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Theoretical and Applied Mechanics Letters is a peer-reviewed journal published by Elsevier, covering Fluid Dynamics and Turbulent Flows, Fluid Dynamics and Vibration Analysis, Model Reduction and Neural Networks.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Theoretical and Applied Mechanics Letters 12 (2023) 45–58.
Formats any DOI in Theoretical and Applied Mechanics Letters style. No sign-up. |
| Publishes research in | Fluid Dynamics and Turbulent Flows Fluid Dynamics and Vibration Analysis Model Reduction and Neural Networks Numerical methods in engineering Computational Fluid Dynamics and Aerodynamics |
| ISSN | 2095-0349 |
| Citation impact (2-yr) | 3.17 |
| h-index | 46 |
| i10-index | 372 |
| Total citations | 12,628 |
| Article processing charge | $1,000 |
| Open access | Yes |
| Top institutions publishing here | Institute of Mechanics |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The effects of two geometric refinement strategies widespread in natural structures, chirality and self-similar hierarchy, on the in-plane elastic response of two-dimensional honeycombs were studied systematically. Simple closed-form expressions were derived for the elastic moduli of several chiral, anti-chiral, and hierarchical honeycombs with hexagon and square based networks. Finite element analysis was employed to validate…
Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable model. This can be achieved by incorporating the residual of physics equations into the loss function. Through minimizing…
In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an innovative physics-constrained Bayesian deep learning approach to reconstruct flow fields from sparse, noisy velocity data, where equation-based constraints are imposed through…
The system of turbulent thermal convection is introduced. Progresses in recent decades in the four major areas of research in turbulent convection are briefly reviewed. Some of the recent trends of the field are then discussed, which also serve to point out that the future directions in this important field of fluid mechanics lie in…
The unsteady mixed convection flow of electrical conducting nanofluid and heat transfer due to a permeable linear stretching sheet with the combined effects of an electric field, magnetic field, thermal radiation, viscous dissipation, and chemical reaction have been investigated. A similarity transformation is used to transform the constitutive equations into a system of nonlinear ordinary…
