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At least 127 records · Page 7

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DOE OSTI2023

Survey of Machine Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Broad Application in Experimental Facilities

This work evaluates and compares various convolutional neural networks (CNNs) trained in previous NETL studies according to metrics effecting diagnostic feasibility, external applicability, and performance. Each CNN surveyed, including image classification, object detection, and time series classification, is used to develop total RDE diagnostics and evaluated alongside conventional techniques with respect to real-time capabilities. Real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup, which is outlined herein for possible adaptations in external experimental facilities.

Johnson, Kristyn↗

DOE OSTI2023

Survey of Machine Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Broad Application in Experimental Facilities

Johnson, Kristyn↗

DOE OSTI2024

The carbon challenge: Design, synthesis, and chemisorption behavior of solid sorbents in direct air capture of carbon dioxide

Direct air capture (DAC) of CO 2 is a promising solution for reducing the carbon footprint through "negative emission" technology. However, the low CO 2 concentration (~400 ppm) and the dynamic nature of DAC processes present challenges in designing effective sorbent systems. Recent advancements in material design and structural engineering have led to the development of high-performance solid sorbents, offering a more stable, safe, and energy-efficient alternative to traditional liquid CO 2 capture methods. This review highlights progress in solid sorbent-based DAC, focusing on amine-modified materials, hydrogen-bonded frameworks, and ionic liquid-engineered scaffolds. The discussion covers design principles, synthesis methodologies, and their impact on CO 2 chemisorption, comparing the advantages and limitations of each approach. Characterization techniques, especially operando methods and computational tools, are reviewed to understand sorbent behavior during CO 2 integration and release. The reaction pathways and interaction mechanisms of these sorbents with CO 2 are analyzed to guide future design. Additionally, the CO 2 chemisorption behaviors, including capacity, sorption kinetics, recyclability, and durability in the presence of gaseous impurities and under humid conditions will be evaluated and compared. Further, the review offers unique insights into the physical properties, chemical structures, and surface engineering effects of these sorbents, based on comprehensive characterization and evaluation techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Predicting the Phase Stability of Multicomponent High-Entropy Compounds

A generic method to estimate the relative feasibility of formation of high-entropy compounds in a single phase, directly from first principles, is developed. As a first step, the relative formation abilities of 56 multicomponent, AO, oxides were evaluated. These were constructed from five cation combinations chosen from A = {Ca, Co, Cu, Fe, Mg, Mn, Ni, Zn}. Candidates for multicomponent oxides are predicted from descriptors related to the enthalpy and configurational entropy obtained from the mixing enthalpies of two-component oxides. The utility of this approach is evaluated by comparing the predicted combinations with the experimentally realized entropy-stabilized oxide, (MgCoCuNiZn)O. In the second step, Monte Carlo simulations are utilized to investigate the phase composition and local ionic segregation as a function of temperature. This approach allows for the evaluation of potential secondary phases, thereby making realistic predictions of novel multicomponent compounds that can be synthesized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Assembling a Cyber Range to Evaluate Artificial Intelligence / Machine Learning (AI/ML) Security Tools

In this case study, we will describe the design and assembly of a cyber security test range we have built at Oak Ridge National Laboratory in Oak Ridge, TN, USA. The range is designed to provide a flexible environment to evaluate cyber security tools—particularly those involving AI/ML—in a way that provides realistic environments and where we can control the experiments to determine the strengths and weaknesses of the tools. We have designed in the ability to repeat the evaluations, so additional tools can be evaluated and compared at a later time. The system is one that can be scaled up or down for experiment sizes. At the time of the conference we will have completed two full-scale, national, government challenges on this range. These challenges are evaluating the performance and operating costs for AI/ML-based cyber security tools for application into large, government-sized environments. These evaluations will be described, in order to provide motivation and context for various design decisions and adaptations we have made. The first challenge measured end-point security tools against 100K malware samples chosen across a range of types. The second is network detection of attempted penetration and exploitations with varying levels of covertness in a high-volume, business network. The scale of each of these challenges is requiring us to create automation systems to repeat the experiments identically for each tool. Preventing there being easy signs of malicious activity for the AI/ML tools to focus on has been a particularly interesting and challenging aspect of designing and executing these challenge events. After the events, the range continues to be used for other research such as adversarial machine learning where the repeatability, scale, and automation required for the national challenge events become essential elements for research.

Nichols, Jeff↗

DOE OSTI2026

$\overline{\Sigma }^{\pm }$ production in $\text {pp}$ and $\text {p}{-}\text{Pb}$ collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV with ALICE

The transverse momentum spectra and integrated yields of anti-$Σ$ hyperons ($\overline{\Sigma}^{\pm}$) have been measured in and collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV with the ALICE experiment. Measurements are performed via the newly accessed decay channel $\overline{\Sigma}^{\pm}$ → $\bar{\textrm{n}}π^±$. A new method of antineutron reconstruction with the PHOS electromagnetic spectrometer is developed and applied to this analysis. The p T spectra of $\overline{\Sigma}^{\pm}$ are measured in the range 0.5 < p T < 3 GeV/c and compared to predictions of the PYTHIA 8, DPMJET, PHOJET, EPOS LHC and EPOS4 models. The EPOS LHC and EPOS4 models provide the best descriptions of the measured spectra both in pp and p-Pb collisions, while models which do not account for multiparton interactions provide a considerably worse description at high p T . The total yields of $\overline{\Sigma }^{\pm }$ in both pp and p-Pb collisions are compared to predictions of the Thermal-FIST model and dynamical models PYTHIA 8, DPMJET, PHOJET, EPOS LHC and EPOS4. All models reproduce the total yields in both colliding systems within uncertainties. The nuclear modification factors R pPb for both $\overline{\Sigma}^{+}$ and $\overline{\Sigma}^{-}$ are evaluated and compared to those of protons, $Λ$ and $Ξ$ hyperons, and predictions of EPOS LHC and EPOS4 models. No deviations of R pPb for $\overline{\Sigma}^{\pm}$ from the model predictions or measurements for other hadrons are found within uncertainties.

Abualrob, I. J. [University of Houston] (ORCID:000↗

DOE OSTI2021

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Compressible pairwise interaction extended point-particle model for force prediction of shock-particle bed interaction

We propose a pairwise influence framework for the complex unsteady compressible particle-laden flow problem by accounting for the scattered hydrodynamic waves emitting from neighboring particles in a Euler-Lagrange simulation. It has been observed from particle-resolved (PR) simulations of randomly dispersed particle beds under a loading shock that the compressible pseudoturbulence dominates the flow system even after the primary shock has passed, which causes fluctuations observed in the forces experienced by the particle. Moreover, the fact that each particle exists in the vicinity of a random arrangement of other particles modifies the time history of the drag force experienced by each particle during and after the passage of the shock. First, the scattering flow field due to an incoming shock interacting with a single sphere is constructed using an analysis of the flow in the acoustic limit. Then we examine the validity of the compressible Maxey-Riley-Gatignol force model by comparing the force prediction against a PR simulation of two interacting particles for various particle arrangements and incoming shock strength. Subsequently, the neighboring influences are stored as a library of maps that can be used readily in the calculation of the perturbation force. Lastly, the pairwise interaction assumption is evaluated by comparing the force predicted with the model with PR simulations of a randomly packed particle bed of 10% volume fraction for both water and air as the fluid medium for an incoming shock Mach number 1.22. With a considerably lower cost for the implementation of the model compared to PR simulations, it is verified that the model is reasonably accurate in pinpointing particles whose peak force is significantly larger or smaller than the mean drag but also to capture the prolonged fluctuations after the initial shock.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Design and Performance of an InAs Quantum Dot Scintillator with Integrated Photodetector

A new scintillation material composed of InAs quantum dots (QDs) hosted within a GaAs matrix was developed, and its performance with different types of radiation is evaluated. A methodology for designing an integrated photodetector (PD) with a low defect density and that is optically matched to the QD’s emission spectrum is introduced, utilizing an engineered epitaxial InAlGaAs metamorphic buffer layer. The photoluminescence (PL) collection efficiency of the integrated PD is examined using two-dimensional scanning laser excitation. The detector response to 5.5 MeV α-particles and 122 keV photons is presented. Yields of 13 electrons/keV for α-particles and 30–60 electrons/keV for photons were observed. The energy resolution of 12% observed with α-particles was mainly limited by noise- and geometry-related optical losses. The radiation hardness of an InAs QDs hosted within GaAs and a wider band gap AlGaAs ternary alloy was studied under a 1 MeV proton implantation up to a 10 14 cm –2 dose. The integrated PL responses were compared to evaluate PL quenching due to non-radiative defects. The QDs embedded in the AlGaAs demonstrated improved radiation hardness compared to QDs in the GaAs matrix and in the InGaAs quantum wells.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Effects of blended cements and carbonation on chloride-induced corrosion propagation

Highlights: • Corrosion propagation of blended cements was investigated for 1 year. • Carbonated and non‑carbonated mortars were evaluated. • Galvanostatic pulse and ER sensors were used to monitor corrosion. • Mortar properties were correlated with corrosion rates and corrosion damage. • Relationships between mortar properties and corrosion behaviour were established. Supplementary cementitious materials are known to refine the pore structure of concrete and accelerate the carbonation progress. The combination of the two processes can have both beneficial and disadvantageous effects on corrosion, especially when chlorides are also involved. In this study the corrosion properties of multiple blended cements were evaluated in carbonated and non‑carbonated states, with chlorides introduced through cyclic ponding. The examination involved monitoring the propagation phase, determining the microstructural properties of cements, and assessing the final corrosion damage. The results showed that the steel in the blended cements initially had a relatively high corrosion activity, which later decreased compared to the OPC. This stabilisation was presumably due to the beneficial changes to the pore structure. Carbonation had a significant impact on the corrosion, with carbonated mortars revealing shallower damage over a larger surface area. This effect was more pronounced for blended cements that exhibited greater susceptibility to carbonation.

36 MATERIALS SCIENCE↗

DOE OSTI2020

High Temperature Erosion In Particle Based CSP Systems

Particle based concentrated solar power plants are increasingly being considered as an alternative to molten salts. Although these particle based systems provide the inherent safety and enhanced lifetime from reduced corrosion compared to molten salt systems, they bring in complexities associated with surface erosion from falling particle impact and sliding motion along the surfaces. Past research on particle related erosion has been limited to high velocity applications and doesn’t necessarily coincide with the operating conditions in CSP systems. In our work, we evaluate characterize the rate of erosion for particles and containment materials at 800ºC. Three different types of erosion resulting from a) impact of solid particles on receiver, particle storage, and heat exchanger walls; b) abrasion erosion from particle sliding motion along walls; and c) attrition erosion as the particles breakdown from particle-to-particle contact and particle-to-wall interactions are evaluated and compared across a varied spectrum of particle and containment material spectrum. From the results, It is noted that temperature plays a key role in the rate of erosion experienced by any material. With the increase in temperature, materials become more ductile and are more readily sheared away from particle impinging or sliding motion. Furthermore, the particles themselves have higher susceptibility to breakdown which also lowers particle thermal absorptance and thus efficiency as heat transfer fluid.

14 SOLAR ENERGY↗

DOE OSTI2020

High Temperature Erosion In Particle Based CSP Systems

14 SOLAR ENERGY↗

DOE OSTI2022

Bio‐based glycerol plasticizers for flexible poly(vinyl chloride) blends

Abstract A series of glycerol‐based compounds were investigated for their application as plasticizers for flexible poly(vinyl chloride) (PVC) blends. The effect of plasticizer chemical structure on the performance, migration behavior, and blend morphology were evaluated and compared to blends produced using the commercial plasticizer dioctyl terephthalate (DOTP). Blends containing 40 phr (parts per hundred rubber) of glycerol‐based plasticizer showed a considerable reduction (between 54 to 86°C) in glass transition temperature ( T g ) relative to neat PVC ( T g ~ 80°C). Tensile testing of samples prepared with the glycerol analogs demonstrated higher ductility (elongation at break values of up to 97%) than DOTP (elongation at break value of 75%) at identical plasticizer loadings. The surface morphologies showed excellent incorporation of the glycerol plasticizers functionalized with alkyl chains longer than four carbons in length into the PVC matrix, whereas droplet formation was observed in blends with shorter chain glycerol derivatives. Leaching behavior of the plasticized samples were evaluated into different media and showed that plasticizers comprised of branched, or longer alkyl chains produced 2‐ to 4‐fold lower migration rates compared to those with shorter alkyl chains into polar solutions.

Halloran, Matthew W.↗

DOE OSTI2022

Proactive demand-side participation: Centralized versus transactive demand-Supply coordination

The active participation of demand-side flexible resources in the wholesale market price formation and load dispatch process is crucial to encouraging demand-side participation. This calls for a joint supply-demand coordination mechanism to fully take advantage of the flexible resources in distribution systems, including distributed energy resources (DERs) and responsive loads (RLs). Here this paper aims at comparing and evaluating the centralized and transactive distribution-level market coordination mechanisms. We introduce the centralized and transactive demand-supply coordination mechanisms for the distribution-level market and elaborate on the structural difference between the two frameworks. Relevant metrics and test scenarios are proposed for a meaningful comparison. The key observations of the comparative study are generalized from the perspective of different entities in the market: fixed loads, flexible loads, DERs, and conventional generators. It is observed that while the centralized approach leads to socially optimum solutions, the transactive approach by allowing for competitive bidding at the distribution-level, results in clearing higher flexible demand, and thus higher electricity cost at the transmission-level. As a result, DERs and fixed loads receive a higher surplus in the centralized approach, while conventional generators and flexible loads are more profitable in the transactive approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A Comparative Study of Machine Learning Algorithms for Industry-Specific Freight Generation Model

According to Bureau of Transportation Statistics, the U.S. transportation system handled 14,329 million ton-miles of freight per day in 2020. Understanding the generation of these freight shipments is crucial for transportation researchers, planners, and policymakers to design and plan for a more efficient and connected freight transportation system. Traditionally, the freight generation modeling has been based on Ordinary Least Square (OLS) regression, although more advanced Machine Learning (ML) algorithms have been evaluated and proven to have excellent performance in various transportation applications in recent years. Furthermore, one modeling approach applied for one industry might not always be applicable for another as their freight generation logics can be quite different. The objective of this study is to apply and evaluate alternative ML algorithms in the estimation of freight generation for each of 45 industry types. Seven alternative ML algorithms, along with the base OLS regression, were evaluated and compared. In addition, the study considered different combinations of variables in both the original and logarithmic form as well as hyperparameters of those ML algorithms in the model selection for each industry type. The results showed statistically significant improvements in the root mean square error reduction by the alternative ML algorithms over the OLS for over 80% of cases. The study suggests utilizing the alternative ML algorithms can reduce the root mean square error by about 30%, depending on industry types.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Learning curves for drug response prediction in cancer cell lines

Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Environmental life cycle assessment methods applied to amine-ionic liquid hybrid CO 2 absorbents

A hybrid solvent mixture of triethyl(octyl)phosphonium cyanopyrrolide [P2228][2-CNPyr] and aqueous monoethanolamine (MEA) has the potential for absorbing CO 2 from post combustion flue gas. However, previous studies have found that the production of phosphonium based ionic liquids (IL) had significantly higher potential environmental impacts compared to MEA. Literature attributes these higher environmental impacts to the phosphine and phosgene-based intermediates required to produce the phosphonium ion of the ionic liquid. This study proposes a novel synthesis pathway that eliminates the need for phosphine and phosgene intermediates in the production of [P2228][2-CNPyr]. The environmental impacts of producing 1kg of the ionic liquid through this novel synthesis route was evaluated using the TRACI 2.1 methodology within the life cycle assessment (LCA) framework. Additionally, the environmental impacts for the production of 1kg of a hybrid solvent was also evaluated and compared against MEA. The life cycle inventory for the production of the IL and its hybrid solvent were calculated based on the stoichiometry and then scaled up. This study found that the IL and its hybrid solvents had higher environmental impacts among 9 of the 10 environmental impact categories calculated by the TRACI 2.1 methodology, except for the ecotoxicity potential. A sensitivity analysis indicated that these solvents were more sensitive to the assumptions of the material requirements of the phosphonium cation than the overall energy or transportation requirements. Despite this sensitivity, both the solvents demonstrated a lower Ecotoxicity Potential compared to MEA, the rest of the environmental impacts were still found to be higher than that of MEA, thereby underscoring the need to investigate novel synthesis routes for the production of phosphonium cation. The uncertainty analysis performed confirmed the findings that the IL has a higher environmental impact potentials across all categories except ecotoxicity potential. The uncertainty analysis also confirms that the phosphonium cation is a major hotspot in production route of these solvents and a source of uncertainty in the model compared to the anion. Altogether, this study underscores the need for investigating novel green chemistry pathway for the synthesis of phosphonium based ionic liquids, such as [P2228][2-CNPyr], to ensure that the these ILs can be a truly green alternative to MEA by not only offering superior CO₂ capture capacity compared to MEA but also being sustainably produced.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

The June 2012 North American Derecho: A Testbed for Evaluating Regional and Global Climate Modeling Systems at Cloud‐Resolving Scales

Abstract In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%.

2012 North American derecho↗