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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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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Two distinct rotations of bithiazole DNA intercalation revealed by direct comparison of crystal structures of Co(III)•bleomycin A 2 and B 2 bound to duplex 5'-TAGTT sites

Bleomycins constitute a family of anticancer natural products that bind DNA through intercalation of a C-terminal tail/bithiazole moiety and hydrogen-bonding interactions between the remainder of the drug and the minor groove. The clinical utility of the bleomycins is believed to result from single- and double-strand DNA cleavage mediated by the HOO-Fe(III) form of the drug. The bleomycins also serve as a model system to understand the nature of complex drug-DNA interactions that may guide future DNA-targeted drug discovery. In this study, the impact of the C-terminal tail on bleomycin-DNA interactions was investigated. Toward this goal, we determined two crystal structures of HOO-Co(III)•BLMA 2 “green” (a stable structural analogue of the active HOO-Fe(III) drug) bound to duplex DNA containing 5'-TAGTT, one in which the entire drug is bound (fully bound) and a second with only the C-terminal tail/bithiazole bound (partially bound). The structures reported here were captured by soaking HOO-Co(III)•BLMA 2 into preformed host–guest crystals including a preferred DNA-binding site. While the overall structure of DNA-bound BLMA 2 was found to be similar to those reported earlier at the same DNA site for BLMB 2 , the intercalated bithiazole of BLMB 2 is “flipped” 180° relative to DNA-bound BLMA 2 . Further, this finding highlights an unidentified role for the C-terminal tail in directing the intercalation of the bithiazole. In addition, these analyses identified specific bond rotations within the C-terminal domain of the drug that may be relevant for its reorganization and ability to carry out a double-strand DNA cleavage event.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comparison of effectiveness and energy use of airborne pathogen mitigation measures to meet clean air targets in a prototypical office building

Organizations such as ASHRAE and the Centers for Disease Control and Prevention (CDC) have proposed guidelines for controlling infectious aerosols in buildings, which can be met through measures such as modified operation of the heating, ventilation, and air-conditioning (HVAC) system or incorporating air-cleaning technologies. However, more research is needed to understand the trade-offs between health, energy, and comfort aspects when designing measures for these guidelines. To address this gap, this paper presents an analysis using new models for air-cleaning technologies, including in-duct and in-room germicidal ultraviolet (GUV) systems and portable air cleaners (PACs). These models are incorporated into an existing prototypical office building model and six measures are designed to meet ASHRAE Standard 241 and CDC clean air targets: MERV 13 HVAC filtration, maximum outdoor air supplied to the building, PACs, and in-duct, upper-room, and whole-room GUV. The measures are simulated for an office building in a cool and humid climate compared against a baseline simulation using MERV 8 filtration. The results show that all measures, except for the maximum outdoor air case, can meet the ASHRAE 241 standard without significant impacts on energy or comfort. The HVAC system measures were not able to meet the CDC target with the default system sizing and lead to significant energy increases, while the in-room measures were able to meet the CDC target with small impacts on energy consumption. Finally, this paper consolidates the simulation findings and provides practical guidance for building operators to meet clean air targets while limiting energy and comfort impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

An experimental comparison of a space-time multigrid method with PFASST for a reaction-diffusion problem

We consider two parallel-in-time approaches applied to a (reaction) diffusion problem, possibly non-linear. In particular, we consider PFASST (Parallel Full Approximation Scheme in Space and Time) and space-time multigrid strategies. For both approaches, we start from an integral formulation of the continuous time dependent problem. Then, a collocation form for PFASST and a discontinuous Galerkin discretization in time for the space-time multi-grid are employed, resulting in the same discrete solution at the time nodes. Strong and weak scaling of both multilevel strategies are compared for varying orders of the temporal discretization. Moreover, we investigate the respective convergence behavior for non-linear problems and highlight quantitative differences in execution times

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Characteristics of graphene oxide-like materials prepared from different deashed-devolatilized coal chars and comparison with graphite-based graphene oxide, with or without the ultrasonication treatment

Deashed-devolatilized coal chars were prepared from anthracite, bituminous, subbituminous, and lignite coals, then oxidized to prepare graphene oxide (GO)-like materials that were extensively characterized and compared with a pristine graphite-based GO sample (CGO). Coal-based GO-like materials and CGO were then subjected to ultrasonication treatment for delamination, which also impacted their physicochemical properties. The surface oxygen contents of CGO and coal-based samples were ~32 % and ~26–35 %, respectively, that were reduced to ~27 % and ~21–24 %, respectively, after ultrasonication. The Fourier transform infrared and X-ray photoelectron spectroscopy spectra of all samples showed different profiles before and after the ultrasonication, suggesting a change in chemical functionalities. The Raman spectra of CGO and coal-based samples exhibited similar D and G bands with D/G intensity ratios (I D /I G ) of 0.93 and 0.88–0.92, respectively. The I D /I G ratio of CGO and most of the coal-based samples increased after the ultrasonication due to creation of more defects. The main X-ray diffraction (XRD) peak for CGO and coal-based samples were observed at ~11° and ~22–24°, respectively. Ultrasonication of the samples resulted in a reduction of crystallite size and number of layers for all CGO and coal-based samples, confirming both splitting and delamination of carbon layers. Additionally, different coal-based samples exhibited almost overlapping XRD profiles after the ultrasonication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrasonic degradation of GenX (HFPO-DA) – Performance comparison to PFOA and PFOS at high frequencies

Sonolytic degradation kinetics of hexafluoropropylene oxide-dimer acid (HFPO-DA or GenX) were studied for the first time at four high ultrasonic frequencies (375, 580, 860 and 1,140kHz) and three power densities (200, 300 and 400W/L), and compared to the degradation of previously studied perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). The frequency of 580kHz displayed the highest degradation of all three PFAS tested. Within each frequency, the degradation performance increased consistently with increasing power density. Degradation rates were highest for GenX over PFOA to PFOS (k GenX = 0.0501 min -1 , k PFOA = 0.0444 min -1 , k PFOS 0.0153 min -1 ), which is in direct agreement with their reported thermal stability (PFOS > PFOA > GenX). No known by-product formation as expected from the literature on truncation mechanism was detected in the samples (i.e. shorter chain carboxylic acid PFAS). Further, fluorine mass balance experiments at 580kHz and 400W/ L confirmed that GenX defluorinated fastest among the three tested PFAS and had lowest loss of fluorine in the mass balance. Degradation experiments with a mixture of all three PFAS displayed lower degradation rates than the individual experiments, where PFOA exhibited the largest reduction in degradation rate (by 31%), followed by GenX (by 19%), and finally by PFOS (by 17%). Overall, our study demonstrates that ultrasound can provide effective destruction of different PFAS (70-90% fluorine from mitigated PFAS was detected as inorganic fluoride) with a similar energy demand to alternative PFAS treatment methods reported in literature.

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

Comparison of liquid absorption-release of superabsorbent polymers in alkali-activated slag and Portland cement systems: An NMR study combined with additional methods

In this study, {sup 1}H low-field NMR was used to compare the liquid absorption-release behaviour of superabsorbent polymers (SAPs) in Portland cement (PC) paste, NaOH-activated slag (SH-AAS) paste, and water glass-activated slag (WG-AAS) paste. The liquid absorption of SAPs in the PC paste was lower than that in the SH-AAS paste and the WG-AAS paste. The liquid release was also faster, which was mainly due to the higher Ca{sup 2+} concentration in the pore fluid of the PC system. In the AAS system, although the Ca{sup 2+} concentration in the pore fluid was low, the Na{sup +} concentration was much higher than that in the PC system due to the activator. The Ca{sup 2+} in the PC system is an important factor affecting the swelling of SAPs, while in the AAS system, the swelling limitation of SAPs is the result of the combined effects of Ca{sup 2+} and Na{sup +}, with Na{sup +} being dominant.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Temperature-dependent changes in gas chromatographic separation metrics for trihexyl(tetradecyl)phosphonium-based ionic liquid stationary phases and comparison to conventional polysiloxane stationary phases

Here, to assess whether the trihexyl(tetradecyl)phosphonium chloride ([P 66614 + ][Cl - ]) ionic liquid (IL), employed as a gas chromatographic stationary phase, exhibits temperature-dependent phase transitions or structural heterogeneity, retention thermodynamics for probe molecules of varied chemical structure were investigated by gas chromatography (GC) over the temperature range of 25 to 64 °C using van’t Hoff analysis. All van’t Hoff plots for n-alkanes (C8–C12) and other probe analytes were linear, indicating the absence of detectable phase-transition behavior. The [P 66614 + ][Cl - ] IL was further compared with the [P 66614 + ] bis[(trifluoromethyl)sulfonyl]imide ([P 66614 + ][NTf 2 - ]) IL and commercial polysiloxane stationary phases in terms of temperature-dependent resolution behavior and chromatographic selectivity over the temperature range of 28–52 °C. The resolution factors of analytes in mixtures obtained using gas chromatography–mass spectrometry (GC–MS) and the summation of resolution values decreased smoothly with temperature on both IL stationary phases and on the dimethylphenylcyano-substituted polymeric stationary phase (OV-1701), whereas the poly(50% diphenyl/50% dimethyl siloxane) stationary phase (SPB-50) exhibited irregularities in its temperature-dependent summation of resolution profile, showing a distinct maximum in the temperature range of 34–40 °C. Selectivity values for the IL stationary phases were largely determined by the anion, with the [P 66614 + ][Cl - ] IL providing enhanced separation between polar and aromatic analytes, while the [P 66614 + ][NTf 2 - ] IL favored dispersive and π-π interactions that resulted in a reversal of elution order for some aliphatic–aromatic and alkyne-containing analyte pairs. Capillary columns prepared using different surface activation methods and coated with identical [P 66614 + ][Cl - ] IL stationary phase showed indistinguishable temperature-dependent trends in summation of resolution value and selectivity. An evaluation of analyte-specific selectivity provides insight into the characteristic separation behavior of the stationary phases and enables identification of analyte classes that are more efficiently separated on the [P 66614 + ]-based IL stationary phases compared to conventional stationary phase materials.

Chromatographic selectivity↗
DOE OSTI2020

Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms

Job scheduling and machine layout are interrelated in improving energy consumption (EC) and productivity measures such as tardiness and represent two important decisions that must be made by manufacturers. This interdependency can be explained by transportation time, which connects scheduling and layout. Scheduling and layout, however, have not been thoroughly studied in conjunction using an integrated model in the context of sustainable manufacturing. Hence, we propose an energy-aware optimization model in which scheduling is integrated with layout in a single-level framework. More specifically, a single objective function is defined to minimize the facility energy cost and the job tardiness penalty, which control EC and tardiness respectively in a flexible job shop system. In order to model machine EC more accurately, we also consider three different machine states: a processing state and two idle states. Our case studies show that the integrated model exhibits better performance in controlling manufacturing EC and job tardiness than a non-integrated model in which machine locations are uncontrollable and transportation times between machines are unchangeable. To deal with large-sized problems, we also introduce four new metaheuristics. The performances of these new metaheuristics are compared in terms of objective function values and CPU times using various case studies. The results indicate that a hybrid ant colony optimization and simulated annealing (ACO-SA) algorithm provides better performance than the other algorithms. Specifically, our case studies show that the integrated model using ACO-SA can improve the objective function value by around 5% when compared to the non-integrated model.

42 ENGINEERING↗
DOE OSTI2022

Comparison of Machine Learning and Deep Learning for View Identification from Cardiac Magnetic Resonance Images

Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. Methods: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. Results: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. Conclusions: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.

artificial intelligence↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2025

Development of fiber orientation in injection molding: Comparison of glass fiber, carbon fiber and their hybrid composites

Fiber orientation distribution (FOD) in injection-molded panels with respect to distance from the gate was analyzed using X-ray computed tomography (X-CT) for glass fiber (GF), carbon fiber (CF), and hybrid CF/GF (CGF) reinforced nylon 66. To understand the reason behind the FOD with different fiber types, computational fluid dynamics (CFD) and rheology were performed. Samples were extracted at three locations: near the gate, center, and opposite end. Thickness of the layers of typical skin-shell-core type FOD varied with fiber type and location. GF achieved flow direction alignment (in shell) earlier than viscous CF and CGF near the gate, whereas CF showed the highest flow-direction alignment at the center due to shear induced orientation. At the opposite end, GF experienced more backflow than others indicating faster mold filling owing to its lower viscosity. Hybrid CGF exhibited GF-dominated center and CF-dominated end region. The numerical model used to obtain FOD and rheological predictions for the CF and GF composites served to corroborate the trends observed in the experimental trials. The FOD responses across fiber types and location were reflected in their longitudinal and transverse properties. Only GF showed higher longitudinal modulus over transverse modulus near the gate attributed to rapid alignment, whereas CF and CGF exhibited opposite trend. However, fountain flow enhanced the longitudinal modulus over transverse modulus with the distance for all, particularly for CF. This study offers insights into mold filling behavior of different fibers which are critical in optimizing injection molding conditions for tailored final properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A detailed experimental and modeling comparison of molecular radiative heat loss in a spark-ignition engine

Radiative heat transfer has been chiefly considered negligible in internal combustion engines, except for Diesel engines where soot radiation was recognized as a significant radiative transfer source. Only more recently, detailed simulations have shown that molecular radiation can be substantial as well. In extension to this, molecular radiative heat transfer can reach detectable levels of about 5–10% of the total heat transfer in spark-ignited engines. The broadband radiative nature of the significant emitting molecules, carbon dioxide and water, makes it necessary to address whether radiative trapping plays a substantial role in either total heat loss or energy redistribution within the cylinder. An experimental setup that allows measurements of the infrared emissions at a 2-crank-angle-degree resolution was developed to determine the importance of radiative trapping by carbon dioxide and water. Measurements were conducted in the well-characterized and documented TCC-III engine at the University of Michigan. The engine operated on a stoichiometric propane/air mixture at a speed of 1,300 RPM. Large Eddy Simulations with added line-by-line photon Monte-Carlo simulations of the molecular radiation were an integral part of this study to plan and devise the measurements. Post-processing of the simulation data included an accurate representation of the experimental volume from where infrared signals are collected. Additionally, the experimental data were used for validation of the photon Monte-Carlo simulations. Joint analysis of experimental and simulated spectra allowed quantifying the significance of radiative trapping. Results show the significant role of radiative trapping when predicting radiative heat transfer in the TCC-III engine.

42 ENGINEERING↗