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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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Carbon dust management is vital to the continuous operation of the Plasma Pyrolysis Assembly (PPA), which supports the Carbon Dioxide Reduction Assembly (CRA) on the International Space Station (ISS). A developmental filter system known as the Cold Trap Carbon Capture Filter is being prototyped and tested to determine its performance under different challenge aerosols.
Filtered Rayleigh scattering (FRS) is a diagnostic technique used for measurements of macroscopic properties of a gas. The FRS signal is passed through a narrow bandwidth molecular filter, commonly gaseous iodine, to measure the broadening and Doppler-shift of the scattering light from the molecules of the gas flow. However, in single-frequency, intensity-based approaches, the effects of pressure, temperature, and velocity can be difficult to deconvolve. To overcome this challenge, frequency-scanning FRS has been used to quasi-spectrally resolve the signal. In this work, a frequency-scanned, burst-mode laser was used to perform scanning FRS in an underexpanded jet to measure temperature, pressure, and radial velocity at measurement rates up to 1 kHz which is ~100,000 times faster than prior implementations of scanning FRS.
Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. Although this problem has a long history, reliable algorithms for their estimation were scant, and necessary and sufficient conditions for identifiability of the covariances were in dispute until recently. Necessary and sufficient conditions for covariance estimation and a batch estimation algorithm were presented in our previous study. This paper presents stochastic gradient descent algorithms for noise covariance estimation in adaptive Kalman filters that are an order of magnitude faster than the batch method for similar or better root mean square error. More significantly, these algorithms are applicable to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. The computational efficiency of the new algorithms stems from adaptive thresholds for convergence, recursive fading memory estimation of the sample cross-correlations of the innovations, and accelerated stochastic gradient descent algorithms. The comparative evaluation of the proposed methods on a number of test cases demonstrates their computational efficiency and accuracy.
A tunable level, phase change material-based metasurface near IR optical filter on a transmissive doped silicon-on-insulator platform is demonstrated. This type of filter is non-volatile, non-mechanical, with switching being enabled by Joule heating via current pulses.
Understanding the hydrodynamics of adiabatic two-phase flows in packed bed reactors offers numerous benefits. It enables the optimization of chemical reactions rates and products, crucial for pharmaceuticals and energy production industries. Additionally, this understanding aids in designing more efficient and compact reactors, reducing space and resource requirements. Insights gained from studying such flows in microgravity contribute to advancements of space technologies and the enhancement of our capabilities for undertaking future long duration safe, and sustainable space exploration missions. The Packed Bed Reactor Experiment-Water Recovery Series (PBRE-WRS) is a flight experiment planned for operation on the Microgravity Science Glove box (MSG) facility of the International Space Station (ISS). The objective of the experiment is to validate hydrodynamic models at a range of gas and liquid flow rates. The microgravity environment allows for measurement and observation of aspects of fluid dynamics that are unique when compared to observations made in full or partial gravity environments. The experiment consists of testing two-phase flow hydrodynamics in three different filters, four (4) orifices and one check valve test articles. The experiment utilizes the hardware of the previously flown PBRE and PBRE-2 with packed beds of different packings materials and sizes. The fluid system of the PBRE-WRS consists of a nitrogen gas delivery and water delivery subsystems. The gas delivery subsystem can accommodate two ranges of gas flow rates, just as for the water delivery system. The nitrogen gas loop is open whereas the water loop is closed. Gas is separated from the water using a gas-liquid phase separator which is located in the water delivery module. Preliminary results from ground testing show the pressure drop increasing at different rates with the liquid and gas flow rates in the Brine filter. In this work, a detailed system description is presented along with a summary of results from ground performance testing of different test sections in an end-to-end preflight testing campaign.
Understanding the hydrodynamics of adiabatic two-phase flows in packed bed reactors offers numerous benefits. It enables the optimization of chemical reactions rates and products which results in more efficient and compact reactors, thus reducing space and resource requirements, crucial for long duration space missions. The Packed Bed Reactor Experiment-Water Recovery Series (PBRE-WRS) is a flight experiment planned for operation on the Microgravity Science Glove box (MSG) facility of the International Space Station (ISS). The objective of the experiment is to validate hydrodynamic models at a range of gas and liquid flow rates, where these models are used in the design of processes for life support systems in microgravity. The microgravity environment allows for measurement and observation of aspects of fluid dynamics that are unique when compared to observations made in full or partial gravity environments. The experiment consists of testing two-phase flow hydrodynamics in three different filters, four (4) orifices and one check valve test articles. The experiment utilizes the hardware of the previously flown PBRE and PBRE-2 with packed beds of different packings materials and sizes. The fluid system of the PBRE-WRS consists of a nitrogen gas delivery and water delivery subsystems. The gas delivery subsystem can accommodate two ranges of gas flow rates, just as for the water delivery system. The nitrogen gas loop is open whereas the water loop is closed. Gas is separated from the water using a gas-liquid phase separator which is located in the water delivery module. Preliminary results from ground testing show the pressure drop across the filters increasing at different rates with the liquid and gas flow rates. In this work, a detailed system description is presented along with a summary of results from ground performance testing of different test sections in an end-to-end preflight testing campaign.
The continued scaling of artificial intelligence and telecommunications hardware is increasingly constrained by the power, bandwidth, and area limitations of transistor-based circuits. Neuromorphic processor units, analog oscillators, and active inductors and capacitors rely on complex multi-transistor architectures restricting material choices and incurring energy and footprint overhead. Here, we show that active reactance in electro-thermal memristors provides an intrinsic, material driven route to neuronal oscillator dynamics and signal processing. Using a physics-based compact modeling framework, we bridge negative differential resistance (NDR) and bias-tunable reactance, which underlies spiking dynamics in electro-thermal memristors. Memristors with negative temperature coefficients of resistance (TCR) manifest current-controlled (CC-) NDR and act as active inductors, thus generating spiking above a critical circuit capacitance; whereas memristors with positive TCR manifest voltage-controlled (VC-) NDR and active capacitance, leading to spiking above a critical inductance. By creating a compact model for La 0.7 Ca 0.3 MnO 3 as a representative VC-NDR material and comparing it with LaCoO 3 manifesting CC-NDR, we explain the physical origins of their distinct current-voltage characteristics, reactive phase shifts and consequent spiking behaviors. Finally, we demonstrate tunable filtering enabled by the active reactance of electro-thermal memristors, establishing them as a compact hardware platform for neuronal oscillator functionality and integrated filtering beyond conventional CMOS.
Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it remains unclear how the performance of these advanced methods depends on the observation network characteristics. In this study, we present initial experiments with the surface quasi‐geostrophic model, in which we compare a recently developed ensemble filter using score‐based diffusion models with the standard Local Ensemble Transform Kalman Filter (LETKF). Our results show that the analysis solutions respond differently to the number, spatial distribution, and nonlinear fraction of assimilated observations. We also find notable changes in the multiscale characteristics of the analysis errors. Given that standard DA techniques will eventually be replaced by more advanced methods, we hope this study sets the ground for future efforts to reassess the value of Earth observing systems in the context of newly emerging algorithms.
Abstract Treatment processes have been proposed that will utilize crossflow filtration to concentrate sludge waste streams at the Department of Energy's Hanford Site. Challenges associated with solid–liquid separation of the waste streams drive a necessary evaluation of available Hanford high level waste (HLW) filtration data. Limiting flux conditions during crossflow filtration are elucidated with the formation of a cake layer on the membrane surface. A mass transfer coefficient between the gel and bulk concentrations plays a critical role in determining filter flux. A correlation between the gel concentration and mass transfer coefficient is made to assist in determining filter performance of select HLW streams. As a process alternative to crossflow filtration, gravity settling of waste streams may be deployed as a solid–liquid separation technique. However, this results in a contrasting performance with the centrifuged solids concentration. A method was developed to estimate expected filtration and settling performance based on physical characterization data for Hanford tank waste samples. By assessing the estimated processing performance of HLW, technical support can be provided during flowsheet planning.
The signal to noise ratio efficiency ϵ SNR in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Employing a Savitzky-Golay filter for background subtraction, in this work, we estimated a fully analytical ϵ SNR without relying on large-statistic simulation data, but only with an arbitrary axion mass and the relevant signal shape information. Hence, our work can provide ϵ SNR using minimal computing time and resources prior to the acquisition of experimental data, without the detailed information that has to be obtained from real experiments. Axion haloscope searches have been observing the coincidence that the frequency independent scale factor ξ is approximately consistent with the ϵ SNR . This was confirmed analytically in this work, when the window length of the Savitzky-Golay filter is reasonably wide enough, i.e., at least 5 times the signal window.
Abstract In this paper, we consider a nonlinear filtering model with observations driven by correlated Wiener processes and point processes. We first derive a Zakai equation whose solution is an unnormalized probability density function of the filter solution. Then, we apply a splitting-up technique to decompose the Zakai equation into three stochastic differential equations, based on which we construct a splitting-up approximate solution and prove its half-order convergence. Furthermore, we apply a finite difference method to construct a time semi-discrete approximate solution to the splitting-up system and prove its half-order convergence to the exact solution of the Zakai equation. Finally, we present some numerical experiments to demonstrate the theoretical analysis.
In this work, a total of 20 actinide samples were irradiated in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL): 233 U (1), 235 U (1), 236 U (1), 238 U (1), 237 Np (2), 239 Pu (1), 240 Pu (1), 242 Pu (2), 244 Pu (2), 241 Am (2), 243 Am (2), 244 Cm (2), and 248 Cm (2). The samples were loaded in a basket containing a 1.1-mm thick cadmium filter. The presence of the filter drastically changes the neutron spectrum in the samples and accentuates the reaction rates in the resonance region. Isotopic ratios were determined using Multi-Collector Inductively Coupled Plasma Mass Spectrometry (MC-ICP-MS) that allowed inferring effective capture cross sections. Detailed best-estimate MCNP models of the experiment using ENDF/B-VII.0 nuclear data were developed to calculate the effective neutron cross sections and, subsequently, compare them with the cross sections inferred from experimental results.
Single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge of current methods is their inability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), to improve the sensitivity and accuracy of label-free scProteomics. TIFF extends the ion accumulation times for peptide ions by filtering out singly charged ions. The peptide identities are assigned by a three-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently identified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during lipopolysaccharide stimulation and identified time-dependent proteome changes.
The computational study of strongly-coupled, gas–solid flows at scales relevant to most environmental and engineering applications requires the use of ‘coarse-grained’ methodologies such as the two-fluid model, particle-in-cell approach or the multiphase Reynolds Averaged Navier–Stokes equations. While these strategies enable computations at desirable length- and time-scales, they rely heavily on models to capture important flow physics that occur at scales smaller than the mesh. To date, the models that do exist are based on a limited set of flow conditions, such as very dilute particle phase. To this end, we leverage a large-scale repository of CFD-DEM data to develop filter-size dependent models for the mean variance in particle volume fraction, a quantity commonly used to assess the degree of clustering, and the granular temperature, a key quantity for accurately predicting gas–solid flows. In conclusion, because of its filter-size dependence, the granular temperature model can be directly translated to coarse-grained approaches and tied directly to grid size.
Radiation transport through particle clouds plays a major role in many engineering applications. Here in this paper, we study this problem by means of solving the radiative transport equation (RTE) on a Eulerian continuous domain. The particle clouds are generated through direct numerical simulation of Navier-Stokes equations coupled with Lagrangian particle transport at different Stokes numbers. We confirm the earlier observation noted in the literature that the solution to the RTE on Eulerian mesh diverges when the Eulerian mesh size is of the order of the particle diameter. This observation is often called the homogenization error stemming from relegation of number density onto the Eulerian domain. In order to circumvent this divergence problem, we propose a filtering strategy that spreads the information of the particles to the neighboring cells in a way that the representation of the particles remains the same even when the mesh size is smaller than that of the particle size. We show through our simulations that our filtering strategy solves the issue of homogenization error for the cases where the Beer-Bouger law is valid.
Accurate modeling of mixing in large-eddy simulation (LES) / transported probability density function (PDF) modeling of turbulent combustion remains an outstanding issue. The issue is particularly salient in turbulent premixed combustion under extreme conditions such as high-Karlovitz number Ka. Here, the present study addresses this issue by conducting an a priori analysis of a power-law scaling based mixing timescale model for the transported PDF model. A recently produced DNS dataset of a high-Ka turbulent jet flame is used for the analysis. A power-law scaling is observed for a scaling factor used to model the sub-filter scale mixing timescale in this high-Ka turbulent premixed DNS flame when the LES filter size is much greater than the characteristic thermal thickness of a laminar premixed flame. The sensitivity of the observed power-law scaling to the different viewpoints (local or global) and to the different scalars for the data analysis is examined and the dependence of the model parameters on the dimensionless numbers Ka and Re (the Reynolds number) is investigated. Different model formulations for the mixing timescale are then constructed and assessed in the DNS flame. The proposed model is found to be able to reproduce the mixing timescale informed by the high-Ka DNS flame significantly better than a previous model.
Metal–organic frameworks (MOFs) hold significant promise for separating gas mixtures, especially hydrocarbons. While the main focus in the field is the development of new adsorbents for specific separations of binary mixtures, a preferable situation would be using a single framework for separating various species. This has been achieved in some flexible MOFs, where ternary mixtures can be separated at different temperatures. Here, we propose a simple yet fascinating way of utilizing the flexibility of MOFs to create tunable filters as a function of external pressure. We thus replace the more costly temperature-driven tunability with a cost-efficient external-pressure tunability. As a proof-of-concept, we select the CaMOF flexible framework for C6 hydrocarbon separation, however, our results are applicable to other flexible framework and gas molecules. Furthermore, our findings provide mechanistic insight and guidelines to engineer separation filters by designing flexible pores with critical sizes that can be effectively manipulated by external pressure.
TheWang-Sheeley-Arge (WSA) model estimates solar wind speed and interplanetary magnetic field polarity in the inner heliosphere using global photospheric magnetic field maps. WSA employs the Potential Field Source Surface (PFSS) and Schatten Current Sheet (SCS) models to determine the Sun's global coronal magnetic field. The PFSS and SCS models are connected through two radial parameters, the source surface and interface radii, which specify the overlap region between the inner SCS and outer PFSS models. Though both radii values are adjustable, they have typically been fixed to 2.5 solar radii. Our work highlights how solar wind predictions improve when the radii are allowed to vary over time. Data assimilation using particle filtering (sequential Monte Carlo) is used to infer optimal values over a fixed time window. Solar wind model predictions and satellite observations are compared with a newly developed quality-of-agreement prediction metric. The agreement metric between the model and observations is assumed to correspond to the probability of the two key WSA model parameters, the source surface and interface radii, where the highest metric value implies the optimal radii.We find that the optimal particle filter values of solar radii can perform twice as well as standard values for an exploratory period during Carrington Rotation 1901, with these values also reducing nonphysical kinking effects seen in solar magnetic field lines. Data assimilation choices of input realization and time frame have implications for variation in the solar wind over time. We present this work's theoretical context and practical applications for prediction accuracy.