling of the spectral shape of the backgrounds, and the radiopurity of the mineral samples. Specifically, we demonstrate that even if the uranium concentration in paleo-detector samples is 10−8 (per weight), many orders of magnitude larger than what we expect in the most radiopure samples obtained from ultra basic rock or marine evaporite deposits, paleo-detectors could still probe DM-nucleon cross sections below current limits. For DM masses ≲ 10 GeV/c2, the sensitivity of paleo-detectors could still reach down all the way to the conventional neutrino floor in a Xe-based direct detection experiment.

Baum, Sebastian (ORCID:0000000167929381)↗
DOE OSTI2021

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks

Here we analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several variations of the classical susceptible-infectious-removed (SIR) model by introducing more compartments and fractional-order and time-delay models. We report the results for the spread of COVID-19 in New York City, Rhode Island and Michigan states and Italy, by simultaneously inferring the unknown parameters and the unobserved dynamics. For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks. In contrast, for fractional differential models, we fit the data by determining different time-dependent derivative orders for each compartment, which we represent by neural networks. We investigate the structural and practical identifiability of these unknown functions for different datasets, and quantify the uncertainty associated with neural networks and with control measures in forecasting the pandemic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

First Ever Field Pilot on Alaska's North Slope to Validate the Use of Polymer Floods for Heavy Oil EOR a.k.a Alaska North Slope Field Laboratory (ANSFL)

Alaska’s high viscosity oil resources that range between 20–30+ billion barrels represent about a third of known North Slope original oil in place (OOIP). These resources are primarily concentrated in the Schrader Bluff formation (also called West Sak on the Western North Slope) and Ugnu reservoirs and are categorized as “viscous oils” and “heavy oils” owing to their in-situ viscosities between 5–10,000 cP and up to a million+ cP respectively. The viscous oil deposits are relatively deeper (2,000 – 5,000 ft), whereas the heavy oils are somewhat shallower (2,000 – 4,000 ft). The typically shallow depths and the proximity to the continuous permafrost results in relatively lower formation temperatures and pressures, and consequently higher viscosities. The vertical depth vs. viscosity delineated in Paskvan et al. (2016) differentiates the viscous and heavy oils. As depicted in Paskvan et al. (2016), currently the main focus (referred to as “developing”) is on the viscous oils in the Schrader Bluff formation in the Milne Point Unit (MPU). Notwithstanding this Alaska North Slope (ANS) specific categorization, we use the industry adopted, all-inclusive term “heavy oil” for all high viscosity oils. Resource characterization and additional details can be found in topical publications of Paskvan et al. (2016) and Targac et al. (2005). Despite the vast resource base, the development pace, vis-à-vis the production of heavy oils has been very slow and limited due to multiple factors such as cost, logistics, challenging arctic environment, poor waterflood sweep efficiency due to mobility contrasts, and significantly high minimum miscibility pressures (MMP). Most importantly, typical or standard thermal methods that are commonplace elsewhere (Canada, California) are inapplicable due to the continuous permafrost. As a consequence, cumulative production of heavy and viscous oils is a little over 1% of OOIP slope wide and currently, there is hardly any production from Ugnu. However, on a broader level, these unfavorable factors are outweighed by the fact that (1) these resources, within the established infrastructure, are too large to ignore because of their strategic importance to the Nation and the State of Alaska and (2) Prudhoe Bay type diluent crude oil is still available for heavy oil transport through the Trans Alaska Pipeline System (TAPS). Similarly, from a reservoir standpoint, the following factors also are important offsets: (1) favorable rock characteristics of Schrader Bluff; (2) the promise demonstrated by the initial scoping studies (Seright 2010, 2011) suggesting significant increase of heavy oil recovery using polymer flooding; (3) successful field implementation in Canada, China and elsewhere in the world, and (4) availability of the existing pairs of horizontal injector-producer in Schrader Bluff The foregoing was recognized as the best readily available opportunity for significant investment by the US Department of Energy and the field operator Hilcorp Alaska LLC to conduct the first ever field scale experiment to test the polymer flooding technology to unlock the vast heavy oil resources on ANS. With this primary goal in mind, the research team embarked on a ~4.5 years long project that focused on the field polymer pilot complemented by supporting laboratory and simulation studies. As documented in this final report, over the course of the project, many lessons have been learned and valuable field and supporting laboratory data has been collected, which also is complemented by numerical reservoir simulations. We have been able to establish the injectivity of polymer solution, evidence of significant reduction in the water cut of previously waterflooded pattern, effective propagation of a hydrolyzed polyacrylamide (HPAM), benefits of low salinity water, provide practical guidance on handling of produced fluids containing breakthrough polymer, fit-for-purpose forecast-worthy history matched simulation model, polymer EOR benefit of 700-1000 bopd over waterflood, and most importantly a low polymer utilization factor of ~1.7 lb/stb. In summary this project is deemed as a scientific, technical and economic success, having met all objectives, fulfilled deliverables and within budget, providing impetus to apply polymer EOR throughout the Milne Point Field paving the way for even heavier viscosity oils in the Ugnu area, eventually extending the economic life of TAPS.

02 PETROLEUM↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Space weather observations, modeling, and alerts in support of human exploration of Mars

Space weather observations and modeling at Mars have begun but they must be significantly increased to support the future of Human Exploration on the Red Planet. A comprehensive space weather understanding of a planet without a global magnetosphere and a thin atmosphere is very different from our situation at Earth so there is substantial fundamental research remaining. It is expected that the development of suitable models will lead to a comprehensive operational Mars space weather alert (MSWA) system that would provide rapid dissemination of information to Earth controllers, astronauts in transit, and those in the exploration zone (EZ) on the surface by producing alerts that are delivered rapidly and are actionable. To illustrate the importance of such a system, we use a magnetohydrodynamic code to model an extreme Carrington-type coronal mass ejection (CME) event at Mars. The results show a significant induced surface field of nearly 3,000 nT on the dayside that could radically affect unprotected electrical systems that would dramatically impact human survival on Mars. Other associated problems include coronal mass ejection (CME) shock-driven acceleration of solar energetic particles producing large doses of ionizing radiation at the Martian surface. In summary, along with working more closely with international partners, the next Heliophysics Decadal Survey must include a new initiative to meet expected demands for space weather forecasting in support of humans living and working on the surface of Mars. It will require significant effort to coordinate NASA and the international community contributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Survey Operations for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) survey is a spectroscopic survey of tens of millions of galaxies at 0 < z < 3.5 covering 14,000 sq. deg. of the sky. In its first 1.1 yr of survey operations, it has observed more than 14 million galaxies and 4 million stars. We describe the processes that govern DESI's observations of the 15,000 fields composing the survey. This includes the planning of each night's observations in the afternoon; automatic selection of fields to observe during the night; real-time assessment of field completeness on the basis of observing conditions during each exposure; reduction, redshifting, and quality assurance of each field of targets in the morning following observation; and updates to the list of future targets to observe on the basis of these results. We also compare the performance of the survey with historical expectations and find good agreement. Simulations of the weather and of DESI observations using the real field-selection algorithm show good agreement with the actual observations. After accounting for major unplanned shutdowns, the dark time survey is progressing about 7% faster than forecast, which is good agreement given approximations made in the simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
DOE OSTI2023

First detection of the BAO signal from early DESI data

We present the first detection of the baryon acoustic oscillations (BAOs) signal obtained using unblinded data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). From a selected sample of 261 291 luminous red galaxies spanning the redshift interval 0.4 < z < 1.1 and covering 1651 square degrees with a 57.9 per cent completeness level, we report a ∼5σ level BAO detection and the measurement of the BAO location at a precision of 1.7 per cent. Using a bright galaxy sample of 109 523 galaxies in the redshift range 0.1 < z < 0.5, over 3677 square degrees with a 50.0 per cent completeness, we also detect the BAO feature at ∼3σ significance with a 2.6 per cent precision. These first BAO measurements represent an important milestone, acting as a quality control on the optimal performance of the complex robotically actuated, fibre-fed DESI spectrograph, as well as an early validation of the DESI spectroscopic pipeline and data management system. Based on these first promising results, we forecast that DESI is on target to achieve a high-significance BAO detection at sub-per cent precision with the completed 5-yr survey data, meeting the top-level science requirements on BAO measurements. This exquisite level of precision will set new standards in cosmology and confirm DESI as the most competitive BAO experiment for the remainder of this decade.

79 ASTRONOMY AND ASTROPHYSICS↗