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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Bioenergetic consequences of compromised mitochondrial DNA repair in the mouse heart

Highlights: • Loss of mitochondrial DNA repair compromises bioenergetic efficiency. • Impairments in bioenergetic efficiency result from ETS stoichiometry shifts. • Bioenergetic flux in Polg{sup m/m} mitochondria favors accelerated electron leak. The progeroid phenotype of mitochondrial DNA (mtDNA) mutator mice has been nebulously attributed to general mitochondrial ‘dysfunction’, though few studies have rigorously defined the bioenergetic consequences of accumulating mtDNA mutations. Comprehensive mitochondrial diagnostics was employed to interrogate the bioenergetic properties of isolated cardiac mitochondria from mtDNA mutator mice and wild type littermates. Assessment of respiratory flux in conjunction with parallel measurements of mitochondrial free energy all point to the cause of respiratory flux limitations observed in mtDNA mutator mouse mitochondria being due to impairments within the energy transduction step catalyzed by the electron transport system in which NADH/NAD{sup +} free energy is transduced to the proton motive force (ΔP). The primary bioenergetic consequence of this limitation appears to be hyper-reduction of NAD(P)H/NAD(P){sup +} redox poise across multiple substrate conditions, particularly evident at moderate to high respiration rates. This hyper-reduced phenotype appears to result from specific reductions in both complex I and complex IV expression, presumably due to compromised mtDNA integrity. Translation of these findings to the working heart would suggest that the primary biological consequence of accumulated mtDNA damage is accelerated electron leak driven by an increase in electron redox pressure for a given rate of oxygen consumption.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Predicting and analyzing the performance of biomass-burning natural draft rocket cookstoves using computational fluid dynamics

Nearly three billion people in the world today rely on biomass for their cooking needs. Indoor cooking using biomass has been identified as a major cause of respiratory illnesses, resulting in four million premature deaths every year. Improved biomass cookstoves may help mitigate this challenge. This paper presents a two-dimensional axisymmetric steady-state computational fluid dynamics (CFD) model of a biomass burning, natural draft rocket cookstove. The CFD model includes coupled sub-models representing combustion, turbulence, and heat transfer. The model is validated against experimental data and used to predict temperatures and flow inside the cookstove, including the airflow rate through the cookstove and heat transfer to the cookpot. We find that the excess air is typically many times stoichiometric air during standard operating conditions and is sensitive to flow field obstructions. We analyze the effects of geometric and operational features such as the pot support height, secondary air entrainment, cone-deck shape, and baffle placement within the cookstove on the flow, airflow rate, mixing, and stove thermal efficiency. The model shows that secondary air entrainment, though ineffective by itself, increases turbulent mixing when used in conjunction with a central baffle but reduces thermal efficiencies due to enhanced heat transfer to the walls. We find that a lower pot support height decreases the airflow rate and increases thermal efficiency. We model thirty-six cone-deck configurations and find that the cone-deck shape primarily affects the airflow rate through the stove, with more constricted designs leading to higher thermal efficiencies.

09 BIOMASS FUELS↗
DOE OSTI2021

ESSENCE- A rapid, shear-enhanced, flow-through, capacitive electrochemical platform for rapid detection of biomolecules

The rapid, sensitive, and selective detection of target analytes using electrochemical sensors are challenging. ESSENCE, a new Electrochemical Sensor that uses a Shear-Enhanced, flowthrough Nanoporous Capacitive Electrode, overcomes current electrochemical sensors' response limitations, selectivity, and sensitivity limitations. ESSENCE is a microfluidic channel packed with transducer material sandwiched by a top and bottom microelectrode. The room-temperature instrument less integration process allows the switch of the transducer materials to make up the porous electrode without modifying the electrode architecture or device protocol. ESSENCE can be used to detect both biomolecules and small molecules by simply changing the packed transducer material. Electron microscopy results confirm the high porosity. In conjunction with the non-planar interdigitated electrode, the packed transducer material results in a flow-through porous electrode. Electron microscopy results confirm the high porosity. The enhanced shear forces and increased convective fluxes disrupt the electric double layer's (EDL) diffusive process in ESSENCE. This disruption migrates the EDL to high MHz frequency allowing the capture signal to be measured at around 100 kHz, significantly improving device timing (rapid detection) with a low signal-to-noise ratio. The device’s unique architecture allows us multiple configuration modes for measuring the impedance signal. This allows us to use highly conductive materials like carbon nanotubes. We show that by combining single-walled carbon nanotubes as transducer material with appropriate capture probes, NP-µIDE has high selectivity and sensitivity for DNA (fM sensitivity, selective against non-target DNA), breast cancer biomarker proteins (p53, pg/L sensitivity, selective against non-target HER2).

Cheng, Yu-Hsuan↗
DOE OSTI2023

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

We present NeRVI, a new deep-learning approach that compresses a large collection of visualization images generated from time-varying data for communicating volume visualization results. Based on an image-based implicit neural representation, our approach represents tens of thousands of high-resolution rendering images parametrized by different parameters via a hybrid model of multilayer perceptrons and convolutional neural networks. Here, our model predicts images and corresponding masks, and the masks are utilized for loss computation and network training to capture fine structural details and small components. In conjunction with model quantization and weight encoding, NeRVI yields highly compact compressive neural representations while preserving the image fidelity well. We demonstrate the effectiveness of NeRVI with isosurface rendering and direct volume rendering images generated from multiple data sets and compare NeRVI with other state-of-the-art deep learning-based (InSituNet, SIREN, NeRF, and NeRV) methods. Quantitative and qualitative results show that NeRVI provides an alternative solution that augments domain scientists' ability to manage, represent, and communicate scientific visualization output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. Here we achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synthesis of wafer-scale SWCNT forests with remarkably invariant structural properties in a bulk-diffusion-controlled kinetic regime

Robust synthesis of vertically aligned carbon nanotubes (VACNT) at large scale is required to accelerate deployment of numerous cutting-edge devices to emerging commercial applications. To address this need, this work demonstrates that the structural characteristics of single-walled CNTs (SWCNTs) produced in a growth regime dominated by bulk diffusion of the gaseous carbon precursor are remarkably invariant over a broad range of process conditions (precursor concentration increased up to 30-fold, catalyst substrate area from 1 cm 2 to 180 cm 2 , growth pressure from 20 to 790 mbar, and gas flowrates up to 8-fold). A simple growth kinetics model is derived that accounts for both reactant bulk diffusion and competing byproduct formation in the presence of excess hydrogen, which quantitatively reproduces the experimental data in the entire isothermal parameter space. The model enables critical predictions for process scale-up optimization including a potential 6-fold increase in CNT production rate, a ~90% carbon conversion efficiency in select conditions, and elimination of reaction rate decay observed at high pressures by using a hydrogen-free growth environment. This model-guided opportunity to substantially improve synthesis throughput and efficiency, in conjunction with the remarkably invariant CNT properties, is appealing for reliable VACNT device fabrication at both small and industrial scales.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The unique architecture of umbrella toxins permits a two-tiered molecular bet-hedging strategy for interbacterial antagonism

Bacteria exist in competitive and rapidly changing environments in which the nature of future threats cannot be easily predicted. Streptomyces coelicolor produces three antibacterial umbrella particles that harbor distinct polymorphic toxin domains and an overlapping set of six diversified lectins. Here, we show that the exquisite specificity of umbrella particles derives from lectin-mediated species-specific binding to previously undescribed hypervariable surface glycoconjugates. A cryo-electron microscopy (cryo-EM) structure of one such lectin in complex with its oligosaccharide substrate defines the molecular basis for targeting through the coordinated recognition of multiple glycan features. Biochemical and genetic studies of several target species, in conjunction with lectin-swapping experiments, support a model whereby S. coelicolor umbrella toxin diversification at the levels of lectin composition and toxin polymorphism represents a unique, two-tiered bet-hedging strategy. Bioinformatic analyses support this as a means by which the unusual architecture of umbrella toxins offers Streptomyces a generalizable strategy to antagonize an unpredictable array of competitors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Effect of nano-silica and curing conditions on the reaction rate of class G well cement exposed to geological CO2-sequestration conditions

Chemical reactions of class G oil well cement submerged in CO{sub 2}-saturated brine were experimentally investigated to evaluate the effects of nano-silica and curing conditions. The progression of depths of reaction zones (depletion, carbonation, and degradation zone) with reaction time up to 62 days were quantitatively measured based on 3D X-ray CT in conjunction with the chemical analysis. The results show that there were no significant difference in the progress of carbonation zone regardless of the curing conditions and nano-silica. The measure of reaction depth for tested specimens concludes that the curing at the high pressure and temperature is more effective to prevent the depletion zone from progressing into the cement interior than the addition of the nano-silica, supported by XRD and NMR results. Image-based measurements, coupled with chemical analyses, quantified the evolution of the reactive zones in the oil well cement, providing insights into the underlying reaction mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficacy of SCMs to mitigate ASR in systems with higher alkali contents assessed by pore solution method

This study demonstrates the efficacy of supplementary cementitious materials (SCMs) to mitigate alkali-silica reaction (ASR) even when used in conjunction with cement of higher alkali contents (up to 1% Na{sub 2}O{sub eq}). The expansion of concrete prisms was studied immersed in simulated pore solution in order to address the limitations of conventional ASR testing methods, accelerated mortar bar test (AMBT) and concrete prism test (CPT). Expansion results demonstrate that 25% fly ash and 50% slag are both sufficient to mitigate ASR even with cements with alkali content up to 1% Na{sub 2}O{sub eq} and that the pore solution method is a viable alternative ASR testing method. Massive amounts of ASR products (~20 μm thickness) were observed in concretes without SCMs consistent with high degree of expansion and extensive cracking. Small amounts of ASR products (≤5 μm thickness) were also observed in concrete with SCMs despite absence of significant expansion.

36 MATERIALS SCIENCE↗
DOE OSTI2017

High-resolution ultrahigh-pressure long column reversed-phase liquid chromatography for top-down proteomics

We report development of an approach providing high-resolution RPLC of proteins and its utility for mass spectrometry-based top-down proteomics. A chromatographic peak capacity of ~450 was achieved for proteins and large polypeptides having MWs up to 43 kDa in the context of proteomics applications. RPLC column lengths from 20 to 200 cm, particle sizes from 1.5 to 5 m, bonding alkyl chains from C1 to C2, C4, C8, and C18, and particle surface structures that spanned porous, superficially porous (porous shell, core-shell), and nonporous were investigated at pressures up to14K psi. Column length was found as the most important factor for >20 kDa proteins in gradient RPLC, and shortening column length degraded RPLC resolution and sensitivity regardless of the size and surface structure of the packing particles used. The alkyl chains bonded to the silica particle surface significantly affected the RPLC recovery and efficiency, and short alkyl C1-C4 phases provided higher sensitivity and resolution than C8 and C18 phases. Long gradient separations (e.g., >10 hours) with long columns (e.g., 100 cm) were particularly effective in conjunction with use of high accuracy mass spectrometers (e.g., the Orbitrap Elite) for top-down proteomics with improved proteoform coverage by allowing multiple HCD, CID, and ETD dissociation modes. It was also found that HCD produced small fragments useful for proteoform identification, while low energy CID and ETD often complemented HCD by providing large fragments.

columns and stationary phases↗
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 OSTI2017

Energy dissipation analysis of elastic–plastic materials

In this article, we present an energy dissipation analysis framework for granular material that is based on thermodynamics. Theoretical formulations are derived from the second law of thermodynamics, in conjunction with a few plausible assumptions on energy transformation and dissipation. The role of plastic free energy is emphasized by a conceptual experiment showing its physical nature. Theoretical formulation is adapted in order to be applied in elastic–plastic finite element method (FEM) simulations. Developed methodology is verified through comparison of input work, stored energy, and energy dissipation of the system. Lastly, separation of plastic work into plastic free energy and energy dissipation removes a common mistake, made in a number of publications, where energy dissipation can attain negative values (energy production) which is impossible.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Conforming window functions for meshfree methods

Window functions provide a base for the construction of approximation functions in many meshfree methods. They control the smoothness and extent of the approximation functions and are commonly defined using Euclidean distances which helps eliminate the need for a meshed discretization, simplifying model development for some classes of problems. However, for problems with complicated geometries such as nonconvex or multi-body domains, poor solution accuracy and convergence can occur unless the extents of the window functions, and thus approximation functions, are carefully controlled, often a time consuming or intractable task. Here in this paper, we present a method to provide more control in window function design, allowing efficient and systematic handling of complex geometries. “Conforming” window functions are constructed using Bernstein–Bézier splines defined on local triangulations with constraints imposed to control smoothness. Graph distances are used in conjunction with Euclidean metrics to provide adequate information for shaping the window functions. The conforming window functions are demonstrated using the Reproducing Kernel Particle Method showing improved accuracy and convergence rates for problems with challenging geometries. Conforming window functions are also demonstrated as a means to simplify the imposition of essential boundary conditions.

, rapid design-to-analysis↗
DOE OSTI2024

Pressure stability in explicitly coupled simulations of poromechanics with application to CO 2 sequestration

We study in detail the pressure stabilizing effects of the non-iterated fixed-stress splitting in poromechanical problems which are nearly undrained and incompressible. When applied in conjunction with a spatial discretization which does not satisfy the discrete inf–sup condition, namely a mixed piecewise linear–piecewise constant spatial discretization, the explicit fixed-stress scheme can have a pressure stabilizing effect in transient problems. This effect disappears, however, upon time step refinement or the attainment of steady state. The interpretation of the scheme as an Augmented Lagrangian method similar to Uzawa iteration for incompressible flow helps explain these results. Moreover, due to the slowly evolving solution within undrained seal regions, we show that the explicit fixed-stress scheme requires very large time steps to reveal its pressure stabilizing effect in examples of geologic CO 2 sequestration. We note that large time steps can result in large errors in drained regions, such as the aquifer or reservoir regions of these examples, and can prevent convergence of nonlinear solvers in the case of multiphase flows, which can make the explicit scheme an unreliable source of pressure stabilization. We conclude by demonstrating that pressure jump stabilization is as effective in the explicit fixed-stress setting as in the fully implicit setting for undrained problems, while maintaining the stability and convergence of the fixed-stress split for drained problems.

58 GEOSCIENCES↗
DOE OSTI2024

Infrared nanoimaging and nanospectroscopy of electrochemical energy storage materials and interfaces

Electrochemical interfaces are central to the function and performance of energy storage devices. Thus, the development of new methods to characterize these interfaces, in conjunction with electrochemical performance, is essential for bridging the existing knowledge gaps and accelerating the development of energy storage technologies. Of particular need is the ability to characterize surfaces or interfaces in a non-destructive way with adequate resolution to discern individual structural and chemical building blocks. To this end, sub-diffraction-limit low-energy infrared optical probes that exploit near-field interactions within atomic force microscopy platforms, such as pseudoheterodyne nanoimaging, photothermal nanoimaging and nanospectroscopy, and nanoscale Fourier transform infrared spectroscopy, are all powerful emerging techniques. These are capable of non-destructive surface probing and imaging at nanometer resolution. This review outlines recent efforts to characterize ex situ, in situ, and operando electrode materials and electrochemical interfaces in rechargeable batteries with these advanced infrared near-field probes.

25 ENERGY STORAGE↗
DOE OSTI2023

Effect of particle size on the capture of uranium oxide colloidal particles from aqueous suspensions via high-gradient magnetic filtration

The effectiveness of High Gradient Magnetic Filtration (HGMF) in capturing uranium oxide particles from suspensions was investigated in this study. Two sets of experiments were performed to evaluate the importance of size on the capture of uranium oxide particles. The first considered two batches sieved into size bins of< 5, 5–10, 10–15, and 15–20 µm, while the second was performed using two suspensions with diameters smaller than 1.0 µm and between 1.0 and 1.5 µm. Iron oxide experiments, with particles between 0.3 and 0.8 µm, were performed for calibration purposes. In all experiments, a surfactant (Triton-X100 or sodium dodecyl sulfate) was used to prevent particle aggregation and limit the influence of non-magnetic capture mechanisms. A magnetic field of approximately 1.1 Tesla was generated using a water cooled electromagnet. HGMF was performed using tubular filters packed with ferromagnetic stainless-steel wool. Of the initial four uranium oxide particle sizes, magnetic capture was only observed for particles with a diameter of less than 5 µm, while larger particles experienced no magnetic and minimal total capture. For particles with diameters smaller than 1.0 µm and between 1.0 and 1.5 µm, capture efficiencies increased by 39 ± 9% and 34 ± 6% respectively, solely due to the magnetic field. Although the magnetic force is proportional to particle diameter, the capture efficiency decreased as diameter increased. So these results suggest that Brownian diffusion, which is influential for micron sized particles and increases with decreasing particle size, is acting in conjunction with the magnetic force to influence the efficacy of HGMF for uranium oxide. This important finding underscores the effectiveness of Brownian diffusion in increasing the rate of collision between particles and collector fibers. A stochastic trajectory model was developed to incorporate the influence of Brownian motion on particle behavior and filter removal efficiency. Modeling results are discussed and compared for uranium and iron oxide particles.

42 ENGINEERING↗
DOE OSTI2018

Large deformation and gas retention during cookoff of a plastic bonded explosive (PBX 9407)

Here, we have used several configurations of the Sandia Instrumented Thermal Ignition (SITI) experiment to develop a pressure-dependent, four-step ignition model for a plastic bonded explosive (PBX 9407) consisting of 94 wt.% RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine), and a 6 wt.% VCTFE binder (vinyl chloride/chlorotrifluoroethylene copolymer). The four steps include desorption of water, decomposition of RDX to form equilibrium products, pressure-dependent decomposition of RDX forming equilibrium products, and decomposition of the binder to form hydrogen chloride and a nonvolatile residue (NVR). We address drying, binder decomposition, and decomposition of the RDX component from the pristine state through the melt and into ignition. We used Latin Hypercube Sampling (LHS) of the parameters to determine the sensitivity of the model to variation in the parameters. We also successfully validated the model using one-dimensional time-to-explosion (ODTX and P-ODTX) data from a different laboratory. Our SITI test matrix included 1) different densities ranging from 0.7 to 1.63 g/cm 3 , 2) free gas volumes ranging from 1.2 to 38 cm 3 , and 3) boundary temperatures ranging from 170 to 190 °C. We measured internal temperatures using embedded thermocouples at various radial locations as well as pressure using tubing that was connected from the free gas volume (ullage) to a pressure gauge. We also measured gas flow from our vented experiments. A borescope was included to obtain in situ video during some SITI experiments. We observed significant changes in the explosive volume prior to ignition. Our model, in conjunction with data observations, imply that internal accumulation of decomposition gases in high density PBX 9407 (90% of the theoretical maximum density) can contribute to significant strain whether or not the experiment is vented or sealed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗