This report is intended to provide value to scientists, engineers, software developers, designers, analysts, regulators, students, and other stakeholders associated with (or intending to work with) computational models related to the mechanics of materials and structures (MOMS). This includes both modelers and experimentalists within the materials science and engineering, mechanical engineering, solid mechanics, structural dynamics, and related communities, spanning academic, industrial, and government affiliation sectors. This report was written with two types of people in mind: novices who have little or no prior experience in robust verification and validation (V&V) and associated/inseparable uncertainty quantification (UQ) practices, and those who have some V&V/UQ experience, but want to establish more rigorous practices. More specifically, researchers, developers, and students associated with materials (both structural and soft materials) and solid mechanics modeling, who utilize advanced computation, materials data, and/or experimental validation tools, should find the information in this report especially useful. It is critical that the community widely adopts robust V&V/UQ practices in order to improve trust, reduce risk, and improve the reliability of MOMS computational models. Beyond practitioners in this field, other stakeholders who can influence the future of advanced computational modeling associated with MOMS should find this report useful, as well. This includes individuals who support financial and/ or time investments in science and technologies surrounding computational modeling, such as funding officers and other decision-makers at federal agencies, and leaders/managers in industry. Educators teaching undergraduate and graduate courses related to MOMS, as well as department heads and/or deans within the relevant disciplines, also could use the information in this report to advance associated curricula and enhance research products.
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Reproducible validation and replication studies in nanoscale physics
Credibility building activities in computational research include verification and validation, reproducibility and replication, and uncertainty quantification. Though orthogonal to each other, they are related. This paper presents validation and replication studies in electromagnetic excitations on nanoscale structures, where the quantity of interest is the wavelength at which resonance peaks occur. The study uses the open-source software PyGBe : a boundary element solver with treecode acceleration and GPU capability. We replicate a result by Rockstuhl et al. (2005, doi:10/dsxw9d) with a two-dimensional boundary element method on silicon carbide (SiC) particles, despite differences in our method. The second replication case from Ellis et al. (2016, doi:10/f83zcb) looks at aspect ratio effects on high-order modes of localized surface phonon-polariton nanostructures. The results partially replicate: the wavenumber position of some modes match, but for other modes they differ. With virtually no information about the original simulations, explaining the discrepancies is not possible. A comparison with experiments that measured polarized reflectance of SiC nano pillars provides a validation case. The wavenumber of the dominant mode and two more do match, but differences remain in other minor modes. Results in this paper were produced with strict reproducibility practices, and we share reproducibility packages for all, including input files, execution scripts, secondary data, post-processing code and plotting scripts, and the figures (deposited in Zenodo). In view of the many challenges faced, we propose that reproducible practices make replication and validation more feasible. This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico ’.
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- Award ID(s):
- 1747669
- PAR ID:
- 10326430
- Date Published:
- Journal Name:
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Volume:
- 379
- Issue:
- 2197
- ISSN:
- 1364-503X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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