Generalization of the Weighted Nonlocal Laplacian in Low Dimensional Manifold Model
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Mathematics↗
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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.
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Model Amphiphilic Block Copolymers with Tailored Molecular Weight and Composition in PDMS-Based Films to Limit Soft Biofouling
A set of controlled surface composition films was produced utilizing amphiphilic block copolymers dispersed in a cross-linked poly(dimethylsiloxane) network. These block copolymers contained oligo(ethylene glycol) (PEGMA) and fluoroalkyl (AF6) side chains in selected ratios and molecular weights to control surface chemistry including antifouling and fouling-release performance. Such properties were assessed by carrying out assays using two algae, the green macroalga Ulva linza (favors attachment to polar surfaces) and the unicellular diatom Navicula incerta (favors attachment to nonpolar surfaces). All films performed well against U. linza and exhibited high removal of attached sporelings (young plants) under an applied shear stress, with the lower molecular weight block copolymers being the best performing in the set. The composition ratios from 50:50 to 60:40 of the AF6/PEGMA side groups were shown to be more effective, with several films exhibiting spontaneous removal of the sporelings. The cells of N. incerta were also removed from several coating compositions. All films were characterized by surface techniques including captive bubble contact angle, atomic force microscopy, and near edge X-ray absorption fine structure spectroscopy to correlate surface chemistry and morphology with biological performance.
36 MATERIALS SCIENCE↗
A Dynamic-model Study of the Effect of Added Weights and Other Structural Variations on the Blade Bending Strains of an Experimental Two-blade Jet-driven Helicopter in Hovering and Forward Flight
Explore the source record for details and available documents.
Mccarty, John Locke↗
Aerodynamic parameter estimation via Fourier modulating function techniques
Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.
Pearson, A. E.↗
Effective Thermal Conductivity of High Porosity Open Cell Nickel Foam
The effective thermal conductivity of high-porosity open cell nickel foam samples was measured over a wide range of temperatures and pressures using a standard steady-state technique. The samples, measuring 23.8 mm, 18.7 mm, and 13.6 mm in thickness, were constructed with layers of 1.7 mm thick foam with a porosity of 0.968. Tests were conducted with the specimens subjected to temperature differences of 100 to 1000 K across the thickness and at environmental pressures of 10(exp -4) to 750 mm Hg. All test were conducted in a gaseous nitrogen environment. A one-dimensional finite volume numerical model was developed to model combined radiation/conduction heat transfer in the foam. The radiation heat transfer was modeled using the two-flux approximation. Solid and gas conduction were modeled using standard techniques for high porosity media. A parameter estimation technique was used in conjunction with the measured and predicted thermal conductivities at pressures of 10(exp -4) and 750 mm Hg to determine the extinction coefficient, albedo of scattering, and weighting factors for modeling the conduction thermal conductivity. The measured and predicted conductivities over the intermediate pressure values differed by 13%.
Sullins, Alan D.↗
Simulating a Low-Weight Encoding of the Fermi-Hubbard Model Feedback and Support with QAOA
Recent work [1] has proposed a compact fermion-to-qubit mapping for the Fermi-Hubbard model (FHM), potentially lowering the overhead needed to simulate such a system on quantum hardware. In prior work [2], we numerically investigated how well a quantum annealer could prepare ground states of the FHM encoded using the resource-efficient mapping. In order to compare these results to gate-model systems, we turn to a QAOA-style approach and replace the annealing schedule with both Trotterizations thereof and also more general Hamiltonian-variational ansatze based on the terms of the mapped FH Hamiltonian. We investigate the achievable ground state fidelities of these gate sequences, consider various optimizations of the ansatze structure, and offer resource estimations of the optimized QAOA protocols for near-term hardware.
quantum computing↗
Constraining Subsurface Model Resolution at Newberry Volcano Using a Weighted Spatial Analysis
Presented by NETL at Geothermal Resources Council Annual Meeting, 10/16
Mark-Moser, Mackenzie K.↗
Computational investigation on CO 2 adsorption in titanium carbide-derived carbons with residual titanium
We develop a new approach for modeling titanium carbide derived-carbon (TiC-CDC) systems with residual titanium by the generation of modified atomistic structures based on a silicon carbide derived-carbon (SiC-CDC) model and the application of weighted combinations of these structures. In our approach, the original SiC-CDC structure is modified by (i) removing carbon, (ii) adding carbon and (iii) adding titanium. The new atomic scale carbide-derived carbon (CDC) structures are investigated using classical molecular dynamics simulations, and their pure CO 2 adsorption isotherms are calculated using grand canonical Monte Carlo simulations. The system of TiC-CDC with residual titanium is modeled as weighted combinations of pure carbon CDC structures, CDC structures with titanium and a TiC crystalline structure. Our modeling is able to produce both structural properties and adsorption isotherms in accordance with experimental data. The fraction of different models in the systems successfully reflects the structural differences in various experimental TiC-CDC samples. The modeling also suggests that in partially etched TiC-CDC systems, the titanium that may be accessible to CO 2 gas at the transitional interface may provide significant interaction sites for CO 2 and may lead to more efficient overall gas adsorption.
36 MATERIALS SCIENCE↗
Verifying infectious disease scenario planning for geographically diverse populations
In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model. We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations. Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.
59 BASIC BIOLOGICAL SCIENCES↗
Hyperspectral Mapping of the Invasive Species Pepperweed and the Development of a Habitat Suitability Model
Mapping and predicting the spatial distribution of invasive plant species is central to habitat management, however difficult to implement at landscape and regional scales. Remote sensing techniques can reduce the impact field campaigns have on these ecologically sensitive areas and can provide a regional and multi-temporal view of invasive species spread. Invasive perennial pepperweed (Lepidium latifolium) is now widespread in fragmented estuaries of the South San Francisco Bay, and is shown to degrade native vegetation in estuaries and adjacent habitats, thereby reducing forage and shelter for wildlife. The purpose of this study is to map the present distribution of pepperweed in estuarine areas of the South San Francisco Bay Salt Pond Restoration Project (Alviso, CA), and create a habitat suitability model to predict future spread. Pepperweed reflectance data were collected in-situ with a GER 1500 spectroradiometer along with 88 corresponding pepperweed presence and absence points used for building the statistical models. The spectral angle mapper (SAM) classification algorithm was used to distinguish the reflectance spectrum of pepperweed and map its distribution using an image from EO-1 Hyperion. To map pepperweed, we performed a supervised classification on an ASTER image with a resulting classification accuracy of 71.8%. We generated a weighted overlay analysis model within a geographic information system (GIS) framework to predict areas in the study site most susceptible to pepperweed colonization. Variables for the model included propensity for disturbance, status of pond restoration, proximity to water channels, and terrain curvature. A Generalized Additive Model (GAM) was also used to generate a probability map and investigate the statistical probability that each variable contributed to predict pepperweed spread. Results from the GAM revealed distance to channels, distance to ponds and curvature were statistically significant (p < 0.01) in determining the locations of suitable pepperweed habitats.
Pepperweed↗
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
The environmental impacts of global warming driven by methane (CH 4 ) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH 4 . Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH 4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH 4 as a classification problem and (ii) predict the intensity of CH 4 as a regression problem. The classification model performance for CH 4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH 4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH 4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.
Majumder, Reek↗
Availability Estimation for Facilities in Extreme Geographical Locations
A value added analysis for the Reliability. Availability and Maintainability of McMurdo Ground Station was developed, which will be a useful tool for system managers in sparing, maintenance planning and determining vital performance metrics needed for readiness assessment of the upgrades to the McMurdo System. Output of this study can also be used as inputs and recommendations for the application of Reliability Centered Maintenance (RCM) for the system. ReliaSoft's BlockSim. a commercial Reliability Analysis software package, has been used to model the availability of the system upgrade to the National Aeronautics and Space Administration (NASA) Near Earth Network (NEN) Ground Station at McMurdo Station in the Antarctica. The logistics challenges due to the closure of access to McMurdo Station during the Antarctic winter was modeled using a weighted composite of four Weibull distributions. one of the possible choices for statistical distributions throughout the software program and usually used to account for failure rates of components supplied by different manufacturers. The inaccessibility of the antenna site on a hill outside McMurdo Station throughout one year due to severe weather was modeled with a Weibull distribution for the repair crew availability. The Weibull distribution is based on an analysis of the available weather data for the antenna site for 2007 in combination with the rules for travel restrictions due to severe weather imposed by the administrating agency, the National Science Foundation (NSF). The simulations resulted in an upper bound for the system availability and allowed for identification of components that would improve availability based on a higher on-site spare count than initially planned.
Fischer, Gerd M.↗
Propensity Score-based Comparison of Long-term Outcomes With 3-Dimensional Conformal Radiotherapy vs Intensity-Modulated Radiotherapy for Esophageal Cancer
Purpose: Although 3-dimensional conformal radiotherapy (3D-CRT) is the worldwide standard for the treatment of esophageal cancer, intensity modulated radiotherapy (IMRT) improves dose conformality and reduces the radiation exposure to normal tissues. We hypothesized that the dosimetric advantages of IMRT should translate to substantive benefits in clinical outcomes compared with 3D-CRT. Methods and Materials: An analysis was performed of 676 nonrandomized patients (3D-CRT, n=413; IMRT, n=263) with stage Ib-IVa (American Joint Committee on Cancer 2002) esophageal cancers treated with chemoradiotherapy at a single institution from 1998-2008. An inverse probability of treatment weighting and inclusion of propensity score (treatment probability) as a covariate were used to compare overall survival time, interval to local failure, and interval to distant metastasis, while accounting for the effects of other clinically relevant covariates. The propensity scores were estimated using logistic regression analysis. Results: A fitted multivariate inverse probability weighted-adjusted Cox model showed that the overall survival time was significantly associated with several well-known prognostic factors, along with the treatment modality (IMRT vs 3D-CRT, hazard ratio 0.72, P<.001). Compared with IMRT, 3D-CRT patients had a significantly greater risk of dying (72.6% vs 52.9%, inverse probability of treatment weighting, log-rank test, P<.0001) and of locoregional recurrence (P=.0038). No difference was seen in cancer-specific mortality (Gray's test, P=.86) or distant metastasis (P=.99) between the 2 groups. An increased cumulative incidence of cardiac death was seen in the 3D-CRT group (P=.049), but most deaths were undocumented (5-year estimate, 11.7% in 3D-CRT vs 5.4% in IMRT group, Gray's test, P=.0029). Conclusions: Overall survival, locoregional control, and noncancer-related death were significantly better after IMRT than after 3D-CRT. Although these results need confirmation, IMRT should be considered for the treatment of esophageal cancer.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Automation of Structural Sizing of Aircraft Concepts Under Static Aeroelastic Constraints
This paper presents an automation process for structural sizing of subsonic and supersonic aircraft concepts under static aeroelastic constraints. The automation process starts with an OpenVSP geometry and ends with a PATRAN plot of a NASTRAN solution for static aeroelastic analysis or optimization. ModelCenter is used to integrate all analysis codes with easy-to-use interfaces. Automation tools are developed to streamline the setup process and avoid user errors. Fuel is distributed by solving an optimization problem to match the center of gravity of aircraft at a specified flight condition. Fuel weights are also automatically attached to the structural model as point masses. All other weights used in FLOPS mission analysis (excluding fuselage and wing structural weights) are automatically attached to or smeared on the structural model. For any given OpenVSP geometry and FLOPS analysis data, a static aeroelastic sizing model for NASTRAN analysis can be generated in a couple of hours. The empirical fuselage and wing structural weights from FLOPS are replaced by structural panel weights from the sized finite-element model. Three supersonic and two subsonic aircraft concepts are used to demonstrate the automation process as a physics-based weight estimation tool for aircraft conceptual design.
Li, Wu↗
A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials
In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.
36 MATERIALS SCIENCE↗
Toward particle accelerator machine state embeddings as a modality for large language models
Understanding and diagnosing the state of a particle accelerator requires navigating high-dimensional control system data, often involving hundreds of interdependent parameters. We propose a novel multimodal embedding framework that jointly learns representations of machine states from both numerical control system readouts and natural language descriptions. This enables the translation of complex machine conditions into human-readable summaries while maintaining fidelity to the underlying physical system. The obtained embeddings are subsequently adapted to an open-weights large language model via cross-attention conditioning. We demonstrate a first implementation trained on European XFEL machine state data. This work covers the embedding model architecture, training methodology, and presents initial examples demonstrating the model's capabilities in action. Due to the general concept of machine state, the model can be easily adapted to other facilities and control system environments.
Accelerator Physics↗
Operating Hardware Impact on the Heat Transfer Properties of Windows
Despite significant advancements in fenestration technology in the last two decades, the thermal transmittance of fenestration products is still significantly higher than that of walls. This corresponds to 60% of the total energy loss of a modern building envelope through the windows. Hence, further development and improvements of fenestration products are necessary. Increasingly stringent codes and standards for fenestration stimulate industry to work on improved solutions. Thus, it is crucial that assessment techniques are able to account for innovations accurately. The thermal effects of non-continuous hardware in window frames are currently ignored by international rating procedures. A preliminary investigation conducted by our team showed significant performance degradation in two of the three out-opening casement profiles caused by the presence of operating hardware. Frames with the structure made of vinyl and fiberglass consist of many air cavities that are penetrated by operating hardware made of highly conductive materials. In these frames, in order to have an accurate assessment, it may be required to employ three-dimensional modeling due to the convective nature of heat transfer within the cavities. However, in this study, we demonstrate that the three-dimensional (3D) effects of non-continuous hardware can be approximated accurately with simpler two-dimensional (2D) simulations. We then develop a simplified model based on weighted average capable of replacing the time- and computation-intensive 3D simulations with 2D simulations and validate it against market available frames and their corresponding hardware. Validation results show that our approximation technique results in discrepancies lower than 0.05 W/(m2K), or 3% of the total thermal transmittance. Thus, we conclude that simplified 2D simulation models may be used for predicting hardware impact in window frames with reasonable accuracy. As windows and glazing structures are becoming ever better thermally insulated, it is becoming even more important to be able to model the impact of the operating hardware on the total thermal performance in order to design the best windows possible and not let the operating hardware ruin an otherwise well-proven design, which is hence addressed in this study.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Modeling of crack bridging in a unidirectional metal matrix composite
The effective fatigue crack driving force and crack opening profiles were determined analytically for fatigue tested unidirectional composite specimens exhibiting fiber bridging. The crack closure pressure due to bridging was modeled using two approaches: the fiber pressure model and the shear lag model. For both closure models, the Bueckner weight function method and the finite element method were used to calculate crack opening displacements and the crack driving force. The predicted near crack tip opening profile agreed well with the experimentally measured profiles for single edge notch SCS-6/Ti-15-3 metal matrix composite specimens. The numerically determined effective crack driving force, Delta K(eff), was calculated using both models to correlate the measure crack growth rate in the composite. The calculated Delta K(eff) from both models accounted for the crack bridging by showing a good agreement between the measured fatigue crack growth rates of the bridged composite and that of unreinforced, unbridged titanium matrix alloy specimens.
Ghosn, Louis J.↗