SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
An improved hybrid particle-finite element method has been developed for the simulation of hypervelocity impact problems. Unlike alternative methods, the revised formulation computes the density without reference to any kernel or interpolation functions, for either the density or the rate of dilatation. This simplifies the state space model and leads to a significant reduction in computational cost. The improved method introduces internal energy variables as generalized coordinates in a new formulation of the thermomechanical Lagrange equations. Example problems show good agreement with exact solutions in one dimension and good agreement with experimental data in a three dimensional simulation.
Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.
Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. Black carbon (BC), dust, and other light-absorbing impurities (LAIs) darken the surface and enhance snowmelt by boosting the absorption of solar energy. It is therefore important for coupled aerosol–climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we conduct several thousand simulations with the Community Land Model (CLM) component of the Community Earth System Model (CESM) to characterize changes in melt runoff due to variations in the amount, timing, and nature (wet or dry) of BC deposition on the GrIS. From this large matrix of simulations, we develop a kernel relating runoff to the location, month, year (from 2006 to 2015), and magnitudes of BC concentration within precipitation and dry deposition flux. BC deposition during June–August causes the largest increase in annually integrated runoff, but winter deposition events also exert large (roughly half as great) runoff perturbations due to reexposure of impurities at the snow surface during summer melt. Current BC deposition fluxes simulated with the atmosphere component of CESM induce a climatological-mean increase in GrIS-wide runoff of ∼ 8 Gt yr −1 , or +6.8 % relative to a paired simulation without BC deposition. We also provide linear equations that relate the increase in total runoff to GrIS-wide wet and dry BC deposition fluxes. It is our hope that the runoff kernel and simple equations provided here can be used to extend the utility of state-of-the-art aerosol models.
Not provided.
The Extra-Vehicular Activity Control System (EVACS) Simulation with Nested Transactions, a recent effort of the MISSION Kernel Team, is documented. The EVACS simulation is a simulation of some aspects of the Extra-Vehicular Activity Control System, in particular, just the selection of communication frequencies. The simulation is a tool to explore mission and safety critical (MASC) applications. For the purpose of this effort, its current definition is quite narrow serving only as a starting point for prototyping purposes. (Note that EVACS itself has been supplanted in a larger scenario of a lunar outpost with astronauts and a lunar rover). The frequency selection scenario was modified to embed its processing in nested transactions. Again as a first step, only two aspects of transaction support were implemented in this prototype: architecture and state recovery. Issues of concurrency and distribution are yet to be addressed.
We have computed the Aerosol Index (AI) at 354 nm, useful for observing the presence of absorbing aerosols in the atmosphere, from aerosol simulations conducted with the Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART) module running online the GEOS-5 Atmospheric GCM. The model simulates five aerosol types: dust, sea salt, black carbon, organic carbon and sulfate aerosol and can be run in replay or data assimilation modes. In the assimilation mode, information's provided by the space-based MODIS and MISR sensors constrains the model aerosol state. Aerosol optical properties are then derived from the simulated mass concentration and the Al is determined at the OMI footprint using the radiative transfer code VLIDORT. In parallel, model derived Absorption Aerosol Optical Depth (AAOD) is compared with OMI retrievals. We have focused our study during ARCTAS (June - July 2008), a period with a good sampling of dust and biomass burning events. Our ultimate goal is to use OMI measurements as independent validation for our MODIS/MISR assimilation. Towards this goal we document the limitation of OMI aerosol absorption measurements on a global scale, in particular sensitivity to aerosol vertical profile and cloud contamination effects, deriving the appropriate averaging kernels. More specifically, model simulated (full) column integrated AAOD is compared with model derived Al, this way identifying those regions and conditions under which OMI cannot detect absorbing aerosols. Making use of ATrain cloud measurements from MODIS, C1oudSat and CALIPSO we also investigate the global impact on clouds on OMI derived Al, and the extent to which GEOS-5 clouds can offer a first order representation of these effects.
Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.
A non-intrusive uncertainty quantification method is applied to computational analysis of supersonic, low-boom aircraft. The mean and standard deviation statistics of the pressure waveforms and loudness metrics are evaluated through use of numerical quadrature. The probability density function (p.d.f.) of these outputs is evaluated via kernel density estimation. The simulations use an inviscid, embedded-boundary Cartesian-mesh flow solver in the nearfield combined with an augmented Burgers’ equation solver for propagation in the farfield. The results show that the p.d.f. of the waveform is bimodal at shocks, which makes the mean and standard deviation statistics inappropriate. Despite this limitation, we show that the moment statistics can provide effective assessment of discrepancies when comparing with experimental data. This is demonstrated by presenting uncertainty analysis of a wind-tunnel test and showing that we significantly improve the predictions when we include the test uncertainties in the simulation. Normal distributions are obtained for the ground signature and loudness metrics, which is primarily due to the careful shaping of the low-boom waveform. Separation of variables and error control are used to reduce computational cost. We demonstrate that this is an efficient approach in the sense of balancing numerical errors in the statistics quadrature with discretization errors in the solvers.
Various NASA Small Business Innovation Research grants from Marshall Space Flight Center, Langley Research Center and Ames Research Center were used to develop the 'kernel' of COMCO's modeling and simulation software, the PHLEX finite element code. NASA needed it to model designs of flight vehicles; one of many customized commercial applications is UNISIM, a PHLEX-based code for analyzing underground flows in oil reservoirs for Texaco, Inc. COMCO's products simulate a computational mechanics problem, estimate the solution's error and produce the optimal hp-adapted mesh for the accuracy the user chooses. The system is also used as a research or training tool in universities and in mechanical design in industrial corporations.
The U.S. Department of Energy Office of Nuclear Energy (DOE NE) and the Idaho National Laboratory (INL) Advanced Reactor Technologies (ART) Advanced Gas Reactor (AGR) Fuel Development and Qualification program (referred to as AGR Fuel program hereafter) are pursuing qualification of tristructural isotropic (TRISO) coated particle fuel for use in high temperature gas cooled reactors (HTGRs). The AGR Fuel program was established to provide a fuel qualification data set in support of the licensing and operation of an HTGR. BWX Technologies Nuclear Operations Group (BWXT-NOG) was subcontracted to fabricate the fuel for the AGR program. Several investments and innovations were realized in preparation to fabricate fuel for the AGR-5/6/7 irradiation experiments that brought fuel fabrication fully out of the laboratory and into engineering-scale operations. These included: • Increased the kernel fabrication line capacity and uniformity • Upgraded ancillary support equipment and processes for the tristructural isotropic (TRISO) coating furnace • Demonstrated efficient production of the matrix precursor powder by dry jet milling of co mingled components • Demonstrated an engineering-scale method for quick and efficient overcoating TRISO particles with the matrix precursor • Demonstrated an automated, multi cavity compacting system with a volumetric feed system • Demonstrated a combined-cycle thermal treatment furnace These changes increased production rates of some of these processes by an order of magnitude or more while eliminating the use of flammable solvents, multiple grinding and sorting operations, and the weighing out of individual die charges. Three fuel kernel lots were fabricated for production of the fuel for AGR-5/6/7. The initial lot (J52R-16-39316) was certified to fuel specifications but was not used because the kernels had a high fraction of internal fissures that caused an unacceptable fraction of the kernels to fragment when charged to the coating furnace where the TRISO coating would be deposited. Fragmented kernels increased the dispersed uranium in the particles and produced a worrisome fraction of dimpled particles with an elevated probability of in-pile failure. After some efforts to identify the cause of the fissure formation, two additional lots were produced with much lower fissure fractions, J52R-16-69317 and 69318. The latter kernel lot was a backup to the first and was not needed. Multiple kernel batches were composited to form each of the lots so as to simulate a commercial-scale operation where kernel batches would also be composited. Multiple TRISO coating runs were performed and the product characterized so that several could be composited into a TRISO lot. TRISO lot J52R-16-98005 conformed to all fuel specifications except the mean outer pyrocarbon (OPyC) layer thickness was thinner than specified. Furthermore, it was determined that the TRISO lot had a dispersed uranium fraction (DUF) that might result in the compacts not meeting the DUF specification. A review of the role of the OPyC layer and consequences of the DUF by the Technical Coordination Team and INL concluded that the fuel was acceptable for use in the AGR-5/6/7 irradiation experiment. The TRISO particles were overcoated with the matrix precursor that had been produced in a jet-milling operation. The overcoating was performed in equipment originally designed to coat pharmaceuticals. The overcoating process performed well; producing highly spherical and uniform overcoats requiring little upgrading and no recycle or rework. TRISO particles were overcoated with the matrix precursor to achieve nominal volumetric packing fractions (PFs) of TRISO particles of 25% and 40% for the irradiation experiments. The 40% PF compacts occupy the first and fifth test capsule in the test train while the inner three capsules are loaded with 25% PF compacts. The resinated graphite matrix precursor powder was a derivative of the German A3-27 matrix formulation, which differs from previous AGR irradiation campaigns that used an A3-3 formulation. Jet milling of the matrix powder precursor produced a finer mean graphite particle size than the milling operations used for the A3-3 matrix powder precursor. Changes made in the matrix formula and equipment yielded compacts with significantly higher matrix density than was attained in previous AGR irradiation campaigns. The changes in the matrix formulation and the means of milling the powders also complicated resolution of the three fuel compact defect fractions, DUF, exposed kernel fraction (EKF), and the silicon carbide defect fraction (SDF). Characterization data from BWXT-NOG had some anomalous results, so samples of the fuel compacts and overcoated TRISO particles were also analyzed by Oak Ridge National Laboratory (ORNL) to ensure that the defect fractions were accurately characterized.
In order to avoid contamination of unobservable and uncertain effective radiative forcing (ERF) on the diagnosis of radiative feedbacks based on short-term climate variability, we verify the Kernel-Gregory feedback calculation method using atmospheric model experiments with prescribed ERF. We show that both clear-sky radiative fluxes and all-sky radiative feedbacks have a closure between model simulations and kernel derivations. A near-zero global mean net cloud feedback is found in the simulation with prescribed ERF, which results in a more negative global net climate feedback, -2 W m -2 K -1 . Consistent with AMIP6 ensemble mean results, the lapse rate feedback is the largest contributor among all feedbacks to temperature amplification over the three poles (Arctic, Antarctic and Tibetan Plateau), followed by surface albedo feedback and Planck feedback deviation from its global mean. Except for the higher surface albedo feedback in the Antarctic, other feedbacks are almost same between the Arctic and Antarctic.
Lifting line theory describes the cumulative effect of shed vorticity from finite span lifting surfaces. In this work, the theory is reformulated to improve the accuracy of the actuator line model (ALM). This model is a computational tool used to represent lifting surfaces, such as wind-turbine blades in computational fluid dynamics. In ALM, blade segments are represented by means of a Gaussian body force distribution with a prescribed kernel size. Prior analysis has shown that a representation of the blade using an optimal kernel width $\unicode[STIX]{x1D716}^{opt}$ of approximately one quarter of the chord size results in accurate predictions of the velocity field and loads along the blades. Also, simulations have shown that use of the optimal kernel size yields accurate representation of the tip-vortex size and the associated downwash resulting in accurate predictions of the tip losses. In this work, we address the issue of how to represent the effects of finite span wings and tip vortices when using Gaussian body forces with a kernel size larger than the optimal value. This question is relevant in the context of coarse-scale large-eddy simulations that cannot afford the fine resolutions required to resolve the optimal kernel size. For this purpose, we present a filtered lifting line theory for a Gaussian force distribution. Based on the streamwise component of the vorticity transport equation, we develop an analytical model for the induced velocity resulting from the spanwise changes in lift force for an arbitrary kernel scale. The results are used to derive a subfilter-scale velocity model that is used to correct the velocity along the blade when using kernel sizes larger than $\unicode[STIX]{x1D716}^{opt}$ . Tests are performed in large-eddy simulation of flow over fixed wings with constant and elliptic chord distributions using various kernel sizes. Results show that by using the proposed subfilter velocity model, kernel-size independent predictions of lift coefficient and total lift forces agree with those obtained with the optimal kernel size.
The pattern formation and spatial spread of infectious populations are investigated using a kernel-based Susceptible–Infectious–Recovered (SIR) model applicable across a wide range of basic reproduction numbers R O . The goal is to examine the role of geographical spreaders on transient spatial pattern formation of infectious populations and the associated maximum invasive front speeds c max . In the simulations conducted here, geographical spreaders are defined as a portion of the infected population Φ experiencing high mobility between identical communities. The spatial organization of the infected population and c max are determined when the infections are randomly initiated in space within multiple communities. For small but finite , scaling analysis and numerical simulations in 1-dimension suggest that when the spreading kernel is Gaussian-shaped, where is the inverse of the infectious duration. This finding for agrees with a diffusion-based representation of mobility in 1-D. Numerical simulations in 2-D across wide-ranging suggest that , the variance of the spatial kernel describing mobility of long-distance geographical spreaders across communities, determines the spatial organization of infections across communities. When (long-distance mobility, where is the minimum spatial extent defining adjacent communities), the infectious population will experience a transient but spatially coherent pattern with a wavelength that can be derived from the spreading kernel properties. Moreover, the 2-D simulations for the bounded kernel suggest that attainment of is also dictated by but the magnitude is not sensitive to unlike diffusion-based models.
Documented here is the recent effort of the MISSION Kernel Team on an Extra-Vehicular Activity Control System (EVACS) simulation with nested transactions. The team has implemented the EVACS simulation along with a design for nested transactions. The EVACS simulation is a project wide aid to exploring Mission and Safety Critical (MASC) applications and their support software. For this effort it served as a trial scenario for demonstrating nested transactions and exercising the transaction support design. The EVACS simulation is a simulation of some aspects of the Extra-Vehicular Activity Control System, in particular, just the selection of communication frequencies. Its current definition is quite narrow, serving only as a starting point for prototyping purposes. (EVACS itself may be supplanted in a larger scenario of a lunar outpost with astronauts and a lunar rover.) Initially the simulation of frequency selection was written without consideration of nested transactions. This scenario was then modified to embed its processing in nested transactions. To simplify the prototyping effort, only two aspects of the general design for transaction support have been implemented: the basic architecture and state recovery. The simulation has been implemented in the programming language Smalltalk. It consists of three components: (1) a simulation support code which provides the framework for initiating, interacting and tracing the system; (2) the EVACS application code itself, including its calls upon nested transaction support; and (3) a transaction support code which implements the logic necessary for nested transactions. Each of these components deserves further description, but for now only the transaction support is discussed.
When a particle is subjected to an unsteady ambient flow, in terms of either time-dependent relative velocity or time-dependent temperature difference, the net heat transfer from the particle cannot be calculated based on the quasi-steady heat transfer correlation alone. Due to unsteady evolution of the thermal boundary layer, there is also a history contribution to heat transfer. The history contribution to heat transfer is expressed as a convolution integral of past evolution of temperature difference between the particle and the surrounding. While Basset history force and its finite Reynolds number extension have been well studied, similar understanding of unsteady heat transfer and thermal history kernel is lacking. Here, we use existing particle-resolved simulation results to develop a finite Peclet number thermal history kernel, which when used with the convolution integral is demonstrated to accurately predict unsteady heat transfer over a range of Peclet numbers and particle-to-fluid heat capacity ratio.
Orientation of block copolymer (BCP) morphology in thin films is critical to applications as nanostructured coatings. Despite being well-studied, the ability to control BCP orientation across all possible block constituents remains challenging. Here, in this study, we deploy coarse-grained molecular dynamics simulations to study diblock copolymer ordering in thin films, focusing on chain makeup, substrate surface energy, and surface tension disparity between the two constituent blocks. We explore the multi-dimensional parameter space of ordering using a machine-learning approach, where an autonomous loop using a Gaussian process (GP) control algorithm iteratively selects high-value simulations to compute. The GP kernel was engineered to capture known symmetries. The trained GP model serves as both a complete map of system response, and a robust means of extracting material knowledge. We demonstrate that the vertical orientation of BCP phases depends on several counter-balancing energetic contributions, including entropic and enthalpic material enrichment at interfaces, distortion of morphological objects through the film depth, and of course interfacial energies. BCP lamellae are found more resistant to these effects, and thus more robustly form vertical orientations across a broad range of conditions; while BCP cylinders are found to be highly sensitive to surface tension disparity.
Sprouting in unthreshed, ripe, hard red spring wheat (Triticum aestivum L.) is induced by rain, but sprouting does not necessarily occur because the crop is wetted. The spike and grain water conditions conducive to sprouting were determined in a series of laboratory experiments. Sprouting did not occur in field growing wheat wetted to 110% water concentration until the spike water concentration was reduced to 12% and maintained at this concentration for 2 days before wetting. When cut at growth stage 11.3, Feekes scale, Saratovskaya 20 (USSR) sprouted after 4 days drying, Olaf and Alex between 7 and 15 days drying and Columbus, recognized for its resistance to harvest time sprouting, after more than 15 days drying. Sprouting potential was enhanced after 4 wetting drying cycles in which any wetted interval was too brief to permit sufficient water imbibition to initiate sprouting. At harvest ripeness, grain water concentration exceeded spike water concentration by 0.7 percentage units. Following 6 months storage, 20% of the kernels in 300 spike bundles (simulating windrows) sprouted within 28 hrs after initiation of wetting to saturation (150% water concentration). Ninety percent sprouting occurred within 8 days in bundles maintained at 75% water concentration and higher, but less sprouting occurred in bundles dried to 50% water concentration before resaturation.