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DOE OSTI2021

Learning Optimal Power Flow Solutions using Linearized Models in Power Distribution Systems

Solving nonlinear optimal power flow (OPF) problem is computationally expensive, and poses scalability challenges for power distribution networks. An alternative to solving the original nonlinear OPF is the linear approximated OPF models. Although, these linear approximated OPF models are fast, the resulting solutions may result in significant optimality gap. Lately, the application of machine learning (ML) methods in successfully solving the nonlinear OPF has been reported. These methods learn and estimate the nonlinear control policies using a purely data-driven approach. In this paper, we propose an approach to complements the ML based approach to solving OPF using solutions from known linearized OPF model. Specifically, we use supervised learning to map the solutions of linear OPF to nonlinear control variables. Unlike, the traditional ML based methods for OPF that approximate the full distribution feeder model using function approximation, our approach uses a two-node approximation of radial networks. The proposed approach is validated using IEEE 123 bus test system for OPF solutions obtained using the nonlinear OPF models.

optimal power flow, power distribution systems, su↗

DOE OSTI2020

Impact of multimode range and location on urban fuel economy on a light-duty spark-ignition based powertrain using vehicle system simulations

Multimode engine operation uses two or more combustion modes to maximize engine efficiency across the operational range of a vehicle to achieve higher overall vehicle fuel economy than is possible with a single combustion mode. More specifically for this study, multimode solutions are explored that make use of boosted SI under high load operation and other advanced combustion modes such as advanced compression ignition (ACI) under part-load conditions to enable additional engine efficiency improvements across a broader range of the engine operating map. ACI combustion has well-documented potential to improve efficiency and emissions under part-load operation but poses challenges that limit full engine speed-load range. This study investigates the potential impact of ACI operational range on simulated fuel economy to help focus research on areas with the most opportunity for improving fuel economy. These simulations make use of a vehicle model, discretized engine data, and employ a systematic exploration of ACI operational range to estimate multimode fuel economy for a mid-size passenger vehicle over U.S. Environmental Protection Agency’s Urban Dynamometer Driving Schedule. The results of this study highlight operational ranges with the highest potential fuel economy and correspondingly areas of focus for multimode research and ACI engine operation1.

Curran, Scott↗

DOE OSTI2024

Quantifying the compound hazard of freezing rain and wind gusts across CONUS

Abstract The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data collected during 2005–2022 to derive hourly ice accumulation at 883 stations across the contiguous USA. These estimates are combined with wind gust observations to compute time series of hourly FZG hazard magnitude using the Sperry–Piltz Ice Accumulation (SPIA) index. Results are evaluated using Storm Reports of damage and economic disruption. The hourly SPIA estimates are also used to (i) derive a geospatial atlas of the hazard including the 50 yr return period event intensities for each US state derived using superstations, and (ii) describe storylines of significant events in terms of meteorological drivers and socioeconomic impacts. The highest values of SPIA during the 18 yr study period occur in a region extending from the Southern Great Plains over the Midwest into the densely populated Northeast. States in these regions also have high 50 yr return period maximum radial ice accumulation of 3–5 cm and co-occurring wind gusts >30 ms −1 . These values are comparable to past estimates for the 500 yr event which may imply this hazard has been previously underestimated. This atlas can be used to inform optimal FZG hazard mitigation strategies for each state/region.

58 GEOSCIENCES↗

DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

XCal: model-based approach to X-ray CT spectral calibration

Transmission X-ray computed tomography (CT) is widely used to quantitatively reconstruct 3D objects composed of multiple materials. However, accurate CT reconstruction requires the system to be calibrated to account for the effective X-ray spectrum. Unfortunately, measurement of the effective spectrum is ill-posed, and existing calibration methods require that the system be recalibrated when the system parameters are changed. In this paper, we propose XCal, a multi-energy model-based spectral calibration approach for X-ray CT. The XCal approach models the effective spectrum using a separable physics-based model of the CT system. The model parameters are then estimated by fitting calibration data with known objects at multiple energies. An important advantage of XCal is that it allows the user to change scanner settings, such as the source voltage or X-ray filters, without the need for recalibration. Evaluations on simulated and measured datasets demonstrate that XCal significantly improves the accuracy of the estimated spectrum as compared to existing calibration methods.

Li, Wenrui [Purdue Univ., West Lafayette, IN (Unit↗

NASA NTRS1983

Settling of fixed erythrocyte suspension droplets

It is pointed out that when particles behave collectively rather than individually, the fractionation of micron-size particles on the basis of size, density, and surface characteristics by centrifugation and electrophoresis is hindered. The formation and sedimentation of droplets containing particles represent an extreme example of collective behavior and pose a major problem for these separation methods when large quantities of particles need to be fractionated. Experiments are described that measure droplet sizes and settling rates for a variety of particles and droplets. Expressions relating the particle concentration in a drop to measurable quantities of the fluids and particles are developed. The number of particles in each droplet is then estimated, together with the effective droplet density. Red blood cells from different animals fixed in glutaraldehyde provide model particle groups.

Omenyi, S. N.↗

NASA NTRS2008

Examination of Observation Impacts derived from OSEs and Adjoint Models

With the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast skill can be estimated accurately and efficiently. The approach allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously. In this study, adjoint-based estimates of observation impact are compared with results from standard observing system experiments (OSEs) in the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) GEOS-5 system. The two approaches are shown to provide unique, but complimentary, information. Used together, they reveal both redundancies and dependencies between observing system impacts as observations are added or removed. Understanding these dependencies poses a major challenge for optimizing the use of the current observational network and defining requirements for future observing systems.

Gelaro, Ronald↗

DOE OSTI2024

Isotopic gamma lines for identification of shielding materials

Identifying the constituting materials of concealed objects is crucial in a wide range of sectors, such as medical imaging, geophysics, nonproliferation, national security investigations, and so on. Existing methods face limitations, particularly when multiple materials are involved or when there are challenges posed by scattered radiation and large areal mass. Here we introduce a novel brute-force statistical approach for material identification using high spectral resolution detectors, such as HPGe. The method relies upon updated semianalytic formulae for computing uncollided flux from source of gamma radiation, shielded by a sequence of nested spherical or cylindrical materials. These semianalytical formulae make possible rapid flux estimation for material characterization via combinatorial search through all possible combinations of materials, using a high-resolution HPGe counting detector. An important prerequisite for the method is that the geometry of the objects is known (for example, from X-ray radiography). We demonstrate the viability of this material characterization technique in several use cases with both simulated and experimental data in spherical geometry.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach

Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.

25 ENERGY STORAGE↗

DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗

DOE OSTI2022

Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling

Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

International Space Station Noise Constraints Flight Rule Process

Crewmembers onboard the International Space Station (ISS) live in a unique workplace environment for as long as 6 ‐12 months. During these long‐duration ISS missions, noise exposures from onboard equipment are posing concerns for human factors and crewmember health risks, such as possible reductions in hearing sensitivity, disruptions of crew sleep, interference with speech intelligibility and voice communications, interference with crew task performance, and reduced alarm audibility. The purpose of this poster is to describe how a recently‐updated noise constraints flight rule is being used to implement a NASA‐created Noise Exposure Estimation Tool and Noise Hazard Inventory to predict crew noise exposures and recommend when hearing protection devices are needed.

Limardo, Jose G.↗

DOE OSTI2024

Assessment of Bird Strike Likelihood to Refine Bird Strike Risk Models

In its most basic form, bird strike risk is comprised of a frequency component that reflects the likelihood of a collision and a severity component that reflects the cost (monetary or otherwise) of the incident. The bird strike risk model currently used by United State Department of Agriculture (USDA) Wildlife Services to evaluate the risk posed by individual bird species at airports and establish priorities for management was developed in 2018. The model uses airport-specific data on the number of reported strikes for a species recorded in the Federal Aviation Administration (FAA)’s National Wildlife Strike Database as a measure of frequency and the species’ relative hazard score as a measure of severity. The model was tested against independent data, found to perform well overall, and is being implemented widely across the United States. However, the model has limitations, including that species known to pose risk to aircraft locally, but not present in the strike record database, are not reflected as a major component of risk. Standard bird survey methodology commonly used at airports (e.g. point counts or transects) potentially can be used to complement wildlife strike records to calculate frequency or relative abundance of species. However, these methods generally focus on airport-wide population estimation and often ignore vital information that contributes to the true likelihood of a strike, such as use of runway protection zones and other critical areas, and spatial and temporal overlap with departing or approaching aircraft. As such, a more detailed understanding of space use by birds across landcovers and population fluctuations across the year is needed to accurately estimate the likelihood of bird strikes at airports. In this manuscript, we will review the extant risk model, including a discussion on its limitations. We then discuss approaches for refining our understanding of strike likelihood and briefly touch on needs for estimating probability of strike severity (cost).

bird strike, aircraft collision, damage by wildlif↗

NASA NTRS1994

The Contemporary Hazard of Cometary Impacts

Cosmic impacts pose a continuing hazard of loss of human life and property. Significant contemporary risk is associated with projectiles in the energy range from about 10 megatons of TNT up to the size of the K/T impactor. The lower threshold for damage is defined by the atmosphere of the Earth, which effectively shields us From smaller projectiles. Up to energies of about a gigaton of TNT, the effects are local or regional for impacts on the land, or coastal for ocean impacts, which can generate large tsunamis. A greater risk is associated with still larger impacts, which are capable of causing global ecological catastrophe, possibly leading to mass mortality From starvation and epidemics. If such a impact took place anywhere on Earth during our lifetimes, we would each be in danger, independent of where the projectile struck. Statistical estimates indicate that each human on this planet runs a risk of roughly 1 in 20,000 of dying from this cause. Prudence suggests that we should be concerned about such impacts and seek ways of avoiding them or mitigating their consequences. The primary objective of any program to deal with this hazard is to determine whether or not such a near-term impact is likely. The best approach for the asteroidal component is a comprehensive telescopic survey, which can discover all Earth-crossing asteroids larger than 1 km in diameter and provide decades of warning in which to plan ways to deflect or destroy a threatening object. Long-period comets, however, pose a much greater challenge, since they cannot be discovered long in advance of a possible impact, their orbits are harder to predict, and they are significantly more difficult to deflect or destroy.

Morrison, David↗

NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

Bucket Drum Excavators↗

NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

bucket drum excavators↗

DOE OSTI2025

Resilient State Recovery Using Prior Measurement Support Information

Resilient state recovery of cyber-physical systems has attracted much research attention due to the unique challenges posed by the tight coupling between communication, computation, and the underlying physics of such systems. By modeling attacks as additive adversary signals to a sparse subset of measurements, this resilient recovery problem can be formulated as an error correction problem. To achieve exact state recovery, most existing results require less than 50% of the measurement nodes to be compromised, which limits the resiliency of the estimators. In this paper, we show that observer resiliency can be further improved by incorporating data-driven prior information. Here, we provide an analytical bridge between the precision of prior information and the resiliency of the estimator. By quantifying the relationship between the estimation error of the weighted ℓ 1 observer and the precision of the support prior, this quantified relationship provides guidance for the estimator’s weight design to achieve optimal resiliency. Several numerical simulations and an application case study are presented to validate the theoretical claims.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2011

Small Orbital Debris Mitigation Mission Architecture

Small orbital debris in LEO (1-10 cm in size) presents a clear and present danger to operational LEO spacecraft. This debris field has dramatically increased (nearly doubled) in recent years following the Chinese ASAT Test in 2007 and the Iridium/Cosmos collision in 2009. Estimates of the number of small debris have grown to 500,000 objects after these two events; previously the population was 300,000 objects. These small, untracked debris objects (appproximately 500,000) outnumber the larger and tracked objects (appproximately 20,000) by a factor 25 to 1. Therefore, the risk of the small untracked debris objects to operational spacecraft is much greater than the risk posed by the larger and tracked LEO debris objects. A recent study by The Aerospace Corporation found that the debris environment will increase the costs of maintaining a constellation of government satellites by 5%, a constellation of large commercial satellites by 11%, and a constellation of factory built satellites by 26% from $7.6 billion to $9.57 billion. Based upon these facts, the NASA Marshall Space Flight Center (MSFC) Advanced Concepts Office (ACO) performed an architecture study on Small Orbital Debris Active Removal (SODAR) using a space-based nonweapons- class laser satellite for LEO debris removal. The goal of the SODAR study was to determine the ability of a space-based laser system to remove the most pieces of debris (1 cm to 10 cm, locations unknown), in the shortest amount of time, with the fewest number of spacecraft. The ESA developed MASTER2005 orbital debris model was used to probabilistically classify the future debris environment including impact velocity, magnitude, and directionality. The study ground rules and assumptions placed the spacecraft into a high inclination Low Earth Orbit at 800 km as an initial reference point. The architecture study results found that a spacecraft with an integrated forward-firing laser is capable of reducing the small orbital debris flux within a 60 to 100 km orbital shell by a significant amount within the one spacecraft's operational lifetime. The technology developments required for such an architecture to be successfully employed are: 1) A pico-pulsed, space qualified laser and 2) The ability to detect and track a small LEO small debris object from a space based platform. Therefore, a conceptual design for a demonstration satellite to showcase the ability to detect and track small orbital debris was completed

Wiegmann, Bruce M.↗