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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Prediction and Validation of Flow Properties in Porous Lattice Structures

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. Finally, the presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

42 ENGINEERING↗
NASA NTRS1985

The casting of semi-permeable membranes in a microgravity environment

The National Electric Company of Venezuela, C.A.D.A.F.E., is sponsoring the development of this experiment which represents Venezuela's first scientific experiment in space. The experience can be classified as an educational one which could add knowledge to the fundamental study of polymeric membranes. Presently, semi-permeable membranes are being manufactured from several different kind of polymers all over the world and specific applications have been identified in fluid separation processes such as reverse osmosis, ultrafiltration and electrodyalisis. Although, the ultrastructure of asymetric and composite membranes have been under intensive study, still there are many questions about the factors affecting this structure and their degree of correlation. Nevertheless, there is indication that the entire morphological structure of polymeric membranes could be affected by the differences in specific gravity between the cast solution and the coagulation liquid, normally used in the membranes preparation process. The casting of semi-permeable membranes in space might help to identify the effect of gravity upon the structure of these membranes. It is important to recognize that the casting process involves changes of states (liquid-gas, liquid-solid) and that in a micro gravity environment, there will be a reduction on buoyancy-driven natural convection and density gradients

Ivan Vera↗
DOE OSTI2017

Monte Carlo sampling and correlation coefficients of critical experiments

We study the influence of calculation parameters on Monte-Carlo generated correlation coefficients of k{sub eff} values for a data set of critical experiments. The correlation coefficients can be used e.g. to reduce conservatism in bias estimations, but generally must be treated correctly if a data set includes correlations. We find that the actual value of the correlation coefficient for a given data set depends on the number of samples and the ratio of the sampling uncertainty to the Monte-Carlo uncertainty of the individual k{sub eff} calculation. We also discuss that the number of input parameters of the model to calculate the k{sub eff} values do not necessarily impact the convergence of the correlation coefficient with increasing sample number. However, we explain how varying the aforementioned parameters lead to different converged results for the correlation coefficients. The conclusion is that a correlation coefficient should not be discussed isolated but with the underlying uncertainties of both, the k{sub eff} estimation and the Monte Carlo sampling approach. The underlying modelling approach of the experimental data has to be described in detail. This should include a sensitivity analysis of the impact of input parameters on the k{sub eff} uncertainty with respect to the modelling approach.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Statistical measures of Planck scale signal correlations in interferometers

Here, a model-independent statistical framework is presented to interpret data from systems where the mean time derivative of positional cross correlation between world lines, a measure of spreading in a quantum geometrical wave function, is measured with a precision smaller than the Planck time. The framework provides a general way to constrain possible departures from perfect independence of classical world lines, associated with Planck scale bounds on positional information. A parametrized candidate set of possible correlation functions is shown to be consistent with the known causal structure of the classical geometry measured by an apparatus, and the holographic scaling of information suggested by gravity. Frequency-domain power spectra are derived that can be compared with interferometer data. As a result, simple projections of sensitivity for specific experimental set-ups suggests that measurements will directly yield constraints on a universal time derivative of the correlation function, and thereby confirm or rule out a class of Planck scale departures from classical geometry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Genome-centric view of carbon processing in thawing permafrost

As global temperatures rise, large amounts of carbon sequestered in permafrost are becoming available for microbial degradation. However, accurate prediction of carbon gas emissions produced from thawing permafrost is limited by our understanding of the resident microbial communities and their associated carbon metabolism. Here, metagenomic sequencing of 214 samples from intact, thawing and thawed sites collected over three years enabled the recovery of 1,529 metagenome-assembled genomes (MAGs), including many from phyla with poor genomic representation. These genomes were shown to broadly reflect the diversity of this complex ecosystem with genus-level representatives recovered for >60% of the community, constituting a two orders of magnitude increase in the number of genomes available for understanding carbon processing in this environment. Metabolic reconstruction, supported by metatranscriptomic and metaproteomic data, revealed key populations involved in organic matter degradation, including bacteria encoding a pathway for xylose degradation only previously identified in fungi. Combined analysis of the microbial communities and geochemical data highlighted lineages correlated with the production of greenhouse gases and suggest novel syntrophic relationships. Our findings link changing biogeochemistry to specific microbial lineages involved in each stage of carbon processing, providing key information for predicting the impact of climate change on these systems.

Woodcroft, Ben J.↗
DOE OSTI2020

Machine-Learning-Driven, Site-Specific Weather Forecasting for Grid-Interactive Efficient Buildings: Preprint

Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Use of the Wilshire Equations to Correlate and Extrapolate Creep Data of Inconel 617 and Nimonic 105

Advanced power plant alloys must endure high temperatures and pressures for durations at which creep data are often not available, necessitating the extrapolation of creep life. A recently developed creep life extrapolation method is the Wilshire equations, with which multiple approaches can be used to increase the goodness of fit of available experimental data and improve the confidence level of calculating long-term creep strength at times well beyond the available experimental data. In this article, the Wilshire equation is used to extrapolate the creep life of Inconel 617 and Nimonic 105 to 100,000 h. The use of (a) different methods to determine creep activation energy, (b) region splitting, (c) heat- and processing-specific tensile strength data, and (d) short-duration test data were investigated to determine their effects on correlation and extrapolation. For Inconel 617, using the activation energy of lattice self-diffusion as Q C * resulted in a poor fit with the experimental data. Additionally, the error of calculated rupture times worsened when splitting regions. For Nimonic 105, the error was reduced when heat- and processing-specific tensile strengths were used. Extrapolating Inconel 617 creep strength to 100,000 h life gave conservative results when compared to values calculated by the European Creep Collaborative Committee.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Direct numerical simulations of turbulent premixed cool flames: Global and local flame dynamics analysis

The cool flame dynamics, especially in turbulent flows, is of great interest for both practical application and fundamental research. Here, in this study, a series of direct numerical simulations of turbulent premixed n-C 7 H 16 /O 2 /O 3 /N 2 cool flames are performed, with the focus on the influence of turbulence intensity (u'/S L , where S L is the laminar flame speed) on the flame structure as well as the global and local cool flame dynamics. It is found that the cool flame front is considerably wrinkled by turbulence at high u'/S L , leading to significantly thickened turbulent cool flame brush and largely altered local reactivity compared with the reference laminar flame. However, the turbulent flame structure in the temperature space is found to be insensitive to u'/S L . Besides, with increasing u'/S L , the normalized turbulent cool flame speed (S L /S L ) is monotonically increased, attributed to substantial augmentation on the flame surface area (A T /A L ), while the stretching factor (I 0 ) remains almost constant and is smaller than 1. The underlying mechanisms for such variations are revealed through local flame dynamics analysis. Specifically, the local flame displacement speed S d is found to be strongly negatively correlated with flame curvature; meanwhile, such negative correlation and the probability distribution function (PDF) of flame curvature are barely influenced by u'/S L , leading to a weak dependence of I 0 on u'/S L . In contrast, the PDF of the tangential strain rate is found to span a much wider range and shift to the positive side as u'/S L increases, suggesting that the enhanced tangential strain rate is the main cause for the increase in surface area of the turbulent premixed cool flame. Finally, the influence of equivalence ratio on above findings is found to be insignificant, indicating that although the local reactivity of turbulent premixed cool flames is altered due to the differential diffusion, the resultant flame- stretch interaction is insensitive to the equivalence ratio. This study presents some unique cool flame dynamics that are distinct from hot flames, which can help improve the understanding and modeling of turbulent cool flames.

Cool flames↗
DOE OSTI2021

Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases

Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These important enzymes are often bimodular, including a catalytic domain and carbohydrate binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our ML results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Phase transition in two-dimensional tellurene under mechanical strain modulation

Here, we carry out computational simulations based on density functional theory (DFT) to investigate different phases of two-dimensional (2-D) tellurene. These phases are classified by their characteristic space groups and symmetry elements. Correlations of these phases to the bulk crystalline tellurium structure are also illustrated. Our specific interests include mechanical property calculations for different phases and the possible phase transitions between them. Simulation results show that these 2-D Te phases have very different elastic moduli due to their different atomic bonding and relaxed structures. Moreover, compression along the in-plane directions facilitates the α → β phase transition, while in-plane tensile strains always make the α-phase more stable than the β-phase. However, the energy difference between the two phases is comparable to or even much smaller than the thermal energy kT, depending on the in-plane strain direction. We find that further increase of the tensile strain along the chain direction beyond a critical value, ca. 12%, may lead to a possible α → γ phase transition. As the tensile strain is above 15%, the γ-phase will be more stable than the α-phase, accompanied by a further reduced transition energy barrier.

2-D materials↗
DOE OSTI2019

From optical to X-ray ghost imaging

Recent advances in ghost imaging techniques and X-ray sources such as synchrotrons and, more recently, X-ray free-electron lasers (XFEL) have made X-ray ghost imaging a growing topic of interest. One specific type of ghost imaging utilizes thermal radiation and the measurement of intensity fluctuation correlation to form a true image without the need of a lens. This technique allows for much higher resolution than traditional X-ray imaging for a mesoscopic or even a microscopic object. In addition to this benefit of not requiring a lens, a surprising experiment has shown that, when set up correctly, this type of ghost imaging can provide clear images through the measurement of intensity fluctuation correlation when traditional images through measurements of intensity are blurred due to optical turbulence and vibrations. This turbulence-free technique will help maintain the high resolution of X-ray ghost imaging. How is an image formed from fluctuations in light? And what makes it turbulence-free? Using the concept of two-photon interference, this article provides an introduction to these fundamentally interesting concepts and X-ray ghost imaging.

ghost imaging↗
DOE OSTI2021

Contributions of biofilm-induced flow heterogeneities to solute retention and anomalous transport features in porous media

We report that microbial biofilms are ubiquitous within porous media and the dynamics of their growth influence surface and subsurface flow patterns which impacts the physical properties of porous media and large-scale transport of solutes. Here, a two-dimensional pore-scale numerical model was used to evaluate the impact of biofilm-induced flow heterogeneities on conservative transport. Our study integrates experimental biofilm images of Paenibacillus 300A strain in a microfluidic device packed with cylindrical grains in a hexagonal distribution, with mathematical modeling. Biofilm is represented as a synthetic porous structure with locally varying physical properties that honors the impact of biofilm on the porous medium. We find that biofilm plays a major role in shaping the observed conservative transport dynamics by enhancing anomalous characteristics. More specifically, when biofilm is present, the pore structure in our geometry becomes more spatially correlated. We observe intermittent behavior in the Lagrangian velocities that switches between fast transport periods and long trapping events. Our results suggest that intermittency enhances solute spreading in breakthrough curves which exhibit extreme anomalous slope at intermediate times and very marked late solute arrival due to solute retention. The efficiency of solute retention by the biofilm is controlled by a transport regime which can extend the tailing in the concentration breakthrough curves. These results indicate that solute retention by the biofilm exerts a strong control on conservative solute transport at pore-scale, a role that to date has not received enough attention.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin

Prostate cancer (PCa) is frequently diagnosed in men, and dysregulation of microRNAs is characteristic of many cancers. MicroRNA-1207-3p is encoded at the non-protein coding gene locus PVT1 on the 8q24 human chromosomal region, an established PCa susceptibility locus. However, the role of microRNA-1207-3p in PCa is unclear. We discovered that microRNA-1207-3p is significantly underexpressed in PCa cell lines in comparison to normal prostate epithelial cells. Increased expression of microRNA-1207-3p in PCa cells significantly inhibits proliferation, migration, and induces apoptosis via direct molecular targeting of FNDC1, a protein which contains a conserved protein domain of fibronectin (FN1). FNDC1, FN1, and the androgen receptor (AR) are significantly overexpressed in PCa cell lines and human PCa, and positively correlate with aggressive PCa. Prostate tumor FN1 expression in patients that experienced PCa-specific death is significantly higher than in patients that remained alive. Furthermore, FNDC1, FN1 and AR are concomitantly overexpressed in metastatic PCa. Consequently, these studies have revealed a novel microRNA-1207-3p/FNDC1/FN1/AR regulatory pathway in PCa. - Graphical abstract: miR-1207-3p/FNDC1/FN1/AR is a novel regulatory pathway in prostate cancer. - Highlights: • Expression of microRNA-1207-3p is significantly lost in prostate cancer (PCa) cells. • MicroRNA-1207-3p regulates proliferation, apoptosis, and migration via direct molecular targeting of the 3′UTR of FNDC1. • MicroRNA-1207-3p regulates proliferation, apoptosis, and migration via direct molecular targeting of the 3′UTR of FNDC1. • FNDC1, FN1, and AR are concurrently overexpressed in metastatic PCa.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

AIM2 is a potential therapeutic target in human renal carcinoma and suppresses its invasion and metastasis via enhancing autophagy induction

Highlights: • Low AIM2 expression was correlated with RCC patient survival. • AIM2 suppressed the proliferation, migration and invasion of renal cancer cells. • AIM2 enhanced the expression of autophagy-related genes. • AIM2 exerts the anti-tumor effect via enhancing the induction of autophagy. Absent in melanoma 2 (AIM2) as a tumor suppressor is recently recognized to play an important role in many human solid tumors. However, the biological significance and function of AIM2 in renal cell carcinoma (RCC) remain unknown. In this study, we proposed to investigate the clinicopathological and prognostic significance of AIM2 in RCC patient specimens, to analyze the correlation between AIM2 expression and disease progression, and to further investigate the role and mechanism of AIM2 in malignant behaviors of renal carcinoma by cellular and animal models. The correlation analysis showed that low AIM2 expression was significantly correlated with lymph node metastasis, poor 5-year overall (P < 0.05) and disease-specific survival (P < 0.05) in RCC patients. Over-expression of AIM2 in 786-O or OSRC-2 cells suppressed the cell proliferation, migration and invasion as compared with control. The increased AIM2 expression enhanced the expression of autophagy-related genes (Bcl-2, Beclin-1, LC3-Ⅱ, and ATG-5) and increased the ratio of LC3-Ⅱ/LC3-Ⅰin renal carcinoma cells. Of note, the blockade of autophagy by 3-Methyladenine (3-MA) abrogated the inhibition of cell migration and invasion by over-expressing AIM2, indicated that AIM2 suppressed the malignant behaviors of renal carcinoma by enhancing the induction of autophagy. Furthermore, 3-MA abolished the prevention of tumor growth on nude mouse established by AIM2-OSRC-2 cells. Our results suggested that AIM2 might serve as a novel prognostic indicator as well as a potential therapeutic target for renal carcinoma treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Comparison of Dislocation Characterization by Electron Channeling Contrast Imaging and Cross-Correlation Electron Backscattered Diffraction

The characterization and quantification of dislocations is predominately carried out using transmission electron microscopy (TEM). Nevertheless, the sample preparation necessary for TEM foils can be cumbersome and possibly introduce defects. Two other techniques for characterizing and quantifying dislocations are electron channeling contrast imaging (ECCI) and cross-correlation electron backscattered diffraction (CC-EBSD). ECCI is similar to TEM in that specific imaging (channeling or diffraction) conditions need to be established, but is carried out in a scanning electron microscope on bulk samples, resulting in less complex sample preparation. CC-EBSD measures subtle changes in orientation measured from EBSD patterns to calculate elastic strains and the Nye tensor to deduce and map the total geometrically necessary dislocation (GND) content. Furthermore, this work presented here compares the capabilities of ECCI and CC-EBSD to map dislocations generated from nanoindentation.

42 ENGINEERING↗
DOE OSTI2025

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Phylogenetically conserved resource partitioning in the coastal microbial loop

Resource availability influences marine microbial community structure, suggesting that population-specific resource partitioning defines discrete niches. Identifying how resources are partitioned among populations, thereby characterizing functional guilds within the communities, remains a challenge for microbial ecologists. We used proteomic stable isotope probing (SIP) and NanoSIMS analysis of phylogenetic microarrays (Chip-SIP) along with 16S rRNA gene amplicon and metagenomic sequencing to characterize the assimilation of six 13 C-labeled common metabolic substrates and changes in the microbial community structure within surface water collected from Monterey Bay, CA. Both sequencing approaches indicated distinct substrate-specific community shifts. However, observed changes in relative abundance for individual populations did not correlate well with directly measured substrate assimilation. The complementary SIP techniques identified assimilation of all six substrates by diverse taxa, but also revealed differential assimilation of substrates into protein and ribonucleotide biomass between taxa. Substrate assimilation trends indicated significantly conserved resource partitioning among populations within the Flavobacteriia, Alphaproteobacteria and Gammaproteobacteria classes, suggesting that functional guilds within marine microbial communities are phylogenetically cohesive. However, populations within these classes exhibited heterogeneity in biosynthetic activity, which distinguished high-activity copiotrophs from low-activity oligotrophs. These results indicate distinct growth responses between populations that is not apparent by genome sequencing alone.

59 BASIC BIOLOGICAL SCIENCES↗