Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Knowledge constraint”

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.

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

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Analysis and optimization of seismic monitoring networks with Bayesian optimal experimental design

SUMMARY Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types and fidelity in order to improve our ability to identify and locate seismic sources. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network’s ability to locate seismic events from arrival time data of detected seismic phases at the regional-scale. This framework requires five elements: (i) A likelihood function that describes the distribution of detection and traveltime data from the sensor network, (ii) A prior distribution that describes a priori belief about seismic events, (iii) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, (iv) An algorithm to compute EIG about seismic events over a data set of hypothetical prior events, (v) An optimizer that finds a sensor network which maximizes EIG. Once we have developed this framework, we explore many relevant questions to monitoring such as: how to trade off sensor fidelity and earth model uncertainty; how sensor types, number and locations influence uncertainty; and how prior models and constraints influence sensor placement.

58 GEOSCIENCES↗
DOE OSTI2023

Tetratricopeptide repeat protein SlREC2 positively regulates cold tolerance in tomato

Abstract Cold stress is a key environmental constraint that dramatically affects the growth, productivity, and quality of tomato (Solanum lycopersicum); however, the underlying molecular mechanisms of cold tolerance remain poorly understood. In this study, we identified REDUCED CHLOROPLAST COVERAGE 2 (SlREC2) encoding a tetratricopeptide repeat protein that positively regulates tomato cold tolerance. Disruption of SlREC2 largely reduced abscisic acid (ABA) levels, photoprotection, and the expression of C-REPEAT BINDING FACTOR (CBF)-pathway genes in tomato plants under cold stress. ABA deficiency in the notabilis (not) mutant, which carries a mutation in 9-CIS-EPOXYCAROTENOID DIOXYGENASE 1 (SlNCED1), strongly inhibited the cold tolerance of SlREC2-silenced plants and empty vector control plants and resulted in a similar phenotype. In addition, foliar application of ABA rescued the cold tolerance of SlREC2-silenced plants, which confirms that SlNCED1-mediated ABA accumulation is required for SlREC2-regulated cold tolerance. Strikingly, SlREC2 physically interacted with β-RING CAROTENE HYDROXYLASE 1b (SlBCH1b), a key regulatory enzyme in the xanthophyll cycle. Disruption of SlBCH1b severely impaired photoprotection, ABA accumulation, and CBF-pathway gene expression in tomato plants under cold stress. Taken together, this study reveals that SlREC2 interacts with SlBCH1b to enhance cold tolerance in tomato via integration of SlNCED1-mediated ABA accumulation, photoprotection, and the CBF-pathway, thus providing further genetic knowledge for breeding cold-resistant tomato varieties.

Zhang, Ying (ORCID:0000000169841164)↗
DOE OSTI2025

Ensuring continued operation of INSPIRE as a cornerstone of the HEP information infrastructure

The INSPIRE platform — the most widely-used discovery service specifically tailored to the needs of researchers in High Energy Physics (HEP) — has become a central component of the information infrastructure for the discipline. Despite this, INSPIRE's continued sustainability is frequently endangered by resource constraints, recently made more acute by the loss of support from historical funders changing their research priorities. If the European particle physics community wishes to ensure INSPIRE's long-term sustainability, the community should secure international support and ensure appropriate funding.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

A numerical framework for simulating the atmospheric variability of supermicron marine biogenic ice nucleating particles

Abstract. We present a framework for estimating concentrations of episodically elevated high-temperature marine ice nucleating particles (INPs) in the sea surface microlayer and their subsequent emission into the atmospheric boundary layer. These episodic INPs have been observed in multiple ship-based and coastal field campaigns, but the processes controlling their ocean concentrations and transfer to the atmosphere are not yet fully understood. We use a combination of empirical constraints and simulation outputs from an Earth system model to explore different hypotheses for explaining the variability of INP concentrations, and the occurrence of episodic INPs, in the marine atmosphere. In our calculations, we examine the following two proposed oceanic sources of high-temperature INPs: heterotrophic bacteria and marine biopolymer aggregates (MBPAs). Furthermore, we assume that the emission of these INPs is determined by the production of supermicron sea spray aerosol formed from jet drops, with an entrainment probability that is described by Poisson statistics. The concentration of jet drops is derived from the number concentration of supermicron sea spray aerosol calculated from model runs. We then derive the resulting number concentrations of marine high-temperature INPs (at 253 K) in the atmospheric boundary layer and compare their variability to atmospheric observations of INP variability. Specifically, we compare against concentrations of episodically occurring high-temperature INPs observed during field campaigns in the Southern Ocean, the Equatorial Pacific, and the North Atlantic. In this case study, we evaluate our framework at 253 K because reliable observational data at this temperature are available across three different ocean regions, but suitable data are sparse at higher temperatures. We find that heterotrophic bacteria and MBPAs acting as INPs provide only a partial explanation for the observed high INP concentrations. We note, however, that there are still substantial knowledge gaps, particularly concerning the identity of the oceanic INPs contributing most frequently to episodic high-temperature INPs, their specific ice nucleation activity, and the enrichment of their concentrations during the sea–air transfer process. Therefore, targeted measurements investigating the composition of these marine INPs and drivers for their emissions are needed, ideally in combination with modeling studies focused on the potential cloud impacts of these high-temperature INPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗
DOE OSTI2023

Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model

Abstract. Comprehensive assessment of climate datasets is important for communicating model projections and associated uncertainties to stakeholders. Uncertainties can arise not only from assumptions and biases within the model but also from external factors such as computational constraint and data processing. To understand sources of uncertainties in global variable-resolution (VR) dynamical downscaling, we produced a regional climate dataset using the Model for Prediction Across Scales (MPAS; dynamical core version 4.0) coupled to the Community Atmosphere Model (CAM; version 5.4), which we refer to as CAM–MPAS hereafter. This document provides technical details of the model configuration, simulations, computational requirements, post-processing, and data archive of the experimental CAM–MPAS downscaling data. The CAM–MPAS model is configured with VR meshes featuring higher resolutions over North America as well as quasi-uniform-resolution meshes across the globe. The dataset includes multiple uniform- (240 and 120 km) and variable-resolution (50–200, 25–100, and 12–46 km) simulations for both the present-day (1990–2010) and future (2080–2100) periods, closely following the protocol of the North American Coordinated Regional Climate Downscaling Experiment. A deviation from the protocol is the pseudo-warming experiment for the future period, using the ocean boundary conditions produced by adding the sea surface temperature and sea-ice changes from the low-resolution version of the Max Planck Institute Earth System Model (MPI-ESM-LR) in the Coupled Model Intercomparison Project Phase 5 to the present-day ocean state from a reanalysis product. Some unique aspects of global VR models are evaluated to provide background knowledge to data users and to explore good practices for modelers who use VR models for regional downscaling. In the coarse-resolution domain, strong resolution sensitivity of the hydrological cycles exists over the tropics but does not appear to affect the midlatitude circulations in the Northern Hemisphere, including the downscaling target of North America. The pseudo-warming experiment leads to similar responses of large-scale circulations to the imposed radiative and boundary forcings in the CAM–MPAS and MPI-ESM-LR models, but their climatological states in the historical period differ over various regions, including North America. Such differences are carried to the future period, suggesting the importance of the base state climatology. Within the refined domain, precipitation statistics improve with higher resolutions, and such statistical inference is verified to be negligibly influenced by horizontal remapping during post-processing. Limited (≈50 % slower) throughput of the current code is found on a recent many-core/wide-vector high-performance computing system, which limits the lengths of the 12–46 km simulations and indirectly affects sampling uncertainty. Our experience shows that global and technical aspects of the VR downscaling framework require further investigations to reduce uncertainties for regional climate projection.

58 GEOSCIENCES↗
DOE OSTI2022

Qualitative trend analysis based on a mixed-integer representation

Shape constrained spline fitting is a useful method to impose prior knowledge onto flexible semi-parametric models during parameter estimation. Most typically, the function shape is imposed through order restrictions on the regression coefficients. The intended shape is considered known or selected based on heuristic rules. In this study, we present a method to estimate the optimal set of order restrictions to segment a univariate data series into episodes with distinct shapes. This is also known as the qualitative trend analysis (QTA) problem. The obtained solution uses a trade-off between lack-of-fit and model complexity. Further, our practical implementation takes inspiration from the generalized order restricted information criterion (GORIC) for inequality-constrained model selection. From this, one learns (a) that QTA can be formulated as a mixed-integer quadratic program (MIQP) and (b) that the newly proposed mixed order restricted information criterion (MORIC) enables optimal segmentation. This is illustrated through didactic case studies.

42 ENGINEERING↗
DOE OSTI2025

Wind Supply Chain Security: Hardware Enumeration and Analysis

This project, undertaken by Idaho National Laboratory (INL) for the Department of Energy (DOE) Wind Energy Technologies Office (WETO), focused on the enumeration and analysis of six key devices important to wind technologies. The devices analyzed included Beckhoff Bus Terminal Controllers (BK1120 and BC9000), a Beckhoff Economy Built-in Panel PC (CP6231), an N-Tron Managed Industrial Ethernet Switch (711FX3), a Bachmann M1 Gateway, and a Bachmann Smart Power Plant Controller. Device selection was driven by availability and budget constraints, with several components sourced from existing wind farms and others procured through a co-agreement with another WETO-funded project. The project's primary objective was to create a hardware bill of materials (HBOM) for each device, identifying and documenting all components to assess potential security and supply chain risks. A detailed analysis revealed over 750 unique components across the six devices, with 80% successfully identified and accompanied by datasheets. Notably, Texas Instruments emerged as the leading supplier, providing over 16% of the components, followed by ON Semiconductor at 11.3%, Analog Devices at 5.3%, and Renesas Electronics Corp at 4.1%. Other notable vendors included Toshiba Corporation, iC-Haus Corporation, Atmel, Vishay, and STMicroelectronics. The enumeration process involved thorough documentation of each component, including its designation, quantity, identifiers, pin package, description, vendor, model, and country of origin. This process provided valuable insights into the complexity and diversity of the electronic systems within these wind devices. It also highlighted the distinct separation of components between vendors, suggesting a trend of vendor-specific component usage. Key findings from the project emphasized the importance of broadening the scope of vendor analysis in future research to gain a comprehensive understanding of component distribution and commonality. The identification of vendor-specific component usage patterns offers new avenues for research and underscores the significance of continued investigation in this field. Overall, this project provides critical insights into the component composition of wind devices, aiding in the development of improved supply chain management and component sourcing strategies. The results contribute valuable knowledge to the wind technology sector, laying the groundwork for enhanced security and resilience in wind energy systems.

17 - WIND ENERGY↗
DOE OSTI2020

Advances in Resin Management Using 3R-Scan - 20154

The most important factor underlying optimal waste management is developing a clear picture of the radioactivity content of the waste and its impact on waste disposal cost. For over 35 years since the publishing of 10CFR61, waste characterization has relied on sampling the final waste product after formation. In the days following 10CFR61, the cost of final disposal was marginal with only a small impact on the overall costs. Constraints were added by provisions of the Low Level Waste Policy Act of 1985 leading to increasingly limited access to those disposal sites that remained available. In addition, Nuclear Regulatory Commission (NRC) pressure promoting waste volume reduction led to disposal costs inevitably rising. Despite this, characterization practices in monitoring of waste generation for activity content still center on the same dated processes. This results in a disposal classification on the basis of endpoint sampling without consideration of the homogeneity of the waste mixture. It can also disregard consideration of the representativeness of the single or small sample base. As a minimum effort, a formalized sampling program of a fixed grouping of waste streams can be implemented that could account for more than 95% of all of the activity carried in solid waste products. The sample results for each radionuclide could then be trended as time passes to develop reasonable scaling factors for difficult to measure radionuclides. This process, identified in NRC guidance, has been rigorously followed by a relatively small number of facilities. The trended scaling factors serve to improve accuracy by identifying anomalous results that could otherwise go undetected. Direct monitoring of the accumulation of activity in process streams generating solid radwaste, including demineralizers and filter streams, is a more precise approach. Nearly all of these streams are monitored by plant chemistry on a regular schedule to maintain water quality. This paper discusses viable options for developing the basis for characterization through process monitoring of the accumulation of activity at the point of generation. Special focus is on resin bed tracking and how process knowledge of these streams can be brought together to form a consistent and precise solid waste radioactivity inventory. Some of the specific points covered in this paper include the merger of the fission product release computer program, 3R-STAT, with the radwaste sample analysis computer program, SCAN4 to create 3R-SCAN, the importance of individual waste stream influences on the overall source term, and the use of historic sample data to develop scaling factors using an automated process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A New Method for High Resolution Surface Change Detection: Data Collection and Validation of Measurements from UAS at the Nevada National Security Site, Nevada, USA

The use of uncrewed aerial systems (UAS) increases the opportunities for detecting surface changes in remote areas and in challenging terrain. Detecting surface topographic changes offers an important constraint for understanding earthquake damage, groundwater depletion, effects of mining, and other events. For these purposes, changes on the order of 5–10 cm are readily detected, but sometimes it is necessary to detect smaller changes. An example is the surface changes that result from underground explosions, which can be as small as 3 cm. Previous studies that described change detection methodologies were generally not aimed at detecting sub-5-cm changes. Additionally, studies focused on high-fidelity accuracy were either computationally modeled or did not fully provide the necessary examples to highlight the usability of these workflows. Detecting changes at this threshold may be critical in certain applications, such as global security research and monitoring for high-consequence natural hazards, including landslides. Here we provide a detailed description of the methodology we used to detect 2–3 cm changes in an important applied research setting—surface changes related to underground explosions. This methodology improves the accuracy of change detection data collection and analysis through the optimization of pre-field planning, surveying, flight operations, and post-processing the collected data, all of which are critical to obtaining the highest output data resolution possible. We applied this methodology to a field study location, collecting 1.4 Tb of images over the course of 30 flights, and location data for 239 ground control points (GCPs). We independently verified changes with orthoimagery, and found that structure-from-motion, software-reported root mean square errors (RMSEs) for both control and check points underestimated the actual error. We found that 3 cm changes are detectable with this methodology, thereby improving our knowledge of a rock’s response to underground explosions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Physics-constrained Gaussian process model for prediction of hydrodynamic interactions between wave energy converters in an array

To improve the efficiency of wave farms and achieve maximum power generation, the layout of wave energy converters (WECs) in an array needs to be carefully designed so that the hydrodynamic interactions can be positively exploited. For this, the hydrodynamic characteristics of the WEC array in different layouts need to be calculated. However, such calculations using numerical models usually entail significant computational cost, especially for large arrays of WECs. To address the computational challenge, a physics-constrained Gaussian process (GP) model is proposed to replace the original expensive numerical model and predict the hydrodynamic characteristics of the WECs for any array layout. By exploring the relationship between the WEC array (i.e., the input) and different hydrodynamic characteristics (i.e., the output), here we summarize a set of physical constraints/features, including invariance, symmetry, and additivity. This prior knowledge about the input-output relationship is then directly embedded in the constructed GP model through the design of physics-constrained kernels. In particular, a double-sum invariant kernel is first developed to incorporate the invariance and symmetry features, and then an additive kernel is developed to incorporate the additive feature of the problem. The invariant kernel and the additive kernel are then integrated to construct the physics-constrained GP model. Compared to the standard GP model, the proposed physics-constrained GP models require less training data to achieve the desired accuracy in predicting the hydrodynamic characteristics and are also less vulnerable to the curse of dimensionality (i.e., good scalability for large arrays) due to the use of an additive kernel. The efficiency, accuracy, and scalability of the proposed approach are demonstrated through an application to predict the hydrodynamic characteristics for WEC arrays of different sizes and layouts.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Medium and Heavy Duty Vehicle Powertrain Electrification and Fleet Demonstration

The Contract Award Grant # EE0007513 dated 2 June, 2016 is for the design, development and demonstration of medium duty package delivery vehicles that will result in a 100% increase in fuel economy as measured in miles per gallon. The project was proposed in 3 phases over two budget periods. The Phases were: 1. Modeling and Simulation – This phase was completed and formed the basis of the specifications for the design and build of the physical systems. The modeling and simulation was conducted by AVL and used real world package delivery routes that AVL mapped. The motors, battery pack, range extender and balance of plant support systems were modeled and iterated until a system with the desired fuel economy improvement was realized. Early design of the major subsystems using the actual vehicle space constraints was also completed. This phase was successfully completed in May of 2017. 2. 1st Build and FE Confirmation - This phase was completed and resulted in the final design, release, procurement and build of all major vehicle systems for commissioning into the first vehicle. The work was a partnered effort between McLaren and AVL. McLaren built the eAxle system while AVL built the range extender and integrated the range extender, battery and balance of plant sub-systems into the first vehicle with a control system to make it operate. Prior to the end of this phase a real world HIL system test gave evidence that the fuel economy improvement would be achieved in the real world. This phase was exited with a 7 month delay due mostly to procurement and supply base issues and ended in May, 2018. 3. Final Build and Demonstration - This phase included the final build and commissioning of the four program vehicles and the operation of a demonstration on a set of routes near San Diego California. During this phase, a multitude of issues arose in different systems in the vehicle. These issue resolution exercises were quite complex and caused repeated delays but were all successfully completed. It should also be noted that an early commercialization analysis resulted in the conclusion that the complexity of the system would not make it cost effective, leading to the start of a follow-on design that used newly available component technologies to specify a more cost effective system. This design will be repurposed and put into demonstration on a separately (non-DOE) funded program. It was decided and agreed with DOE that this phase should be considered completed. The knowledge and know-how gained on this program has been incorporated into and transferred to the new program. This phase and the entire program was considered closed on June 1, 2021.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

CODEX clusters

Context: Large area catalogs of galaxy clusters constructed from ROSAT All-Sky Survey provide the basis for our knowledge of the population of clusters thanks to long-term multiwavelength efforts to follow up observations of these clusters. Aims: The advent of large area photometric surveys superseding previous, in-depth all-sky data allows us to revisit the construction of X-ray cluster catalogs, extending the study to lower cluster masses and higher redshifts and providing modeling of the selection function. Methods: We performed a wavelet detection of X-ray sources and made extensive simulations of the detection of clusters in the RASS data. We assigned an optical richness to each of the 24 788 detected X-ray sources in the 10 382 square degrees of the Baryon Oscillation Spectroscopic Survey area using red sequence cluster finder redMaPPer version 5.2 run on Sloan Digital Sky Survey photometry. We named this survey COnstrain Dark Energy with X-ray (CODEX) clusters. Results: We show that there is no obvious separation of sources on galaxy clusters and active galactic nuclei (AGN) based on the distribution of systems on their richness. This is a combination of an increasing number of galaxy groups and their selection via the identification of X-ray sources either by chance or by groups hosting an AGN. To clean the sample, we use a cut on the optical richness at the level corresponding to the 10% completeness of the survey and include it in the modeling of the cluster selection function. We present the X-ray catalog extending to a redshift of 0.6. Conclusions: The CODEX suvey is the first large area X-ray selected catalog of northern clusters reaching fluxes of 10 –13 ergs s –1 cm –2 . We provide modeling of the sample selection and discuss the redshift evolution of the high end of the X-ray luminosity function (XLF). Our results on z < 0.3 XLF agree with previous studies, while we provide new constraints on the 0.3 < z < 0.6 XLF. We find a lack of strong redshift evolution of the XLF, provide exact modeling of the effect of low number statistics and AGN contamination, and present the resulting constraints on the flat ΛCDM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Vertical profiles of light absorption and scattering associated with black carbon particle fractions in the springtime Arctic above 79° N

Despite the potential importance of black carbon (BC) for radiative forcing of the Arctic atmosphere, vertically resolved measurements of the particle light scattering coefficient (σ sp ) and light absorption coefficient (σ ap ) in the springtime Arctic atmosphere are infrequent, especially measurements at latitudes at or above 80° N. Here, relationships among vertically distributed aerosol optical properties (σap, σsp and single scattering albedo or SSA), particle microphysics and particle chemistry are examined for a region of the Canadian archipelago between 79.9 and 83.4° N from near the surface to 50 hPa. Airborne data collected during April 2015 are combined with ground-based observations from the observatory at Alert, Nunavut and simulations from the Goddard Earth Observing System (GEOS) model, GEOS-Chem, coupled with the TwO-Moment Aerosol Sectional (TOMAS) model (collectively GEOS-Chem–TOMAS; Kodros et al., 2018) to further our knowledge of the effects of BC on light absorption in the Arctic troposphere. The results are constrained for σsp less than 15 Mm -1 , which represent 98% of the observed σsp, because the single scattering albedo (SSA) has a tendency to be lower at lower σsp, resulting in a larger relative contribution to Arctic warming. At 18.4 m 2 g -1 , the average BC mass absorption coefficient (MAC) from the combined airborne and Alert observations is substantially higher than the two averaged modelled MAC values (13.6 and 9.1 m2 g -1 ) for two different internal mixing assumptions, the latter of which is based on previous observations. The higher observed MAC value may be explained by an underestimation of BC, the presence of small amounts of dust and/or possible differences in BC microphysics and morphologies between the observations and model. In comparing the observations and simulations, we present σap and SSA, as measured, and σ ap 2 and the corresponding SSA to encompass the lower modelled MAC that is more consistent with accepted MAC values. Median values of the measured σap, rBC and the organic component of particles all increase by a factor of 1.8±0.1, going from near-surface to 750 hPa, and values higher than the surface persist to 600 hPa. Modelled BC, organics and σap agree with the near-surface measurements but do not reproduce the higher values observed between 900 and 600 hPa. The differences between modelled and observed optical properties follow the same trend as the differences between the modelled and observed concentrations of the carbonaceous components (black and organic). Model-observation discrepancies may be mostly due to the modelled ejection of biomass burning particles only into the boundary layer at the sources. For the assumption of the observed MAC value, the SSA range between 0.88 and 0.94, which is significantly lower than other recent estimates for the Arctic, in part reflecting the constraint of σsp<15 Mm -1 . The large uncertainties in measuring optical properties and BC, and the large differences between measured and modelled values here and in the literature, argue for improved measurements of BC and light absorption by BC and more vertical profiles of aerosol chemistry, microphysics and other optical properties in the Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hawai‘i Play Fairway (Final Report)

Most of Hawai'i's geothermal resources are blind—their manifestations, such as hot springs and steam vents, do not appear on the ground surface because the heated water flows far below. With the exception of $K\bar{i}lauea East Rift Zone$, in most areas of Hawai'i, high lateral permeability in the first kilometer below ground surface prevents surface thermal features from developing. As a methodology for discovering these blind resources, Play Fairway Analysis (PFA) involves finding potential locations of blind hydrothermal systems and describing potential geothermal sources in rift-zone settings. Using the PFA to find Hawai'i's geothermal resources, the University of Hawai'i (UH) conducted the Hawai‘i Play Fairway Project, Hawai'i's first statewide geothermal resource assessment since 1985. Sponsored by the U.S. Department of Energy, the Hawai'i Play Fairway Project provided an updated resource assessment, a roadmap for additional exploration activities, and the identification of areas for further exploration. Benefitting from UH's core competency in earth sciences and experienced geothermal researchers, the project comprised three phases. During the first phase, the team identified, compiled, and ranked existing geologic, groundwater, and geophysical datasets relevant to subsurface heat, fluid and permeability. Using a Bayesian statistical approach, the team developed a statistical methodology to integrate these data into a resource probability map. The team evaluated the confidence in the probability value and considered development viability of areas with geothermal resources. With these analyses, the team identified 10 locations in the Hawaiian Islands for exploration activities. For the second phase, the team collected new groundwater data in 10 locations across the state and new geophysical data on $L\bar{a}na‘i, Maui$, and central Hawai'i Island and modeled topographically induced stress to better characterize subsurface permeability. Analyzing the subsurface stresses, the team evaluated the potential for fracture-induced permeability. The team inverted the MT and gravity data to produce 3D models of resistivity and density, respectively, on $L\bar{a}na‘i$, across $Haleakal\bar{a}'s$ SW rift (Maui), and surrounding Mauna Kea (Hawai‘i Island). The team developed and applied a new method for incorporating depth information about resistivity, density, and potential for fracture-induced permeability into the statistical method for computing resource probability in these three focus areas. The team incorporated the new groundwater results with the new geophysical results and the calculations of potential for fracture-induced permeability to produce updated maps of resource probability and confidence. Through combining data from the first and second phases, the team determined locations for further exploration during the third phase. For MT and gravity surveys, the team recommended $Kaua'i's$ $L\bar{i}hu'e$ $Basin$, the east rift of $Maui's$ $Haleakal\bar{a}$ volcano, and the southwest rift of Hawai'i Island's Mauna Loa volcano. The MT and gravity surveys aimed to enable improved confidence in the resource potential in these locations. For drilling deep groundwater well(s), the team recommended Southeast Mauna Kea and $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$. During the third phase, further exploration involved drilling a groundwater well in $L\bar{a}na's$ $P\bar{a}l\bar{a}wai$ $Basin$ and performing more geophysical surveys. We deepened an existing water well proximal to our target area on $L\bar{a}na'i$ due to funding constraints that precluded us from spudding a new well that would exceed 1km depth. Drilling was preceded by a number of substantial elements including: writing an Environmental Assessment and the subsequent legal process, performance of deviation logging, lowering a camera down the well, coordinating site preparation with $P\bar{u}lama$ $L\bar{a}na'i$, shipping the UH-owned rig interisland, procuring supplies, and leading 3 community meetings on $L\bar{a}na'i$. Drilling occurred 24/7 the entire month of June 2019 over which time $L\bar{a}na'i$ $Well$ 10 was deepened from 427 m to 1057 m, with continuous core collected. We measured a roughly linear temperature gradient averaging 42°C/km and a maximum bottom hole temperature of 66°C. This gradient is more than twice the background for Hawai'i and within a range of gradients measured in this depth range for some exploration wells within KERZ. We consider these results encouraging for $L\bar{a}na'i's$ resource potential and recommend following with a slim hole within $L\bar{a}na'i's$ caldera (our target zone) to ~ 2 km. Further, the positive implications such results have for the island of O‘ahu are substantial - the shield stage of O'ahu's volcanoes ended 1-2 My earlier. However, O'ahu uses more electricity than the rest of the islands combined, and the utility recently called for 500-700MW of firm, dispatchable renewable electricity on O'ahu by 2033. In Phase 3, we also collected limited new encouraging groundwater data, and updated our thoughts on the probabilities of fluid and permeability at resource depths (Pr F = 1; Pr P = mostly unconstrained). Ultimately, we advocate for using our final probability of heat, and confidence in this probability, to drive the next phase of exploration. We contend further development of geothermal in Hawai‘i will enable the state to achieve its 100% renewable policy objective and Hawai'i to transition off of fossil fuels through geothermal discovery and development. The project not only produced a large amount of data and expanded the existing knowledge of Hawai'i's geothermal resources, but also produced publications, theses, presentations, core photos, datasets, media reports, television interviews, community events, and a blog. Students and new professionals benefitted from the project's hands-on research experiences and educational opportunities and earned awards and recognition.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Enhancing Cloud Cybersecurity: Prescriptive Controls for Operational Technology

This whitepaper provides strategic insights and recommendations into security cloud-based solutions for electric utilities, encompassing operational technology (OT), virtual power plants (VPP), distributed energy resources (DERs), applications, networks, and data storage as they transition to and leverage cloud infrastructure through managed service providers (MSPs) and cloud service providers (CSPs). Principles derived from established frameworks serve as a foundation for best practices across cybersecurity projects and remove the constraints of settling on a single framework. For organizations that prefer not to integrate a specific framework altogether, elements of the proposed approach could be adopted or tailored to best fit defined requirements and expected functionalities. The Cirrus assessment, a utility cloud feasibility tool, and the roadmap it provides serve as a precursor to this paper, which seeks to be a valuable resource for defining next steps following cloud technology integration feasibility appraisal. With its comprehensive approach to adoption, the Cirrus framework offers strategic guidance on responsibly preparing for or deploying a utility cloud solution. The previously published whitepaper, “Use Case-Informed Framework for Utility Cloud Migration,” details the guiding strategy, research, and deployment of cloud solutions within electric and interconnected grid systems. Before implementing the controls suggested in this document, it is recommended that stakeholders complete Cirrus's cloud integration assessment and pair the results with their unique cybersecurity controls to form a comprehensive cloud-based utility cybersecurity plan. The Cirrus outcome will consider a series of future architectures for the grid before and after the energy transition and evaluate the arguments for and against cloud applications for each electric and interconnected grid layer. This document is a companion to the original whitepaper, "Use Case-Informed Framework for Utility Cloud Migration" to further identify and recommend security controls based on Cirrus’s cloud integration assessment output. The following whitepaper outlines the cybersecurity controls that secure cloud-service models pertinent to the electric sector using the predefined categories identify, protect, detect, and respond and recover. The objective is to outline prescriptive security controls based on the type of architecture and data stored in the cloud. The focus includes dissecting the shared responsibility model and elucidating what on-premises Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) entail. A pivotal consideration in this context is allocating responsibility for foundational cybersecurity aspects—having used Cirrus for the cloud integration assessment. The ensuing controls detailed herein also represent a checklist of controls necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and strategic foresight in a safe and responsible manner.

42 ENGINEERING↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗