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.
Complex system study data indicate that factors associated with multilayer insulation pose major problem in scale modeling, that numerical analysis aids correction for known compromises of scaling criteria, and that probable errors in scale modeling experiments fall within range predicted by statistical analysis.
Optimizing a large number of trajectories over a wide range of parameters is a difficult and computationally intensive, particularly when the parametric space has a large number of dimensions. Solving parametric studies like these require good initial conditions for each optimization case, which results in a significant amount of manual interaction and human judgment and can be time consuming. The Space Launch System (SLS) uses POST2 (Program to Optimize Simulated Trajectories II) to simulate different ascent trajectories and perform mission analysis. SLS mission analysis currently uses two types of large scale, multidimensional parameter spaces. The qualifying factor between these spaces is the grid density, which determines the set of applicable solution methodologies. One type has a relatively low number of dimensions (2-3), but a large number of grid coordinates (2000- 4000), whereas the second type has a relatively low number of grid coordinates (150-350), but a higher number of dimensions (7-10).
The IRIS-HEP software institute, as a contributor to the broader HEP Python ecosystem, is developing scalable analysis infrastructure and software tools to address the upcoming HL-LHC computing challenges with new approaches and paradigms, driven by our vision of what HL-LHC analysis will require. The institute uses a "Grand Challenge" format, constructing a series of increasingly large, complex, and realistic exercises to show the vision of HL-LHC analysis. Recently, the focus has been demonstrating the IRIS-HEP analysis infrastructure at scale and evaluating technology readiness for production. As a part of the Analysis Grand Challenge activities, the institute executed a "200 Gbps Challenge", aiming to show sustained data rates into the event processing of multiple analysis pipelines. The challenge integrated teams internal and external to the institute, including operations and facilities, analysis software tools, innovative data delivery and management services, and scalable analysis infrastructure. The challenge showcases the prototypes - including software, services, and facilities - built to process around 200 TB of data in both the CMS NanoAOD and ATLAS PHYSLITE data formats with test pipelines. The teams were able to sustain the 200 Gbps target across multiple pipelines. The pipelines focusing on event rate were able to process at over 30 MHz. These target rates are demanding; the activity revealed considerations for future testing at this scale and changes necessary for physicists to work at this scale in the future. The 200 Gbps Challenge has established a baseline on today's facilities, setting the stage for the next exercise at twice the scale.
It is commonplace in cosmology to analyze fields projected onto the celestial sphere, and in particular density fields that are defined by a set of points e.g. galaxies. When performing an harmonic-space analysis of such data (e.g. an angular power spectrum) using a pixelized map one has to deal with aliasing of small-scale power and pixel window functions. We compare and contrast the approaches to this problem taken in the cosmic microwave background and large-scale structure communities, and advocate for a direct approach that avoids pixelization. We describe a method for performing a pseudo-spectrum analysis of a galaxy data set and show that it can be implemented efficiently using well-known algorithms for special functions that are suited to acceleration by graphics processing units (GPUs). The method returns the same spectra as the more traditional map-based approach if in the latter the number of pixels is taken to be sufficiently large and the mask is well sampled. The method is readily generalizable to cross-spectra and higher-order functions. It also provides a convenient route for distributing the information in a galaxy catalog directly in harmonic space, as a complement to releasing the configuration-space positions and weights, and a route to spectral apodization. Finally, we make public a code enabling the application of our method to existing and upcoming datasets.
A Direct Numerical Simulation (DNS) database was created representing mixing of species under high-pressure conditions. The configuration considered is that of a temporally evolving mixing layer. The database was examined and analyzed for the purpose of modeling some of the unclosed terms that appear in the Large Eddy Simulation (LES) equations. Several metrics are used to understand the LES modeling requirements. First, a statistical analysis of the DNS-database large-scale flow structures was performed to provide a metric for probing the accuracy of the proposed LES models as the flow fields obtained from accurate LESs should contain structures of morphology statistically similar to those observed in the filtered-and-coarsened DNS (FC-DNS) fields. To characterize the morphology of the large-scales structures, the Minkowski functionals of the iso-surfaces were evaluated for two different fields: the second-invariant of the rate of deformation tensor and the irreversible entropy production rate. To remove the presence of the small flow scales, both of these fields were computed using the FC-DNS solutions. It was found that the large-scale structures of the irreversible entropy production rate exhibit higher morphological complexity than those of the second invariant of the rate of deformation tensor, indicating that the burden of modeling will be on recovering the thermodynamic fields. Second, to evaluate the physical effects which must be modeled at the subfilter scale, an a priori analysis was conducted. This a priori analysis, conducted in the coarse-grid LES regime, revealed that standard closures for the filtered pressure, the filtered heat flux, and the filtered species mass fluxes, in which a filtered function of a variable is equal to the function of the filtered variable, may no longer be valid for the high-pressure flows considered in this study. The terms requiring modeling are the filtered pressure, the filtered heat flux, the filtered pressure work, and the filtered species mass fluxes. Improved models were developed based on a scale-similarity approach and were found to perform considerably better than the classical ones. These improved models were also assessed in an a posteriori study. Different combinations of the standard models and the improved ones were tested. At the relatively small Reynolds numbers achievable in DNS and at the relatively small filter widths used here, the standard models for the filtered pressure, the filtered heat flux, and the filtered species fluxes were found to yield accurate results for the morphology of the large-scale structures present in the flow. Analysis of the temporal evolution of several volume-averaged quantities representative of the mixing layer growth, and of the cross-stream variation of homogeneous-plane averages and second-order correlations, as well as of visualizations, indicated that the models performed equivalently for the conditions of the simulations. The expectation is that at the much larger Reynolds numbers and much larger filter widths used in practical applications, the improved models will have much more accurate performance than the standard one.
Transport analysis, ion-scale turbulence measurements, and initial linear and nonlinear gyrokinetic simulations are reported for a transport validation study based on low aspect ratio NSTX-U L-mode discharges. The relatively long, stationary L-modes enabled by the upgraded centerstack provide a more ideal target for transport validation studies that were not available during NSTX operation. Transport analysis shows that anomalous electron transport dominates energy loss while ion thermal transport is well described by neoclassical theory. Linear gyrokinetic GYRO analysis predicts that ion temperature gradient (ITG) modes are unstable around normalized radii ρ=0.6-0.8, although E×B shearing rates are larger than the linear growth rates over much of that region. Deeper in the core (ρ=0.4-0.6), electromagnetic microtearing modes (MTM) are unstable as a consequence of the relatively high beta and collisionality in these particular discharges. Consistent with the linear analysis, local, nonlinear ion-scale GYRO simulations predict strong ITG transport at ρ=0.76, whereas electromagnetic MTM transport is important at ρ=0.47. The prediction of ion-scale turbulence is consistent with 2D beam emission spectroscopy (BES) that measures the presence of broadband ion-scale fluctuations. Interestingly, the BES measurements also indicate the presence of bi-modal poloidal phase velocity propagation that could be indicative of two different turbulence types. Furthermore, in the region between (ρ=0.56, 0.66), ion-scale simulations are strongly suppressed by the locally large E×B shear. Instead, electron temperature gradient (ETG) turbulence simulations predict substantial transport, illustrating electron-scale contributions can be important in low aspect ratio L-modes, similar to recent analysis at conventional aspect ratio. However, agreement within experimental uncertainties has not been demonstrated, which requires additional simulations to test parametric sensitivities. The potential need to include profile-variation effects (due to the relatively large value of ρ∗=ρ i /a at low aspect ratio), including electromagnetic and possibly multi-scale effects, is also discussed.
Transport analysis, ion-scale turbulence measurements, and initial linear and nonlinear gyrokinetic simulations are reported for a transport validation study based on low aspect ratio NSTX-U L-mode discharges. The relatively long, stationary L-modes enabled by the upgraded centerstack provide a more ideal target for transport validation studies that were not available during NSTX operation. Transport analysis shows that anomalous electron transport dominates energy loss while ion thermal transport is well described by neoclassical theory. Linear gyrokinetic GYRO analysis predicts that ion temperature gradient (ITG) modes are unstable around normalized radii $\rho$=0.6-0.8, although $E\timesB$ shearing rates are larger than the linear growth rates over much of that region. Deeper in the core ($\rho$=0.4-0.6), electromagnetic microtearing modes (MTM) are unstable as a consequence of the relatively high beta and collisionality in these particular discharges. Consistent with the linear analysis, local, nonlinear ion-scale GYRO simulations predict strong ITG transport at $\rho$=0.76, whereas electromagnetic MTM transport is important at $\rho$=0.47. The prediction of ion-scale turbulence is consistent with 2D beam emission spectroscopy (BES) that measures the presence of broadband ion-scale fluctuations. Interestingly, the BES measurements also indicate the presence of bi-modal poloidal phase velocity propagation that could be indicative of two different turbulence types. However, in the region between ($\rho$=0.56, 0.66), ion-scale simulations are strongly suppressed by the locally large $E\timesB$ shear. Instead, electron temperature gradient (ETG) turbulence simulations predict substantial transport, illustrating electron-scale contributions can be important in low aspect ratio L-modes, similar to recent analysis at conventional aspect ratio. However, agreement within experimental uncertainties has not been demonstrated, which requires additional simulations to test parametric sensitivities. The potential need to include profile-variation effects (due to the relatively large value of $\rho_*$=$\rho_i$/a at low aspect ratio), including electromagnetic and possibly multi-scale effects, is also discussed.
A methodology based on stochastic sampling of input data covariances has been developed for uncertainty quantification with the SCALE code system. This provides the capability to determine uncertainties for a wide range of responses computed with any SCALE sequence. The methods used in the stochastic computation are discussed here for determining the uncertainty in the multiplication factor of used nuclear fuel (UNF), taking into account the impact of nuclear data uncertainties on the depletion calculations.
This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.
Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximated Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.
Development of a Bayesian analysis of the scale and shape parameters in the Weibull distribution and the corresponding reliability function with respect to the usual life-testing procedures. For the scale parameter theta, Bayesian estimates of theta and reliability are obtained for the uniform, exponential, and inverted gamma prior probability densities. Bhattacharya's results (1967) for the one-parameter exponential life-testing distribution are reduced to a special case of these results. A fully Bayesian analysis of both the scale and shape parameters is developed by assuming independent prior distributions; since in the latter case, analytical tractability is not possible, Bayesian estimates are obtained through a conjunction of Monte Carlo simulation and numerical-integration techniques. In both cases, a computer simulation is carried out, and a comparison is made between the Bayesian and the corresponding minimum-variance unbiased, or maximum likelihood, estimates. As expected, the Bayesian estimates are superior.
Growing the bioeconomy requires products and pathways that are cost-competitive. Technoeconomic analyses (TEAs) aim to predict the long-term economic viability and often use what are known as n th plant cost and performance parameters. However, as TEA is more widely adopted to inform everything from early-stage research to company and investor decision-making, the n th plant approach is inadequate and risks being misused to inform the early stages of scale-up. Some methods exist for conducting first-of-a-kind/pioneer plant cost analyses, but these receive less attention and have not been critically evaluated. This article explores TEA methods for early-stage scale-up, critically evaluates their applicability to biofuels and bioproducts, and recommends strategies for producing TEA results better suited to guiding prioritization and successful scale-up of bioprocesses.
Highlights: • Soft joint showing sharp macro-zones and marked by asymmetrical process effects • Gradient of thermomechanical loads identified through progressive stages of transformation • Highly textured recrystallized β grains at the joint core with distorted β lattice • Lattices, phase fractions, microstructures and hardness homogenized after heat treatment - Abstract: Linear Friction Welding (LFW) is a solid-state joining process producing narrow joints mainly developed for the aircraft industry. The Thermo-Mechanical Processing (TMP) loads imposed on the material cause complex microstructure transformations. This study aimed at characterizing the local microstructures and crystallographic configurations to identify the mechanisms impacting the stability and the mechanical properties of a Ti17 LFW joint with the objective of establishing an innovative homogenizing Post-Weld Heat Treatment (PWHT). The process caused a narrow zone of ±2 mm from the friction interface to reach the isothermal α → β transformation temperature under a heating rate of 500 K/s followed by quenching (−100 K/s). The TMP resulted in the formation a ±3 mm wide soft Process Affected Zone (PAZ) delimited by three characteristic zones: the Welding Line (WL width ≈ 400 μm), The Thermo-Mechanically Affected Zone (TMAZ width ≈ 1 mm) and the Heat Affected Zone (HAZ). β TMP resulted in the total dissolution of the α phase in the joint core (WL + TMAZ) and formed strongly textured {110}<111> β recrystallized grains in the WL by continuous dynamic recrystallization of squeezed prior-β grains in the TMAZ. α + β TMP led to a gradual loss of hardness in the HAZ whose microstructure is similar to the one of the base material (BM) but is subject to gradual α laths dissolution due to the local temperatures experienced during LFW. A PWHT consisting in a β annealing followed by an α + β ageing resulted in a uniformly hardened weld with a homogenized Widmanstätten microstructure in the whole assembly.
Abstract not provided.
Loss of reactive nitrogen (N) from agricultural fields in the U.S. Midwest is a principal cause of the persistent hypoxic zone in the Gulf of Mexico. We used eight years of high resolution satellite imagery, field boundaries, crop data layers, and yield stability classes to estimate the proportion of N fertilizer removed in harvest (NUE) versus left as surplus N in 8 million corn (Zea mays) fields at subfield resolutions of 30 × 30 m (0.09 ha) across 30 million ha of 10 Midwest states. On average, 26% of subfields in the region could be classified as stable low yield, 28% as unstable (low yield some years, high others), and 46% as stable high yield. NUE varied from 48% in stable low yield areas to 88% in stable high yield areas. We estimate regional average N losses of 1.12 (0.64–1.67) Tg N y -1 from stable and unstable low yield areas, corresponding to USD 485 (267–702) million dollars of fertilizer value, 79 (45–113) TJ of energy, and greenhouse gas emissions of 6.8 (3.4–10.1) MMT CO 2 equivalents. Matching N fertilizer rates to crop yield stability classes could reduce regional reactive N losses substantially with no impact on crop yields, thereby enhancing the sustainability of corn-based cropping systems.
Not Available
Explore the source record for details and available documents.
The purpose of the fluid dynamics experiments in the MIR (Matched Index of-Refraction) flow system at Idaho National Laboratory (INL) is to develop benchmark databases for the assessment of Computational Fluid Dynamics (CFD) solutions of the momentum equations, scalar mixing, and turbulence models for the flow ratios between coolant channels and bypass gaps in the interstitial regions of typical prismatic standard fuel element (SFE) or upper reflector block geometries of typical Modular High-temperature Gas-cooled Reactors (MHTGR) in the limiting case of negligible buoyancy and constant fluid properties. The experiments will use optical techniques, primarily particle image velocimetry (PIV) in the INL Matched Index of Refraction (MIR) flow system.