Search NASA⌕ Search

SEARCH · Search NASA

Results for “quantitative evaluation”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantitative comparison of impurity transport in turbulence reduced and enhanced scenarios at Wendelstein 7-X

We assess the turbulent particle transport being responsible for the limitation of the confinement and, thus, the overall performance of the neoclassically optimized stellarator Wendelstein 7-X. The radial particle transport is experimentally inferred from the evaluation of impurity injection into turbulence reduced and enhanced plasma scenarios revealing a completely different confinement behavior. The impact of the density gradient on the turbulent ion transport is theoretically estimated using large-scale non-linear gyro-kinetic simulations enabling, for the first time in Wendelstein 7-X, a quantitative comparison to the experimentally assessed impurity transport properties. We demonstrate that impurity transport in most of the Wendelstein 7-X discharges, up to now impossible to cover only with neoclassical estimations, is dominated by turbulence and can be modelled via gyro-kinetic simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Liquid–Liquid Microfluidic Flows for Ultrafast 5-Hydroxymethyl Furfural Extraction

We study the effects of liquid–liquid flow patterns on the extraction of 5-hydroxymethyl furfural from the aqueous phase into an organic phase (methyl isobutyl ketone, ethyl acetate, and 2-pentanol) at ultrashort residence times of 0.2–20 s in a coiled capillary microchannel. Slug, droplet, slug-droplet, dispersed, parallel, annular, and irregular two-phase flow patterns are generated. We evaluate the extraction efficiency and the volumetric mass transfer coefficient via experiments and computational fluid dynamics (CFD). The CFD predictions are in quantitative agreement with the experimental data. The convective and diffusive contributions to the mass transfer of different flow patterns are analyzed. For all solvent pairs, the extraction efficiency varies irregularly with increasing flow rate due to changes in the mass transfer mechanism caused by a transition between flow regimes. Overall, we demonstrate >90% extraction efficiencies with high volumetric mass transfer coefficients ranging from 0.006 to 2.17 s–1. Furthermore, a merit index is defined to assess the trade-off between the fast mass transfer and the pressure drop in biphasic microchannels. In general, the mass transfer rate increases significantly at high flow rates at the expense of a high-pressure drop. Furthermore, the ethyl acetate/water irregular flow pattern is optimal due to a large throughput, extraction efficiency, and mass transfer coefficient, with a minimal pressure drop.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A study of real-world micrograph data quality and machine learning model robustness

Abstract Machine-learning (ML) techniques hold the potential of enabling efficient quantitative micrograph analysis, but the robustness of ML models with respect to real-world micrograph quality variations has not been carefully evaluated. We collected thousands of scanning electron microscopy (SEM) micrographs for molecular solid materials, in which image pixel intensities vary due to both the microstructure content and microscope instrument conditions. We then built ML models to predict the ultimate compressive strength (UCS) of consolidated molecular solids, by encoding micrographs with different image feature descriptors and training a random forest regressor, and by training an end-to-end deep-learning (DL) model. Results show that instrument-induced pixel intensity signals can affect ML model predictions in a consistently negative way. As a remedy, we explored intensity normalization techniques. It is seen that intensity normalization helps to improve micrograph data quality and ML model robustness, but microscope-induced intensity variations can be difficult to eliminate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-temperature high-pressure microfluidic system for rapid screening of supercritical CO 2 foaming agents

CO 2 foam helps to increase the viscosity of CO 2 flood fluid and thus improve the process efficiency of the anthropogenic greenhouse gas’s subsurface utilization and sequestration. Successful CO 2 foam formation mandates the development of high-performance chemicals at close to reservoir conditions, which in turn requires extensive laboratory tests and evaluations. This work demonstrates the utilization of a microfluidic reservoir analogue for rapid evaluation and screening of commercial surfactants (i.e., Cocamidopropyl Hydroxysultaine, Lauramidopropyl Betaine, Tallow Amine Ethoxylate, N,N,N' Trimethyl-N'-Tallow-1,3-diaminopropane, and Sodium Alpha Olefin Sulfonate) based on their performance to produce supercritical CO 2 foam at high salinity, temperature, and pressure conditions. The microfluidic analogue was designed to represent the pore sizes of the geologic reservoir rock and to operate at 100 °C and 13.8 MPa. Values of the pressure drop across the microfluidic analogue during flow of the CO 2 foam through its pore network was used to evaluate the strength of the generated foam and utilized only milliliters of liquid. The transparent microfluidic pore network allows in-situ quantitative visualization of CO 2 foam to calculate its half-life under static conditions while observing if there is any damage to the pore network due to precipitation and blockage. The microfluidic mobility reduction results agree with those of foam loop rheometer measurements, however, the microfluidic approach provided more accurate foam stability data to differentiate the foaming agent as compared with conventional balk testing. The results obtained here supports the utility of microfluidic systems for rapid screening of chemicals for carbon sequestration or enhanced oil recovery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Path to Global Discovery: U.S. Leadership and Partnership in Particle Physics

In February 2022, HEPAP, the High Energy Physics Advisory Panel to DOE (Department of Energy) and NSF (National Science Foundation), was charged with forming a subpanel to conduct an international benchmarking study to evaluate U.S. leadership in particle physics in a global context (Appendix D). HEPAP formed an International Benchmarking Subpanel and gathered qualitative and quantitative data from the international particle physics community to 1) determine how the U.S. particle physics program can maintain critical international cooperation in an increasingly competitive environment for both talent and resources, 2) identify key areas where the U.S. has or could aspire to leadership roles, and 3) determine how programs and facilities can be structured to attract and retain talented people. This report also serves as input to P5 (Particle Physics Project Prioritization Panel), a subpanel of HEPAP that defines the strategic scientific direction for the U.S. particle physics program.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Gamma-Ray Imaging to Monitor Accumulating Holdup

Over time, nuclear materials can accumulate in process equipment and keeping track of such material is of interest to many stakeholder communities including safeguards, process monitoring, criticality safety, and radiological safety. Passive gamma-ray imaging provides a visual and quantitative tool that can be applied to this problem, allowing one to determine the amount of material present from the images. In this work we emulate the accumulation of highly enriched uranium (HEU) in a large rectangular stainless-steel duct by stacking 5, 27 × 50 cm2 cards, each of which has approximately 11 g of 235U in oxide form distributed across its surface, behind each other. A series of images were collected using a coded-aperture gamma-ray imager, starting with a single card, adding a second card, etc. Long integrations were collected to allow evaluating the performance of the approach as a function of integration time. The images are analyzed using first principles to determine the amount of material in each image. The results are compared to the known amount of material in each configuration, and as a function of time. The analysis includes a first-principles error propagation and to further evaluate the uncertainty, a boot-strap sampling approach is also applied to the data. The work offers a proof of concept for unattended monitoring of uranium holdup with a quantitative gamma-ray imaging technique.

Ziock, Klaus-Peter↗
DOE OSTI2021

Techno-economic assessment of emerging CO 2 electrolysis technologies

Recycling of waste CO 2 to bulk chemicals has a tremendous potential for the decarbonization of the chemical industry. Quantitative analysis of the prospects of this technology is hindered by the lack of flexible techno-economic assessment (TEA) models that enable evaluation of the processing costs under different deployment scenarios. In this protocol, we explain how to convert literature data into metrics useful for evaluation of the emerging electrolysis technologies, derive TEA models, and illustrate their use with a CO 2 -to-ethylene example. For complete details on the use and execution of this protocol, please refer to Barecka et al. (2021a).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Protective effects of intravitreal administration of mesenchymal stem cell-derived exosomes in an experimental model of optic nerve injury

Traumatic optic neuropathy results in the loss of retinal ganglion cells (RGCs), leading to unavoidable visual impairment. However, there is no effective therapy by far. Accumulated studies support the perception that mesenchymal stem cells (MSCs) secrete exosomes that serve as a protective paracrine factor. The study aimed to explore and evaluate the potential therapeutic effects of intravitreal transplantation of MSC-derived exosomes (MSC-exos) in an experimental model of optic nerve crush (ONC). Exosomes were isolated from rat MSCs and characterized by transmission electron microscope and western blotting. At the onset of ONC, a single intravitreal injection of exosomes or PBS was administered to the rats. At day 30, hematoxylin and eosin staining, immunohistochemistry, and βIII-tubulin staining were performed to evaluate the survival of RGCs. Moreover, TUNEL assay was used to examine the apoptosis of RGCs. Inflammation-relevant factors were identified via quantitative polymerase chain reaction. The expression levels of cell apoptosis-related molecules and key members of the PI3K/AKT signaling pathway were determined via western blot analysis. We found that MSC-exos exhibited typical characteristic morphologies (cup-shaped) and sizes (peak size of 93 nm). Furthermore, they exhibited substantial expression of the exosome markers CD63 and TSG101, but lacked the expression of the cellular marker GM130. Treatment with intravitreal MSC-exos notably promoted the survival of RGCs in ONC rats. The level of pro-inflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-8, and MCP-1, were reduced, whereas those of the anti-inflammatory factor IL-10 were increased. Moreover, the apoptosis induced by ONC was decreased by the administration of MSC-exos via upregulation of the Bcl-2/Bax ratio and downregulation of caspase-3 activity. Furthermore, MSC-exos significantly stimulated AKT phosphorylation, whereas LY294002 restored the apoptosis-preventing effects of MSC-exos. The results of our results demonstrated that intravitreal administration of MSC-exos ameliorates ONC-induced injury in a rat model. These findings might aid in the development of effective exosome-based therapeutic strategies for the treatment of optic nerve degeneration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

3D Quantification of Pore Networks and Anthropogenic Carbon Mineralization in Stacked Basalt Reservoirs

Basalt formations are promising candidates for the geologic storage of anthropogenic CO 2 due to their storage capacity, porosity, permeability, and reactive geochemical trapping ability. The Wallula Basalt Carbon Storage Pilot Project demonstrated that supercritical CO 2 injected into >800 m deep Columbia River Basalt Group stacked reservoir flow tops mineralizes to ankerite-siderite-aragonite on month-year timescales, with 60% of the 977 metric tonnes of CO 2 converted within two years. The potential impacts of mineral precipitation and consequent changes on rock porosity, pore structure, pore size, and pore size distributions have likely been underestimated hitherto. Herein, we address these knowledge gaps using X-ray Micro Computed Tomography (XMT) to evaluate the pore network architecture of sidewall cores recovered two years after CO 2 injection. In the present study, we performed a detailed quantitative analysis of the CO 2 -reacted basalt cores by XMT imaging. Reconstructed 3D images were analyzed to study the distribution and volumetric details of porosity and carbonate nodules in the cores along with the various other phases, providing insight into paragenesis and carbonate growth mechanisms, including mineralogic/chemical zonation. Finally, these findings are being used to parametrize multiphase reactive transport models to predict the fate and transport of subsurface CO 2 , enabling scale-up to commercial-scale geologic carbon storage in basalts and other reactive mafic-ultramafic formations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

An analytical approach to evaluating magnetic-field closure and topological changes in FRC devices

Here, we describe mathematical methods based on optimizing a modified non-linear flux function (MFF) to evaluate whether odd-parity perturbations affect the local closure of magnetic field lines in field-reversed configurations. Using the MFF methodology, quantitative formulas are derived that provide the shift of the field minimum and the threshold for field-line opening, a discontinuous change in field topology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

JET EROSION OF PARTICLE BEDS: PROJECTING CRITICAL SUSPENSION VELOCITIES FROM EFFECTIVE CLEARING / CLEANING RADII

Here we explore the relationship between effective cleaning/clearing radii, ECR, and critical suspension velocities, Ucs. Although one set of physics controls both the radial extent of erosion (i.e., the ECR) and the velocity needed to erode from the center of jet impingement to any given location (i.e., Ucs, typically defined from nozzle center to the vessel center), the relationship between these two remains unexplored quantitatively. Here we advance the model of Kuhn, et al., (PNNL-22816, 2013) as described by Pease, et al. (FEDSM2017-69444, 2017) to evaluate the relationship between the effective clearing/cleaning radius and the nozzle velocity on flat surfaces including flat bottomed vessels. Two governing dimensionless groups are identified. We present both a closed form analytical but transcendental solution and a non-iterative approximation modeled after the Serghides approximation of Colebrook’s nonlinear equation for both ECR versus nozzle velocity and Ucs. Comparison of the model to data from flume testing on a flat surface finds reasonable agreement.

jet, erosion, critical suspension velocity, effect↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Direct air capture (DAC) is a method for removing CO2 directly from air. Metal organic frameworks (MOFs) have been studied as DAC sorbent materials because of their structural and chemical diversity. Thermodynamic calculations using classical force fields are often used to evaluate MOFs for their performance in separations such as CO2 capture. Machine-learned force fields (MLFFs) can use machine learning to form quantitative relationships between a material’s chemical structure and the forces and energies predicted by more accurate quantum mechanical calculations, such as dispersion-corrected density functional theory (DFT). These descriptions of forces and energies can be used to improve the accuracy of adsorption calculations. In this work, classical models were used to pre-screen MOFs for CO2 capture. DFT calculations were then used to examine the adsorption mechanism. Next, MLFF models were developed for MOFs to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption. These methods were parametrized based on thousands of DFT calculations of CO2 in flexible MOFs and used to predict MOF structural properties as well as CO2 adsorption properties.

Findley, John↗
DOE OSTI2025

Enhanced Monte Carlo Simulations for Electron Energy Loss Mitigation in Real-Space Nanoimaging of Thick Biological Samples and Microchips

High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantitative Understanding and Implementation of Screen-printed p+ Poly-Si/Oxide Passivated Contact to Enhance the Efficiency of p-PERC Cells

Investigate the passivation quality of p-TOPCon to evaluate the promise and viability of upgraded p-PERC with p-TOPCon back. Model and simulate the p-PERC and p-TOPCon cells to predict the gain in cell efficiency by implementation of the p-TOPCon Develop a simple process sequence and fabricate p-TOPCon-PERC cell with ≥ 21.0% efficiency

p-TOPCon, P-PERC↗
DOE OSTI2026

L$_{μ}$ − L$_{τ}$ gauge bosons in beam dumps and supernovae

We study the phenomenology of a sub-GeV L$_{μ}$ − L$_{τ}$ gauge boson. We find discrepancies with existing literature in sensitivity projections for the upcoming SHiP experiment and in the treatment of supernovae cooling constraints. We present a quantitative analysis of different production modes in beam dumps and compare our results to previous work. In the context of supernovae, we re-evaluate the standard supernova cooling bounds from SN1987A and analyze additional supernova-based probes: diffusive cooling, constraints from the existence of low-energy supernovae, and the absence of a high-energy neutrino signal from SN1987A.

Blinov, Nikita [York U., Canada] (ORCID:0000000228↗