Computational methods are increasingly being incorporated into the exploitation of microstructure–property relationships for microstructure-sensitive design of materials. In the present work, we propose non-intrusive materials informatics methods for the high-throughput exploration and analysis of a synthetic microstructure space using a machine learning-reinforced multi-phase-field modeling scheme. We specifically study the interface energy space as one of the most uncertain inputs in phase-field modeling and its impact on the shape and contact angle of a growing phase during heterogeneous solidification of secondary phase between solid and liquid phases. We evaluate and discuss methods for the study of sensitivity and propagation of uncertainty in these input parameters as reflected on the shape of the Cu6Sn5intermetallic during growth over the Cu substrate inside the liquid Sn solder due to uncertain interface energies. The sensitivity results rank
multiUQ: An intrusive uncertainty quantification tool for gas-liquid multiphase flows
Assessing the effects of input uncertainty on simulation results for multiphase flows will allow for more robust
engineering designs and improved devices. For example, in atomizing jets, surface tension plays a critical role in
determining when and how coherent liquid structures break up. Uncertainty in the surface tension coefficient can
lead to uncertainty in spray angle, drop size, and velocity distribution. Uncertainty quantification (UQ) determines
how input uncertainties affect outputs, and the approach taken can be classified as non-intrusive or intrusive. A
classical, non-intrusive approach is the Monte-Carlo scheme, which requires multiple simulation runs using samples
from a distribution of inputs. Statistics on output variability are computed from the many simulation outputs.
While non-intrusive schemes are straightforward to implement, they can quickly become cost prohibitive, suffer
from convergence issues, and have problems with confounding factors, making it difficult to look at uncertainty
in multiple variables at once. Alternatively, an intrusive scheme inserts stochastic (uncertain) variables into the
governing equations, modifying the mathematics and numerical methods used, but possibly reducing computational
cost. In this work, we extend UQ methods developed for single-phase flows to handle gas-liquid multiphase
dynamics by developing a stochastic conservative level set approach and a stochastic continuous surface tension
method. An oscillating droplet and a 2-D atomizing jet are used to test the method. In these test cases, uncertainty
about the surface tension coefficient and initial starting position will be explored, including the impact on breaking/
merging interfaces.
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- Award ID(s):
- 1511325
- NSF-PAR ID:
- 10098577
- Date Published:
- Journal Name:
- 14th Triennial International Conference on Liquid Atomization and Spray Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract σ SI ,σ IL , andσ IL , respectively, as the most influential parameters on the shape of the intermetallic. Furthermore, we use variational autoencoder, a deep generative neural network method, and label spreading, a semi-supervised machine learning method for establishing correlations between inputs of outputs of the computational model. We clustered the microstructures into three categories (“wetting”, “dewetting”, and “invariant”) using the label spreading method and compared it with the trend observed in the Young-Laplace equation. On the other hand, a structure map in the interface energy space is developed that showsσ SI andσ SL alter the shape of the intermetallic synchronously where an increase in the latter and decrease in the former changes the shape from dewetting structures to wetting structures. The study shows that the machine learning-reinforced phase-field method is a convenient approach to analyze microstructure design space in the framework of the ICME. -
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Summary This paper presents an approach for efficient uncertainty analysis (UA) using an intrusive generalized polynomial chaos (gPC) expansion. The key step of the gPC‐based uncertainty quantification
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Volunteer computing (VC) uses consumer digital electronics products, such as PCs, mobile devices, and game consoles, for high-throughput scientific computing. Device owners participate in VC by installing a program which, in the background, downloads and executes jobs from servers operated by science projects. Most VC projects use BOINC, an open-source middleware system for VC. BOINC allows scientists create and operate VC projects and enables volunteers to participate in these projects. Volunteers install a single application (the BOINC client) and then choose projects to support. We have developed a BOINC project, nanoHUB@home, to make use of VC in support of the nanoHUB science gateway. VC has greatly expanded the computational resources available for nanoHUB simulations. We are using VC to support “speculative exploration”, a model of computing that explores the input parameters of online simulation tools published through the nanoHUB gateway, pre-computing results that have not been requested by users. These results are stored in a cache, and when a user launches an interactive simulation our system first checks the cache. If the result is already available it is returned to the user immediately, leaving the computational resources free and not re-computing existing results. The cache is also useful for machine learning (ML) studies, building surrogate models for nanoHUB simulation tools that allow us to quickly estimate results before running an expensive simulation. VC resources also allow us to support uncertainty quantification (UQ) in nanoHUB simulation tools, to go beyond simulations and deliver real-world predictions. Models are typically simulated with precise input values, but real-world experiments involve imprecise values for device measurements, material properties, and stimuli. The imprecise values can be expressed as a probability distribution of values, such as a Gaussian distribution with a mean and standard deviation, or an actual distribution measured from experiments. Stochastic collocation methods can be used to predict the resulting outputs given a series of probability distributions for inputs. These computations require hundreds or thousands of simulation runs for each prediction. This workload is well-suited to VC, since the runs are completely separate, but the results of all runs are combined in a statistical analysis.more » « less
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Accepted Manuscript1.0
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- The DOI is not currently available.
