Macroevolutionary dynamics of climatic niche space

How and why lineages evolve along niche space as they diversify and adapt to different environments is fundamental to evolution. Progress has been hampered by the difficulties of linking a comprehensive empirical characterization of species niches with flexible evolutionary models that describe their evolution. Consequently, the relative influence of external episodic and biotic factors remains poorly understood. Here we characterize species' two-dimensional temperature and precipitation niche space occupied (i.e., species niche envelope) as complex geometries and assess their evolution across a large vertebrate radiation (all Aves) using a model that captures heterogeneous evolutionary rates on time-calibrated phylogenies. We find that extant birds coevolved from warm, mesic climatic niches into colder and drier environments and responded to the K-Pg boundary with a dramatic increase in disparity. Contrary to expectations of subsiding rates of niche evolution as lineages diversify, our results show that overall rates have increased steadily, with some lineages experiencing exceptionally high evolutionary rates, associated with colonization of novel niche spaces, and others showing niche stasis. Both competition- and environmental change-driven niche evolution transpire and result in highly heterogeneous rates near the present. Our findings share the limitations of all work based purely on extant taxa but highlight the growing ecological and conservation insights arising from the model-based integration of increasingly comprehensive and robust environmental and phylogenetic information.

Data-Driven Modeling of Geometry-Adaptive Steady Heat Convection Based on Convolutional Neural Networks

Heat convection is one of the main mechanisms of heat transfer, and it involves both heat conduction and heat transportation by fluid flow; as a result, it usually requires numerical simulation for solving heat convection problems. Although the derivation of governing equations is not difficult, the solution process can be complicated and usually requires numerical discretization and iteration of differential equations. In this paper, based on neural networks, we developed a data-driven model for an extremely fast prediction of steady-state heat convection of a hot object with an arbitrary complex geometry in a two-dimensional space. According to the governing equations, the steady-state heat convection is dominated by convection and thermal diffusion terms; thus the distribution of the physical fields would exhibit stronger correlations between adjacent points. Therefore, the proposed neural network model uses convolutional neural network (CNN) layers as the encoder and deconvolutional neural network (DCNN) layers as the decoder. Compared with a fully connected (FC) network model, the CNN-based model is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism, and symmetry. Furthermore, we applied the signed distance function (SDF) as the network input for representing the problem geometry, which contains more information compared with a binary image. For displaying the strong learning and generalization ability of the proposed network model, the training dataset only contains hot objects with simple geometries: triangles, quadrilaterals, pentagons, hexagons, and dodecagons, while the testing cases use arbitrary and complex geometries. According to the study, the trained network model can accurately predict the velocity and temperature field of the problems with complex geometries, which has never been seen by the network model during the model training; and the prediction speed is two orders faster than the CFD. The ability of accurate and extremely fast prediction of the network model suggests the potential of applying reduced-order network models to the applications of real-time control and fast optimization in the future.

Integration of piezoelectric stacks in components using powder bed fusion

Laser-based powder bed fusion of metals (PBF-LB/M) is the most commonly used additive manufacturing process for fabricating complex metal parts by selective, layer-wise melting of metallic powder using a laser beam. This manufacturing technique can easily fabricate parts with complex geometries that cannot be fabricated using conventional manufacturing processes. These parts with complex geometries are generally used by aerospace and space industries, and advancement in functionalization of additive manufactured parts is highly beneficial to these industries. However, the parts constructed using additive manufacturing are monolithic, stiff, and lightweight and hence, they are vulnerable to high amplitude resonant vibrations. This is due to the low damping factor of the materials used and the absence of interfaces and connections that contribute to structural damping in conventional structures. The integration of piezoelectric materials within these structures would enable the control of vibration characteristics. The techniques presented in this study will enable a high level of freedom in the placement of piezoelectric materials and investigate the potential of merging parts constructed using additive manufacturing with piezoelectric materials. Furthermore, a technique to track the stress state during the integration process, which is crucial for the pre-stress evaluation of integrated piezoelectric stacks, is presented and shows characteristics similar to a force cell. Pre-stress is successfully tracked during integration and in some concepts tensile stress onto the piezoelectric material is occurring. Finally, to verify the functionality for potential piezoelectric damping, power conversion was reported with laser vibrometer measurements and FE validation.

Comparative analyses of modeling techniques for cathodic protection

The investigation in this paper provided an outline of the used scientific models for the cathodic protection frame-work modeling and relatively assessed current modeling strategies. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) investigation was applied in six alternatives and five criteria. Among the criteria, a high criticality was put on the strengths in complex geometries and the unwavering quality of the results. From the study outcomes, it can be established that the best cathodic protection modeling technique considering a number of factors like, the strength in complex geometries like subsea structures, simplicity of use, time allotment required for estimation, industry track record and robustness of the results was the Finite Element Method (FEM) with a score of 0.73 which is a value of relative closeness to the ideal solution of 1. The second best modeling procedure was Boundary Element Method (BEM) having a value of 0.72, while the least cathodic protection modeling method was analytical models with a TOPSIS score of 0.3372. Regardless of FEM rising as the best cathodic protection modeling technique, the significant detriment related with it is the timeframe required for the estimation. Finally, this research concluded by showing different models performance and comparison made with the numerical results. It is expected that the result of this work will be of significant help for the strengthening of the application of TOPSIS for offshore/subsea engineers.