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DOE OSTI2020

Impact of Mixed Traffic on the Energy Savings of a Truck Platoon

A two-truck platoon based on a prototype cooperative adaptive cruise control (CACC) system was tested on a closed test track in a variety of realistic traffic and transient operating scenarios - conditions that truck platoons are likely to face on real highways. The fuel consumption for both trucks in the platoon was measured using the SAE J1321 gravimetric procedure as well as calibrated J1939 instantaneous fuel rate, serving as proxies to evaluate the impact of aerodynamic drag reduction under constant-speed conditions. These measurements demonstrate the effects of: the presence of a multiple-passenger-vehicle pattern ahead of and adjacent to the platoon, cut-in and cut-out manoeuvres by other vehicles, transient traffic, the use of mismatched platooned vehicles (van trailer mixed with flatbed trailer), and the platoon following another truck with adaptive cruise control (ACC). These scenarios are intended to address the possibility of “background aerodynamic platooning” impacting realized savings on public roads. Using calibrated J1939 fuel rate analysis, fuel savings for curved track sections versus straight track sections were also evaluated for these scenarios, highlighting differences in the implementation of the CACC control strategies compared to a stock ACC implementation. The use of different trailer types and the presence of passenger-vehicle traffic patterns showed a measurable impact on platoon performance in some conditions, but the basic fuel savings trends were retained.

adaptive cruise control↗

DOE OSTI2020

Impact of Mixed Traffic on the Energy Savings of a Truck Platoon: Preprint

adaptive cruise control↗

NASA NTRS2008

GOES-R Geostationary Lightning Mapper Performance Specifications and Algorithms

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series will carry a GLM that will provide continuous day and night observations of lightning. The mission objectives for the GLM are to: (1) Provide continuous, full-disk lightning measurements for storm warning and nowcasting, (2) Provide early warning of tornadic activity, and (2) Accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997- present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. The science data will consist of lightning "events", "groups", and "flashes". The algorithm is being designed to be an efficient user of the computational resources. This may include parallelization of the code and the concept of sub-dividing the GLM FOV into regions to be processed in parallel. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama, Oklahoma, Central Florida, and the Washington DC Metropolitan area) are being used to develop the prelaunch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution.

Mach, Douglas M.↗

NASA NTRS1997

From Top to Bottom - the Multiwavelength Campaign of V824 Ara (HD 155555)

A great deal of progress has been made in recent years in decomposing the 2-D structure in the atmospheres of late-type stars. Doppler images of many photospheres single stars, T Tauri stars, Algols, RS CV(sub n) binaries to name a few - are regularly published (Strassmeier 1996; Richards and Albright 1996; Rice and Strassmeier 1996; Kuerster et al. 1994). Ultraviolet spectral images of chromospheres appear in the literature (e.g., Walter et al. 1987; Neff et al. 1989) but are less common owing to the difficult nature of obtaining complete phase coverage. Zeeman doppler images of magnetic fields are now feasible (e.g., Donati et al. 1992). Performing Doppler imaging of the same targets over many seasons has also been accomplished (e.g, Vogt et al. 1997). Even when a true image reconstruction is not possible due to poor spectral resolution, we can still infer a great deal about spatial structure if enough phases are observed. However, it is increasingly apparent that to make sense of recent results, many different spectral features spanning a range of formation temperature and density must be observed simultaneously for a coherent picture to emerge. Here we report on one such campaign. In 1996, we observed the southern hemisphere RS CV(sub n) binary V824 Ara (P=1(sup d).68, G5IV+K0V-IV-IV) over one complete stellar rotation with the Hubble Space Telescope and EUVE. In conjunction, radio and optical photometry and spectroscopy were obtained from the ground. Unique to this campaign is the complete phase coverage of a number of activity proxy indicators that cover source temperatures ranging from the photosphere to the corona.

Dempsey, Robert↗

DOE OSTI2024

Surface elevation, snow depth, vegetation height, and color imagery from multiple UAV surveys from 2018 to 2023 across a watershed near Teller road mile marker 27, Seward Peninsula, Alaska

Multiple photogrammetric surveys using an unoccupied aerial system (UAS) were performed across a watershed along Teller road on the Seward Peninsula (Alaska) to investigate terrain, vegetation and snowpack properties. This work was led by the environmental geophysics team from Lawrence Berkeley National Laboratory as part of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The multiple photogrammetric surveys used mostly a DJI Matrice 210 UAS with a DJI X5S camera, Ground Control Points (GCPs) surveyed with a Real Time Kinematic (RTK)-GPS, and a structure from motion (SfM) technique for photogrammetric reconstruction. Digital Surface elevation Models (DSMs) were inferred at various times of year, including near peak in plant and leaf density (July 19 2017), close to peak in snow depth (April 1 2019 and April 11 2022), and at low plant and leaf density of tall after the first bare-ground date (June 9 2019). Snow and canopy height were obtained by subtracting DSMs from a Digital Terrain Model (DTM) proxy inferred from the June 9 2019 survey. The derived products include ortho-mosaiced RGB map, DSMs, a DTM proxy, snow depth (April 2019 and 2022) and canopy height (July 2017). Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.csv), image (*.jpg), and GeoTiff (*.tif) formats. This metadata document contains a description of the survey and processing steps and the inferred products.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

From Waste-Heat Recovery to Refrigeration: Compositional Tuning of Magnetocaloric Mn 1+ x Sb

Magnetic refrigeration, as well as waste-heat recovery, can be accomplished through the magnetocaloric effect, where temperature changes the magnetic state of a material or vice versa. Promising magnetocaloric materials display large changes in magnetic entropy (ΔS M ) upon application of a moderate magnetic field and are often associated with magnetic materials possessing some degree of magnetostructural coupling. In such compounds, the magnetic transition is coupled to some structural transition at the ordering temperature, and indicators for these are readily calculated by the magnetic deformation proxy Σ M . MnSb, with a Curie temperature T C = 577 K, has a calculated magnetic deformation of Σ M = 5.9% and is a promising candidate material for waste-heat recovery. The temperature dependence of structural, magnetic, and magnetocaloric properties of Mn 1+ x Sb, where $x$ is a tunable amount of interstitial Mn, is studied here. specifically, excess Mn is incorporated as an interstitial whose magnetic moment is antialigned with the stoichiometric Mn, and the excess Mn has the effect of lowering T C , such that the Curie temperature can be tuned from 577 K to nearly room temperature at 318 K for $x$ = 0.2. For $x$ = 0.0, 0.1, and 0.2, values of ΔS M under a maximum magnetic field $H$ = 5 T are found to be 3.65, 3.00, and 2.83 J K -1 kg -1 , respectively. While the maximum ΔS M decreases with $x$, the high refrigerant capacity a more holistic measure of performance-is retained in this highly tunable system.

36 MATERIALS SCIENCE↗

DOE OSTI2025

From Edge to HPC: Investigating Cross-Facility Data Streaming Architectures

In this paper, we investigate three cross-facility data streaming architectures, Direct Streaming (DTS), Proxied Streaming (PRS), and Managed Service Streaming (MSS). We examine their architectural variations in data flow paths and deployment feasibility, and detail their implementation using the Data Streaming to HPC (DS2HPC) architectural framework and the SciStream memory-to-memory streaming toolkit on the production-grade Advanced Computing Ecosystem (ACE) infrastructure at Oak Ridge Leadership Computing Facility (OLCF). We present a workflow-specific evaluation of these architectures using three synthetic workloads derived from the streaming characteristics of scientific workflows. Through simulated experiments, we measure streaming throughput, round-trip time, and overhead under work sharing, work sharing with feedback, and broadcast and gather messaging patterns commonly found in AI-HPC communication motifs. Our study shows that DTS offers a minimal-hop path, resulting in higher throughput and lower latency, whereas MSS provides greater deployment feasibility and scalability across multiple users but incurs significant overhead. PRS lies in between, offering a scalable architecture whose performance matches DTS in most cases.

George, Anjus [ORNL] (ORCID:0000000179737061)↗

DOE OSTI2025

DROP DURABILITY ASSESSMENT OF ELECTRONIC ASSEMBLIES UNDER OFF-AXIS LOADING WITH SKEWED FIXTURES

This thesis studies drop durability of electronic assemblies when the acceleration vector is oriented at 45° to the out-of-plane direction of the circuit card. The off-axis drop tests are accomplished with a skewed fixture and are conducted as a proxy for multiaxial drop testing. Advanced shock testing and vibration test methods have been developed over the last few decades to better represent real-world field environments during ground-based laboratory testing. However, many of these test methods require expensive and specialized equipment not available in most laboratories. An alternative approach for approximating simultaneous loading along multiple axes on conventional equipment utilizes skewed fixtures which have seen use in off-axis random vibration and drop impact testing. These methods generally rely on the conversion of a uniaxial input load from the test equipment (using a uniaxial drop tower or shaker) into a multiaxial load when resolved in the reference frame of the test article (mounted on a skewed fixture). Skewed fixture design is presented and recommendations for conducting skewed angle drop testing are introduced based on local measurements along the skewed face of the fixture to accurately monitor the impact event. Characterization tests were performed with a skewed fixture, at simultaneous acceleration loads from 500 to 3,000 g in two (in-plane and out-of-plane) directions, while meeting standard time domain tolerances. Upon experimental characterization, drop shock durability tests were conducted on a printed circuit assembly (PCA). Mean drops-to-failure were measured and quantified with Weibull statistics. Dominant solder joint failure modes were identified via failure analysis. Prior work on inclined angle impact testing is limited, and the majority of solder joint interconnect level fatigue studies are conducted considering perpendicular loading normal the circuit card. Low-cycle fatigue curves are generated based on plastic strain and plastic work density within the solder joint. A multiscale nonlinear finite element model is used to relate board-level flexure to solder joint interconnect level plastic strain. A high strain rate solder constitutive model allows for accurate modeling of solder plasticity resulting from high-impact drop shock. Fatigue parameters are computed from the Coffin-Manson relation and Palmgren-Miner damage accumulation. This work serves to apply established low-cycle fatigue methods for conventional drop shock loading (impact normal to circuit card) to non-perpendicular loading with a skewed fixture.

Hower, Jonathan [Kansas City National Security Cam↗

NASA NTRS2017

Online Multi-Modal Learning and Adaptive Information Trajectory Planning for Autonomous Exploration

In robotic information gathering missions, scientists are typically interested in understanding variables which require proxy measurements from specialized sensor suites to estimate. However, energy and time constraints limit how often these sensors can be used in a mission. Robots are also equipped with cheaper to use navigation sensors such as cameras. In this paper, we explore a challenging planning problem in which a robot is required to learn about a scientific variable of interest in an initially unknown environment by planning informative paths and deciding when and where to use its sensors. To tackle this we present two innovations: a Bayesian generative model framework to automatically learn correlations between expensive science sensors and cheaper to use navigation sensors online, and a sampling based approach to plan for multiple sensors while handling long horizons and budget constraints. Our approach does not grow in complexity with data and is anytime making it highly applicable to field robotics. We tested our approach extensively in simulation and validated it with real data collected during the 2014 Mojave Volatiles Prospector Mission. Our planning algorithm performs statistically significantly better than myopic approaches and at least as well as a coverage-based algorithm in an initially unknown environment while having added advantages of being able to exploit prior knowledge and handle other intricacies of the real world without further algorithmic modifications.

learning↗

DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗

NASA NTRS1995

NOAA-11 SBUV/2 measurements of solar UV variations

The SBUV/2 instrument onboard the NOAA-11 satellite made daily solar spectral irradiance measurements in the wavelength region 160405 nm at 1.1 nm resolution between January 1989 and October 1994. These observations continued the uninterrupted series of solar measurements begun by the Nimbus-7 SBUV in 1978 and continued by NOAA-9 SBUV/2. While the measurements made by the SBUV-series instruments furnish an excellent data base for studies of solar UV variability, these instruments do not have an internal mew to evaluate and correct for long-term instrument sensitivity degradation, needed to evaluate solar cycle timescale irradiance change. During yearly Shuttle flights the Shuttle SBUV (SSBUV) also performs solar spectral irradiance measurements in the wavelength region 200 to 400 nm with an instrument that is calibrated preflight, inflight, and postflight. Comparisons between the simultaneous NOAA-11 SBUV/2 and SSBUV solar measurements are used to identify and correct long term sensitivity changes in the satellite instrument. The NOAA-11 data will then be used to evaluate long-term solar change. We present a progress report on the above process. At this preliminary stage uncertainties in the calibration transfer between SSBUV and NOAA-11 SBUV/2 are too large to accurately evaluate long-term solar change near the A1 edge, but solar rotational activity variations can be evaluated. We find that rotational activity declined from roughly 6% peak-to-peak (p-p) near the maximum of solar cycle 22 in 1989-1991 to approximately 3% p-p in mid 1992 and 2% p-p by mid 1994. Emphasizing rotational variations, comparisons between the 200 nm data and the NOAA-11 Mg II proxy index are presented.

Cebula, R. P.↗

DOE OSTI2022

A computer vision algorithm for interpreting lacustrine carbonate textures at Searles Valley, USA

Investigations of the paleohydrologies of pluvial lake systems have often employed lake carbonate deposits called “tufa” that grow subaqueously and can be preserved long after the drying of the lake. For this reason, tufa have been used as a proxy for minimum lake level. However, they exhibit a variety of textures that hold the potential to reveal richer paleoclimatological information. With the goal of determining if tufa texture can be used as a proxy for lake environment, this study investigates the textures of tufa at Mono Lake, California in comparison to the fossil tufa in Searles Valley, California. While observations in the last century suggest that the tufa in the Mono basin grew in waters similar to the modern, the tufa at Searles formed during the last glacial period, when the Great Basin contained a system of pluvial lakes on the scale of the modern Great Lakes. The tufa at both basins have been observed to have a range of classifiable textures, and new methods of inspecting visual data could be informative about what factors control these textures. To this end, a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to project images of the tufa at Searles and Mono into a coordinate space, allowing for simple, quantitative comparisons of the visual similarity of textures. In this work, the textures of tufa at Searles are compared to each other, as well as to the tufa at Mono. This study performs a robust assessment of the feasibility of Mono Lake as a modern analogue for Searles Valley. It finds that there is a justifiable basis for the comparison of certain fossil facies at Searles to the tufa at Mono, significant progress towards the goal of using texture as a metric for the environment in which tufa formed.

58 GEOSCIENCES↗

NASA NTRSDate not supplied

Considerations on Electrolytic Conductivity Measurement for Monitoring of Ionic Silver Biocide Dosing

NASA interest in ionic silver (Ag+) as a biocide for spacecraft potable water systems motivates the development of Ag+ concentration sensors to ensure nominal dosing. The electrolytic conductivity change of highly-purified potable water is linearly related to the concentration of chemically-dosed Ag+ and could serve as a useful proxy measurement, while the conductivity change during electrolytic dosing may be less so, depending on the influent water chemistry and electrolytic efficiency. Understanding and mitigating the potentially deleterious effects of Ag+ interaction with conductivity measurement systems requires investigation. Issues associated with traditional conductivity cells, which rely on wetted(typically metal or graphite) electrodes, and capacitively-coupled contactless conductivity detection (C4D) for this application are considered. Traditional conductivity cells may potentially be subject to significant excitation-induced or auto-galvanic reduction of Ag+. These may result in Ag+ depletion or electrode fouling and associated measurement error. Proper selection of electrode material, excitation parameters, and cell geometry may limit such effects. C4D uses electrodes placed outside an inert, insulating material, with dielectric polarization enabling the production of an electric field and resultant current across the analyte solution. This approach could potentially mitigate problems with Ag+ depletion and fouling by eliminating the possibility of auto-galvanic deposition and reducing the Faradaic current density. However, it is necessary to confirm C4D performance at very low conductivity levels and to determine if long-term operation produces conductive deposits, which could result in measurement error. A commercial C4D system with 1/16” (1.59 mm)outer diameter flow tubing and claimed performance in the conductivity range appropriate for this application was identified, and its sensitivity in the upper parts of this range was confirmed in a preliminary experiment. A concept for a C4D detector that could allow for full rate flow-through using planar electrodes and thin-film dielectric layers is discussed.

silver↗

DOE OSTI2025

Measurement of the Lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector

The Lund jet plane (LJP) is measured for the first time in $t\bar{t}$ events, using 140 $\textrm{fb}^{-1}$ of $\sqrt{s} = 13$ TeV pp collision data collected with the ATLAS detector at the LHC. The LJP is a two-dimensional observable of the sub-structure of hadronic jets that acts as a proxy for the kinematics of parton showers and hadron formation. The observable is constructed from charged particles and is measured for R = 1.0 anti-$k_t$ jets with transverse momentum above 350 GeV containing the full decay products of either a top quark or a daughter W boson. The other top quark in the event is identified from its decay into a b-quark, an electron or a muon and a neutrino. The measurement is corrected for detector effects and compared with a range of Monte Carlo predictions sensitive to different aspects of the hadronic decays of the heavy particles. In the W-boson-initiated jets, all the predictions are incompatible with the measurement. In the top quark initiated jets, disagreement with all predictions is observed in smaller subregions of the plane, and with a subset of the predictions across the fiducial plane. The measurement could be used to improve the tuning of Monte Carlo generators, for better modelling of hadronic decays of heavy quarks and bosons, or to improve the performance of jet taggers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Model Performance in Simulating the Mid-Holocene Green Sahara

The Green Sahara Period, spanning about 11,500 to 5,000 years ago, offers an opportunity to test the ability of climate models to simulate large-scale changes in northern African climate through the strengthening of the West African Monsoon. In this study, we evaluate the performance of four models in simulating the mid-Holocene (6,000 BP), namely – EC-Earth, iCESM, CCSM4-Toronto, and the GISS ModelE2.1-G. Two scenarios are considered for each model – a standard PMIP scenario simulated with the mid-Holocene orbital parameters and greenhouse gas concentrations with vegetation prescribed to pre-industrial conditions, as well as a Green-Sahara scenario which additionally considers factors such as enhanced vegetation, reduced dust, presence of lakes, and land and soil feedbacks. All mid-Holocene scenarios capture an increase in monsoonal precipitation in northern Africa. However, a comparison of the two mid-Holocene scenarios reveals significantly higher precipitation in northern Africa for all the Green-Sahara scenarios relative to the PMIP scenarios – an observation consistent across all models. Accompanied by a strengthened Saharan Heat Low, these changes in the West African Monsoon are also linked to polar amplification, a stronger Indian Summer Monsoon and alterations to the Walker circulation. Model results are in agreement with pollen-based SAT records, multi-proxy SST records and African lake level records. This comparison indicates that a realistic simulation of the mid-Holocene Green Sahara requires consideration of multiple factors in addition to orbital and greenhouse gas forcings.

Green Sahara Period↗

DOE OSTI2025

Global stellarator coil optimization with quadratic constraints and objectives

Most present stellarator designs are produced by costly two-stage optimization: the first for an optimized equilibrium, and the second for a coil design reproducing its magnetic configuration. Few proxies for coil complexity and forces exist at the equilibrium stage. Rapid initial state finding for both stages is a topic of active research. Most present convex coil optimization codes use the least square winding surface method by Merkel (NESCOIL), with recent improvements in conditioning, regularization, sparsity, and physics objectives. While elegant, the method is limited to modeling the norms of linear functions in coil current. We present QUADCOIL, a global coil optimization method that targets combinations of linear and quadratic functions of the current. It can directly constrain and/or minimize a wide range of physics objectives unavailable in NESCOIL and REGCOIL, including the Lorentz force, magnetic energy, curvature, field-current alignment, and the maximum density of a dipole array. QUADCOIL requires no initial guess and runs nearly $10$ 2 x faster than filament optimization. Integrating it in the equilibrium optimization stage can potentially exclude equilibria with difficult-to-design coils, without significantly increasing the computation time per iteration. QUADCOIL finds the exact, global minimum in a large parameter space when possible, and otherwise finds a well-performing approximate global minimum. It supports most regularization techniques developed for NESCOIL and REGCOIL. We demonstrate QUADCOIL’s effectiveness in coil topology control, minimizing non-convex penalties, and predicting filament coil complexity with three numerical examples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Simplified predictive models for CO 2 sequestration performance assessment

CO2 sequestration in deep saline formations is increasingly being considered as a viable strategy for the mitigation of greenhouse gas emissions from anthropogenic sources. In this context, detailed numerical simulation based models are routinely used to understand key processes and parameters affecting pressure propagation and buoyant plume migration following CO2 injection into the subsurface. As these models are data and computation intensive, the development of computationally-efficient alternatives to conventional numerical simulators has become an active area of research. Such simplified models can be valuable assets during preliminary CO2 injection project screening, serve as a key element of probabilistic system assessment modeling tools, and assist regulators in quickly evaluating geological storage projects. We present three strategies for the development and validation of simplified modeling approaches for CO2 sequestration in deep saline formations: (1) simplified physics-based modeling, (2) statisticallearning based modeling, and (3) reduced-order method based modeling. In the first category, a set of full-physics compositional simulations is used to develop correlations for dimensionless injectivity as a function of the slope of the CO2 fractional-flow curve, variance of layer permeability values, and the nature of vertical permeability arrangement. The same variables, along with a modified gravity number, can be used to develop a correlation for the total storage efficiency within the CO2 plume footprint. Furthermore, the dimensionless average pressure buildup after the onset of boundary effects can be correlated to dimensionless time, CO2 plume footprint, and storativity contrast between the reservoir and caprock. In the second category, statistical “proxy models” are developed using the simulation domain described previously with two approaches: (a) classical Box-Behnken experimental design with a quadratic response surface, and (b) maximin Latin Hypercube sampling (LHS) based design with a multidimensional kriging metamodel fit. For roughly the same number of simulations, the LHS-based metamodel yields a more robust predictive model, as verified by a k-fold cross-validation approach (with data split into training and test sets) as well by validation with an independent dataset. In the third category, a reduced-order modeling procedure is utilized that combines proper orthogonal decomposition (POD) for reducing problem dimensionality with trajectory-piecewise linearization (TPWL) in order to represent system response at new control settings from a limited number of training runs. Significant savings in computational time are observed with reasonable accuracy from the PODTPWL reduced-order model for both vertical and horizontal well problems – which could be important in the context of history matching, uncertainty quantification and optimization problems. The simplified physics and statistical learning based models are also validated using an uncertainty analysis framework. Reference cumulative distribution functions of key model outcomes (i.e., plume radius and reservoir pressure buildup) generated using a 97-run full-physics simulation are successfully validated against the CDF from 10,000 sample probabilistic simulations using the simplified models. The main contribution of this research project is the development and validation of a portfolio of simplified modeling approaches that will enable rapid feasibility and risk assessment for CO2 sequestration in deep saline formations.

54 ENVIRONMENTAL SCIENCES↗