Predicting Performance Margins:Linking the microscale to the macroscale.
Abstract not provided.
Holm, Elizabeth A↗
SEARCH · Search NASA
Results for “macroscale”
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.
Direct Numerical Simulations of Macroscale Structures with Embedded Polycrystalline Microstructures and Comparisons to Homogenization Theory.
Abstract not provided.
Bishop, Joseph E.↗
Understanding the Effects of Microscale Material Variability in Macroscale Structures through Direct Numerical Simulations.
Abstract not provided.
Bishop, Joseph E.↗
Simulating dislocation plasticity in BCC metals by integrating fundamental concepts with macroscale models.
Abstract not provided.
Battaile, Corbett Chandler.↗
Modeling Mechano-Electrochemical Phenomena from the Mesoscale to the Macroscale.
Abstract not provided.
Roberts, Scott Alan↗
Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues
In upscaling methods, closures for nonlinear problems present a well-known challenge. While a number of theoretical methods have been proposed for handling such closures, nonlinearities still remain a significant obstacle for many problems. In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in multiscale tissues. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used in an algorithm for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space rather than incorporating them as part of a constraint in the optimization process. Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are three outcomes of this work of particular note. 1) Our overall approach results in an upscaled nonlinear PDE. The PDE is closed using a neural network, and our approach results in the definition of the classical effectiveness factor for effecting closure. 2) We identify particular source terms for the closure problem that are important for representing the structure of the closure. These source terms involve macroscale concentrations and their gradients. We adopt these source terms to use as explicit features in the learning algorithm. We find the trained networks that include the macroscale source terms generate models that are able to predict the correction factor with increased fidelity over those that do not. 3) We find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometrical complexity of the two example tissue types. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling where the exact form of the macroscale equation remains unknown.
97 MATHEMATICS AND COMPUTING↗
A multiscale anisotropic polymer network model coupled with phase field fracture
The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local-global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double-edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. In conclusion, the results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber-like materials.
42 ENGINEERING↗
Molecular shear heating and vortex dynamics in thermostatted two dimensional Yukawa liquids
It is well known that two-dimensional macroscale shear flows are susceptible to instabilities leading to macroscale vortical structures. The linear and nonlinear fate of such a macroscale flow in a strongly coupled medium is a fundamental problem. A popular example of a strongly coupled medium is a dusty plasma, often modelled as a Yukawa liquid. Recently, laboratory experiments and molecular dynamics (MD) studies of shear flows in strongly coupled Yukawa liquids indicated the occurrence of strong molecular shear heating, which is found to reduce the coupling strength exponentially leading to the destruction of macroscale vorticity. To understand the vortex dynamics of strongly coupled molecular fluids undergoing macroscale shear flows and molecular shear heating, MD simulation has been performed, which allows the macroscopic vortex dynamics to evolve, while at the same time “removes” the microscopically generated heat without using the velocity degrees of freedom. We demonstrate that by using a configurational thermostat in a novel way, the microscale heat generated by shear flow can be thermostatted out efficiently without compromising the large scale vortex dynamics. In the present work, using MD simulations, a comparative study of shear flow evolution in Yukawa liquids in the presence and absence of molecular or microscopic heating is presented for a prototype shear flow, namely, Kolmogorov flow.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Solar wind temperature and speed
Averaging on a time scale of several solar rotations, the solar wind proton temperature, T, increases monotonically with bulk speed V. This macroscale T-V relation does not change appreciably with solar cycle. The temperatures corresponding to intervals of increasing speed are only approximately 15% higher than those corresponding to decreasing speeds, indicating that the macroscale T-V relation is not appreciably affected by stream interactions. On a time scale of a tenth of a solar rotation, there are time dependent T(t) - V(t) relations which are closely related to the stream profiles. These T(t) - V(t) relations can meaningfully be resolved into two components - the macroscale T-V relation and systematic, time-dependent deviations from the macroscale relation. These results support the view that the macroscale T-V relation is not appreciably affected by non-steady, interplanetary processes, but is determined rather by the proton heating mechanism.
Burlaga, L. F.↗
Effective properties of a fly ash geopolymer: Synergistic application of X-ray synchrotron tomography, nanoindentation, and homogenization models
Microstructural and micromechanical investigation of a fly ash-based geopolymer using: (i) synchrotron X-ray tomography (XRT) to determine the volume fraction and tortuosity of pores that are influential in fluid transport, (ii) mercury intrusion porosimetry (MIP) to capture the volume fraction of smaller pores, (iii) scanning electron microscopy (SEM) combined with multi-label thresholding to identify and characterize the solid phases in the microstructure, and (iv) nanoindentation to determine the component phase elastic properties using statistical deconvolution, is reported in this paper. The phase volume fractions and elastic properties are used in multi-step mean field homogenization (Mori–Tanaka and double inclusion) models to determine the homogenized macroscale elastic modulus of the composite. The homogenized elastic moduli are in good agreement with the flexural elastic modulus determined on macroscale paste beams. The combined use of microstructural and micromechanical characterization tools at multiple scales provides valuable information towards the material design of fly ash geopolymers.
36 MATERIALS SCIENCE↗
Operator learning for predicting multiscale bubble growth dynamics
We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Benchmarking Microscale Ductility Measurements (Final Report of the Project DE-NE0008799)
Conventional macroscale experimentation is generally considered to be straightforward with few limitations. Conversely, micro/nanoscale experimentation presents numerous challenges in loading device design, sample preparation and handling, as well as accurate understanding of grain size and local texture effects on recorded measurements. Despite these challenges, nanopillar compression, MEMs based micro-tension, and nanoindentation approaches have been able to provide fundamental contributions to the understanding of material behavior at small lengthscales. However, the overarching shortcoming of these micro/nanoscale experimentation approaches, is the inability to directly translate measurements evaluated at the nm and µm length scales (e.g., hardness) to macroscale tensile material behavior (i.e., elastic modulus, yield strength, and ductility). The objectives of the proposed study are, 1) to establish best practices for obtaining tensile microscale ductility measurements, and 2) to validate methodologies to for comparing microscale ductility measurements to macroscale ductility measurements. In order to achieve these objectives, a multi-lengthscale, multi-temperature testing protocol and simulation framework are executed first on copper as a model material to validate the following approach, and second on reactor grade Zircaloy-2. Experiments are conducted on specimens extracted from the same test piece to ensure nominally identical grain size and texture from specimens to specimen. Motivated by the need to isolate the contribution of size-effects on obtained mechanical property measurements, specimens are manufactured with thicknesses at the micro- (1-10 µm), meso- (10-100's µm), and macroscales (sub-sized ASTM E8). In-situ full-field deformation techniques (scanning electron microscopy (SEM) grid methods and optical DIC) are incorporated into testing at each specimen length-scale to capture plasticity localization and evolution. Experimental testing for all specimens is conducted at both room temperature and elevated temperatures to probe the role of thermal activation on plastic deformation accommodation processes. Simulation efforts focus on examining the mechanical behavior of microscale specimens using a finite element approach with explicitly resolved grain morphologies, and an embedded crystal plasticity model. The cost-efficient implementation method allows for the modeling of a statistically significant number of both real (i.e., digital twin) and generated microstructures to obtain an understanding of the interrelationships between specimen microstructure and geometric variables (grain size, texture, specimen geometry, etc.) on microscale mechanical behavior.
36 MATERIALS SCIENCE↗
Solar wind temperature and speed.
When it is averaged on a time scale of several solar rotations, the solar wind proton temperature T increases monotonically with the bulk speed V (Hundhausen et al., 1970; Burlaga and Ogilvie, 1970). This macroscale T-V relation does not change appreciably with solar cycle. The temperatures corresponding to intervals of increasing speed are only 15% higher than those corresponding to decreasing speeds, and thus it is indicated that the macroscale T-V relation is not appreciably affected by stream interactions. On a time scale of a tenth of a solar rotation there are time dependent T(t)-V(t) relations that are closely related to the stream profiles, as was noted by Hundhausen (1973). These T(t)-V(t) relations can meaningfully be resolved into two components - the macroscale T-V relation and systematic time dependent deviations from the macroscale relation.
Burlaga, L. F.↗
On Multiscale Modeling: Preserving Energy Dissipation Across the Scales with Consistent Handshaking Methods
A mesh objective crack band model was implemented within the generalized method of cells micromechanics theory. This model was linked to a macroscale finite element model to predict post-peak strain softening in composite materials. Although a mesh objective theory was implemented at the microscale, it does not preclude pathological mesh dependence at the macroscale. To ensure mesh objectivity at both scales, the energy density and the energy release rate must be preserved identically across the two scales. This requires a consistent characteristic length or localization limiter. The effects of scaling (or not scaling) the dimensions of the microscale repeating unit cell (RUC), according to the macroscale element size, in a multiscale analysis was investigated using two examples. Additionally, the ramifications of the macroscale element shape, compared to the RUC, was studied.
Pineda, Evan J.↗
Improved multiphysics model of the High Temperature Engineering Test Reactor for the simulation of loss-of-forced-cooling experiments
We present a multiphysics model of the High Temperature Engineering Test Reactor for comparison with past and predict future loss-of-forced-cooling (LOFC) experiments. The approach selected combines (1) 3-D full-core superhomogenization-corrected neutronics, (2) 3-D full-core homogenized or semi-heterogeneous heat transfer (macroscale), (3) 2-D axisymmetric fuel rod heat transfer (mesoscale), and (4) 1-D thermal-hydraulics channels. Although large sensitivities remain and the available experimental data are insufficient for use in a rigorous validation exercise, the overall transient behavior is successfully reproduced. The novelty of our work includes (1) a new macroscale-mesoscale heat transfer coupling approach relying on gap conductance to drastically speed up numerical convergence by two orders of magnitude, (2) determination of a radial effective thermal conductivity that yields reasonably close results between the homogenized and semi-heterogeneous macroscale thermal models, and (3) a preliminary study of the reactor's early behavior following a LOFC event, thus enabling further assessment of numerical models against fission power measurements.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Multiscale topology optimization using neural network surrogate models
In this study, we are concerned with optimization of macroscale elastic structures that are designed utilizing spatially varying microscale metamaterials. The macroscale optimization is accomplished using gradient-based nonlinear topological optimization. But instead of using density as the optimization decision variable, the decision variables are the multiple parameters that define the local microscale metamaterial. This is accomplished using single layer feedforward Gaussian basis function networks as a surrogate models of the elastic response of the microscale metamaterial. The surrogate models are trained using highly resolved continuum finite element simulations of the microscale metamaterials and hence are significantly more accurate than analytical models e.g. classical beam theory. Because the derivative of the surrogate model is important for sensitivity analysis of the macroscale topology optimization, a neural network training procedure based on the Sobolev norm is described. Since the SIMP method is not appropriate for spatially varying lattices, an alternative method is developed to enable creation of void regions. Lastly, the efficacy of this approach is demonstrated via several examples in which the optimal graded metamaterial outperforms a traditional solid structure.
42 ENGINEERING↗
Macro-to-microchannel transition in two-phase flow: Part 1 - Two-phase flow patterns and film thickness measurements
The classification of macroscale, mesoscale and microscale channels with respect to two-phase processes is still an open question. The main objective of this study focuses on investigating the macro-to-microscale transition during flow boiling in small scale channels of three different sizes with three different refrigerants over a range of saturation conditions to investigate the effects of channel confinement on two-phase flow patterns and liquid film stratification in a single circular horizontal channel (Part 2 covers the flow boiling heat transfer and critical heat flux). This paper presents the experimental two-phase flow pattern transition data together with a top/bottom liquid film thickness comparison for refrigerants R134a, R236fa and R245fa during flow boiling in small channels of 1.03, 2.20 and 3.04 mm diameter. Based on this work, an improved flow pattern map has been proposed by determining the flow patterns transitions existing under different conditions including the transition to macroscale slug/plug flow at a confinement number of Co {approx} 0.3-0.4. From the top/bottom liquid film thickness comparison results, it was observed that the gravity forces are fully suppressed and overcome by the surface tension and shear forces when the confinement number approaches 1, Co {approx} 1. Thus, as a new approximate rule, the lower threshold of macroscale flow is Co = 0.3-0.4 while the upper threshold of symmetric microscale flow is Co {approx} 1 with a transition (or mesoscale) region in-between. (author)
42 ENGINEERING↗
Using electron channeling contrast imaging to inform and improve the growth of high-efficiency GaAs solar cells on nanopatterned GaAs substrates
Patterned substrates provide opportunities for reducing the cost of high-efficiency III-V devices by incorporating mechanically weak layers beneficial for substrate reuse (e.g. by spalling). In this work, the functionality of electron channeling contrast imaging (ECCI) as a tool to efficiently understand and mitigate defect formation is exemplified by developing a process in which high-quality III-V material can be grown on nanopatterned GaAs substrates. Reactive ion etching used in the patterning process was found to damage the GaAs substrate surface, leading to the formation of stacking faults in the epitaxial material as observed by ECCI. Etching the patterned substrates in a 1 NH4OH: 1 H2O2: 50 DI H2O solution for 10 s prior to growth removed the substrate surface damage and stacking faults were no longer present. Growth of solar cell device structures initially produced samples with many macroscale flaws creating shunts in the devices, which complicated the assessment of material quality by device measurements. However, ECCI revealed that the epitaxial material surrounding macroscale flaws was free from any crystallographic defects such as stacking faults and threading dislocations. With this knowledge, we focused on refining the patterning process to eliminate the macroscale flaws. Solar cells were then grown on the improved nanopatterned substrates and exhibited device structures with defect densities less than 5 x 105 cm -2 and average conversion efficiency of 24.8%, nearly identical to devices grown on unpatterned epi-ready substrates (25.0%).
14 SOLAR ENERGY↗