This study presents a method for determining the drag parameter in the 2D shallow water (SW) equation for flows through a coastal forest by conducting a series of 3D numerical simulations (3D NSs). Following the theory of multiscale modeling, an evaluation method procedure is proposed. We first prepare a local test domain that contains a sufficient number of trees to constitute part of a coastal forest. Then, 3D NSs are conducted in this test domain with various inflow conditions. Based on the corresponding results, the momentum losses over the test domain are converted into the drag parameter of the global SW equation. A response surface of the drag parameter is constructed as a function of the flow conditions. The stabilized finite element method is employed for both the local and the global NSs, and the phase-field method is utilized to represent 3D free surfaces. Comparisons between the 2D SW calculation results and the 3D NS results are also performed to verify the validity of the proposed method.

This study presents a method for the detection of the most likely tsunami scenario among a set of possible scenarios using an observational wave sequence based on a sequential Bayesian update scheme. The proposed method consists of two phases: an offline preliminary learning phase and an online real‐time detection update phase. The innovation of this study is that proper orthogonal decomposition (POD) and Bayesian update are used together with an established tsunami simulation technique. In the offline reinforcement learning process, a series of tsunami simulations are carried out based on geophysically feasible scenarios, and the spatial modes of wave data calculated at predefined synthetic gauge locations are extracted through the application of POD. When a real tsunami event occurs and observational ocean data are obtained, the online process can then be performed as follows: using the stored spatial modes along with their component coefficients, pseudocoefficients are repeatedly estimated from the obtained wave data and used to sequentially update the most likely tsunami scenario according to the posterior probability through Bayesian update. A verification analysis is carried out to illustrate the procedure of the proposed method, and a validation analysis is conducted to demonstrate both the capabilities and applicability of the process with reasonable accuracy. A comprehensive discussion details the characteristic features of the proposed method in terms of the real‐time prediction of tsunami hazards and risks.

Tohoku University has been conducting research on the mechanical reliability of SOFCs under the financial support by NEDO (New Energy and Industrial technology Development Organization, Japan). Both numerical and experimental approaches have been taken at various levels, from electrochemical to structural analyses. An in-house code, SIMUDEL, was developed to simulate oxygen potential distribution inside the constituent materials in order to account for the chemical strain in cell/stack analyses. Non-linear stress-strain behaviors and inelastic deformation of the materials were characterized to improve the accuracy of the simulation. Predicting actual cell behavior, however, is still difficult, as unexpected factors often remain for each specific case. It is thus important to have in-situ or operando tools to observe the behavior of cells, such as deformation and stress generation. For that purpose, laser profilometer and X-ray stress analyzer were employed with a specially designed cell holder. Methods for evaluating local current density was also proposed. Currently, we are developing a protocol for testing robustness of planar SOFCs. Severe condition tests will be performed, and the results will be analyzed with the above-mentioned simulation.

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