Search NASA⌕ Search

SEARCH · Search NASA

Results for “Engineering - Aeronautics, Mathematics and Computing”

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.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A combined numerical and experimental study to elucidate primary breakup dynamics in liquid metal droplet-on-demand printing

Droplet-on-demand liquid metal jetting is emerging as a powerful technology for the additive manufacturing of metallic parts. The success of this method hinges on overcoming several technological challenges. Additionally, the principal one among these challenges is the controlled repeatable ejection of single uniform droplets. Due to the high density and surface tension of liquid metals, the droplet ejection process occurs near the minimal extremes of the printability phase diagram, defined by acceptable ranges for the Weber (We) and Ohnesorge (Oh) numbers. In this work, we experimentally demonstrate the satellite-free ejection of pneumatically actuated molten tin droplets in this extreme corner of printability and use a combination of high-speed video analysis and volume-of-fluid modeling to elucidate the droplet dynamics. While the simulations at low Oh and We can correctly describe several aspects of the breakup process, such as an increasing tail and pinch-point near the nozzle, no single parameter set can completely capture the droplet shape at breakup. Instead, the experimental droplet dynamics appear to include features from both high and low Oh breakup. This disagreement is ascribed to the incomplete description of the droplet ejection process including wetting and exit effects near the nozzle opening and surface effects such as transient cooling and oxide formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Fully consistent Eulerian Monte Carlo fields method for solving probability density function transport equations in turbulence modeling

The probability density function (PDF) transport equation method is a sophisticated model for the closure of turbulent mixing and turbulent reactive flows. An efficient solution approach for solving the PDF transport equation has been vital for the method to be widely used in applications. The Eulerian Monte Carlo fields (EMCF) method has been developed to solve the PDF transport equation efficiently for decades. A recent work by Wang et al. revealed a serious issue of the EMCF method for not being fully consistent with the PDF transport equation for which the method is designed to solve. This work advances the state of the art by introducing fully consistent EMCF methods for solving the PDF transport equation. The fully consistent EMCF formulations are derived for two different PDF equation forms. The consistency of the EMCF formulations is mathematically confirmed by examining the derived moment transport equations from the EMCF formulations and from the PDF transport equation. The method of manufactured solutions is employed to further verify the consistency and convergence of the different EMCF formulations numerically. The newly introduced EMCF formulations bring the EMCF method to full consistency with the PDF transport equations for the first time.

42 ENGINEERING↗
DOE OSTI2022

Effect of void positioning on the detonation sensitivity of a heterogeneous energetic material

We show although it is well-established that voids profoundly influence the initiation and reaction behaviors of heterogeneous energetic materials such as polymer-bonded explosives (PBX) and propellants, there has been little study of how void location in different constituents in the microstructures of such materials affect the macroscale behavior. Here, we use three-dimensional (3D) mesoscale simulations to study how void placement within the reactive grains versus the polymer binder influences the shock-to-detonation transition (SDT) in a polymer-bonded explosive. The material studied here has a microstructure comprised of 75% PETN (pentaerythritol tetranitrate) grains and 25% HTPB (hydroxyl-terminated polybutadiene) polymer binder by volume. Porosities up to 10% in the form of spherical voids distributed in both the grains and polymer are considered. An Arrhenius reactive burn relation is used to model the chemical kinetics of the PETN grains under shock loading, thereby resolving the heterogeneous detonation behavior of the PBX. The influence of void location on the shock initiation sensitivity of the material is quantitatively ranked by comparing the predicted run distance to detonation (RDD) for each sample. The analysis includes inherent quantification of uncertainties arising from the stochastic variations in the microstructure morphologies and void distributions by using statistically equivalent microstructure sample sets (SEMSS), leading to probabilistic formulations for the RDD as a function of shock pressure. The calculations reveal that the location of voids in the composite microstructure significantly affects the RDD. Specifically, voids exclusively within the grains cause the PBX to be more sensitive (having shorter RDD) than voids in the polymer binder. Unique probabilistic relationships are derived to map the probability of observing RDD for each void location material case, allowing for prediction of initiation behavior anywhere in the shock pressure – RDD space. These findings agree with trends reported in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simulation of GaN-based light emitting diodes incorporating composition fluctuation effects

III-Nitride light emitting diodes (LEDs) are widely used in a range of high efficiency lighting and display applications, which has enabled significant energy savings in the last decade. Despite the wide application of GaN LEDs, transport mechanisms across InGaN/GaN heterostructures in these devices are not well explained. Fixed polarization sheet charges at InGaN/GaN interfaces lead to large interface dipole charges, which create large potential barriers to overcome. One-dimensional models for transport across such heterostructures predict turn-on voltages that are significantly higher than that found in real devices. As a result, conventional models for transport cannot predict the performance of new designs such as for longer wavelength LEDs or for multi-quantum well LEDs. In this work, we show that incorporating low and high indium compositions within quantum wells at the submicrometer scale can provide an accurate prediction of the characteristics of GaN/InGaN light emitting diodes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of pebble-based extruded carbon rods for extreme plasma heat flux environments

This work presents first experiments toward the development of continuously renewable (extrudable) pebble-based carbon rods for use as plasma-facing components in extreme steady-state plasma flux environments. The primary envisioned application of this work is a first wall that can survive long-term in future magnetic fusion power reactors while also improving recovery of the reactor fuel (tritium and deuterium atoms). Bench tests applying extreme steady-state front-surface heat loads of up to 50 MW/m 2 are presented. Continuous pebble rod front-surface recession and intact pebble recovery are successfully demonstrated, at a rate of order 0.2 cm/s. Numerical simulations of the pebble rod front-surface recession are able to match observations reasonably well, indicating that the recession mechanism can be understood as occurring due to pebble thermal expansion and resulting shock and cracking of the inter-pebble binder. Tests of the pebble rod extrusion demonstrate that friction between the rods and the stainless-steel extrusion channel is tolerably low (<50 N for the expected channel length) over a wide range of temperatures. Front-surface outgassing rates below 1000 Torr L/s/m 2 are achieved, believed to be sufficiently low for use in magnetic fusion reactors. In conclusion, initial parametric scans over pebble rod size and binder fraction to vary front-surface recession rates are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Reducing Richtmyer–Meshkov instability jet velocity via inverse design

In this work, we detail a novel application of inverse design and advanced manufacturing to rapidly develop and experimentally validate modifications to a shaped charge jet analog. The shaped charge jet analog comprises a copper liner, a high explosive (HE), and a silicone buffer. Here, we apply a genetic algorithm to determine an optimal buffer design that can be placed between the liner and the HE that results in the largest possible change in jet velocity. The use of a genetic algorithm allows for discoveries of unintuitive, complex, yet optimal buffer designs. Experiments using the optimal design verified the effectiveness of the buffer and validated the machine learning approach to hydrodynamic design optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Calibrating a finite-strain phase-field model of fracture for bonded granular materials with uncertainty quantification

To study the mechanical behavior of mock high explosives, an experimental and simulation program was developed to calibrate, with quantified uncertainty, a material model of the bonded granular material Idoxuridine and nitroplasticized Estane-5703. This paper reports on the efficacy of such a framework as a generalizable methodology for calibrating material models against experimental data with uncertainty quantification. Additionally, this paper studies the effect of two manufacturing temperatures and three initial granular configurations on the unconfined compressive behavior of the resulting bonded granular materials. In each of these cases, the same calibration framework was used; in that, hundreds of high-fidelity direct numerical simulations using a new, graphics processing unit-enabled, high-performance finite element method software, Ratel, were run to calibrate a finite-strain phase-field fracture model against experimental data. It was found that manufacturing temperature influenced the elastic response of the mock high explosives, with higher temperatures yielding a stiffer response. By contrast, it was found that the initial configuration of the grains had a negligible impact on the overall behavior of the mock high explosives though it remains possible that local damage accumulation within the specimens could be altered by the initial configurations. Overall, the calibration framework was successful at creating well-calibrated models, showing its usefulness as an engineering and scientific tool.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Reinforcement learning for bluff body active flow control in experiments and simulations

Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.

42 ENGINEERING↗
DOE OSTI2021

Artificial intelligence velocimetry and microaneurysm-on-a-chip for three-dimensional analysis of blood flow in physiology and disease

Significance Microfluidics is an important in vitro platform to gain insights into mechanics of blood flow and mechanisms of pathophysiology of human diseases. Extraction of 3D fields in microfluidics with dense cell suspensions remains a formidable challenge. We present artificial-intelligence velocimetry (AIV) as a general platform to determine 3D flow fields and a microaneurysm-on-a-chip to simulate blood flow in microaneurysms in patients with diabetic retinopathy. AIV is built on physics-informed neural networks that integrate seamlessly 2D images from microfluidic experiments or in vivo observations with physical laws to estimate full 3D velocity and stress fields. AIV could be integrated into imaging technologies to automatically infer key hemodynamic metrics from in vivo and in vitro biomedical images.

42 ENGINEERING↗
DOE OSTI2020

Modeling of gap conductance for LWR fuel rods applied in the BISON code

A new gap conductance model is proposed in this study as a combination of Toptan’s model and the Ross-Stoute model. A variance-based sensitivity analysis is performed to understand how simulation results depend on all input parameters of the proposed model. Additionally, new modeling options (e.g. fill gas thermal conductivity, temperature jump distance, thermal accommodation coefficient, etc.) are added into the nuclear fuel performance code, BISON. The need for further investigation of the gap heat transfer between fuel and cladding in BISON motivated this study to evaluate its impact on the code’s predictions. New gap conductance modeling is proposed. A series of integral-effects validation tests is performed: (1) to demonstrate the impact of the proposed model on the code’s fuel temperature predictions at the beginning of life and through the reactor’s life; (2) to ensure that the proposed model is capable of accurately modeling gap heat transfer characteristics in real-world problems; and (3) to investigate the impact of the estimation of fission gas release on the fuel temperature predictions with the proposed model. Overall, the results indicate that the proposed gap conductance model improves BISON’s predictions.

42 ENGINEERING↗
DOE OSTI2026

Modeling Flow in a Microreactor Core: From High-Fidelity CFD to Subchannel Analysis

The accurate prediction of pressure drop in tightly packed, low–Reynolds number (Re) bare rod bundles is essential for the thermal-hydraulic design of the Microreactor Applications Research Validation and EvaLuation (MARVEL) reactor and other microreactor concepts. However, existing friction factor correlations, particularly the upgraded Cheng-Todreas (UCTD) correlation, have limited validation for the small pitch-to-diameter ratios (P/Ds) and transitional flow conditions characteristic of these systems. In this work, we perform high-fidelity large-eddy simulations (LESs) of both an infinite bare rod bundle and a finite bare 37-pin scalloped rod bundle across the range 1000≤ 𝑅𝑒 ≤5500. The simulations reveal strong gap vortex–driven transitional behavior and indicate that the UCTD may underpredict the friction factor by up to 28% at 𝑃/𝐷 = 1.05. Using the LES-calculated pressure drops, we formulate a new friction factor correlation that follows the Cheng-Todreas functional form but is calibrated for low-Re and tightly packed geometries representative of a MARVEL-like reactor. The correlation is implemented in the MOOSE (Multiphysics Object-Oriented Simulation Environment) subchannel module and compared against both the LES and UCTD predictions. Across all subchannel types, the proposed model reduces the streamwise velocity differences from as high as 44% (UCTD) to below 9%, and decreases the pressure gradient differences from 13% to 25% (UCTD) to 0.7% to 7% relative to the LES results. These results suggest that the new correlation has the potential to improve the pressure drop and flow field predictions for such geometries, highlighting the importance of high-fidelity simulations in supporting microreactor thermal-hydraulic model development and motivating future pressure drop experiments for compact rod bundles to further validate these findings.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

On MCNP Stochastic Volume Estimation Normalization

This paper resolves a perennial point of confusion regarding the source-weighting normalization factor recommended in the MCNP manual (𝜋⁢𝑟 2 ) to stochastically estimate the volume of a region within an enclosing inward-directed spherical surface source with radius 𝑟. The normalization factor arises from the relationship between a sphere’ s mean chord length, its volume, and the values estimated by MCNP track-length tallies. A brief derivation is given that relates these quantities and results in the stated normalization. The correctness of this factor is demonstrated by estimating the volume of a variety of convex and nonconvex volumes. A heuristic demonstration of how biasing the inward-directed source reduces the statistical uncertainty of the stochastic volume estimate is also given, but a rigorous analysis of this improvement is left as future work.

42 ENGINEERING↗
DOE OSTI2021

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

42 ENGINEERING↗
DOE OSTI2024

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Lastly, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

42 ENGINEERING↗
DOE OSTI2020

Dislocation dynamics in polycrystalline materials

An extension of the dislocation dynamics method to polycrystalline materials is presented with emphasis on handling interactions between dislocations and grain boundaries. The key new features of the method include: (1) a grain boundary detection algorithm, (2) new dislocation emission criteria based on power dissipation and area growth, (3) mobility of dislocations at grain boundaries, and (4) extension of topological operations e.g. mesh adaption, collision, and dissociation to handle dislocations interacting with grain boundaries. Furthermore, verification and validation examples are executed to compare our calculations with existing analytic methods, molecular dynamics simulations, and experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Solidification and grain formation in alloys: a 2D application of the grand-potential-based phase-field approach

Solidification is a significant step in the forming of crystalline structures during various manufacturing and material processing techniques. Solidification characteristics and the microstructures formed during the process dictate the properties and performance of the materials. Hence, understanding how the process conditions relate to various microstructure formations is paramount. In this work, a grand-potential-based multi-phase, multi-component, multi-order-parameter phase-field model is used to demonstrate the solidification of alloys in 2D. This model has several key advantages over other multi-phase models such as it decouples the bulk energy from the interfacial energy, removes the constraints for the phase concentration variable, and prevents spurious third-phase formation at the two phase interfaces. Here, the model is implemented in a finite-element-based phase-field modeling code. The role of various modeling parameters in governing the solidification rate and the shape of the solidified structure is evaluated. It is demonstrated that the process conditions such as temperature gradient, thermal diffusion, cooling rate, etc, influence the solidification characteristics by altering the level of undercooling. Furthermore, the capability of the model to capture directional solidification and polycrystalline structure formation exhibiting various grain shapes is illustrated. In both these cases, the process conditions have been related to the growth rate and associated shape of the dendritic structure. As a result, this work serves as a stepping stone towards resolving the larger problem of understanding the process–structure–property–performance correlation in solidified materials.

36 MATERIALS SCIENCE↗