For C/C composite finger seals, complex working parameters such as rotor runout, assembly clearance and impact19,20,21, contact pressure, dynamic response, friction and wear, leakage rate and other dynamic performances have been studied22,23,24. The results showed that C/C composite can significantly improve the dynamic performances of finger seals, reduce wear rate and increase service life. The wear mechanism and analytical calculation laid a foundation for the wear life prediction of finger seals25,26.
- A possibility opened by these atlases is that of meta-analyses relating cell types and states with biological conditions or demographics metadata3,4.
- These allow efficient learning across tasks and datasets and have been used for cell type classification26.
- Composite materials and structures are inherently inhomogeneous and anisotropic across multiple scales.
- In the micromechanics context, this approach is advantageous in that it provides an accurate study of the local fields.
- This term has the objective of pulling together cells belonging to the same cell type towards their correspondent prototype in latent space.
Access Paper:
This makes it difficult to know whether the analysis predicts the correct answer for the right reasons. To address this limitation, there are numerous opportunities to combine machine learning and multiscale modeling towards a priori satisfying the fundamental laws of physics, and, at the same time, preventing overfitting of the data. Assuming we know the governing ordinary and partial differential equations, finite element models can predict the behavior of the system from given initial and boundary conditions measured at a few selected points. This approach is incredibly powerful, but requires that we actually know the physics of the system, for example through the underlying kinematic equations, the balance of mass, momentum, or energy.
Materials availability
We tried to fix as many hyperparameters as possible to keep the computational overhead within a reasonable limit. We selected the set of hyperparameters that yielded the best integration performance and then used these to obtain results for the benchmarks displayed in Fig. A table with the grid of values considered during our hyperparameter search is available at Supplementary Table 1. ScPoli’s training objective includes a supervised term we call prototype loss. This term has the objective of pulling together cells belonging to the same cell type towards their correspondent prototype in latent space. The radial stiffness distribution of the all finger beams in the circumference.
Electronic supplementary material
(a) Original image consisting of squares of different sizes; (b) pattern spectrum using structural opening; (c) pattern spectrum using opening-by-reconstruction, by λ × λ squares. Now, provided we are able to project a signal f onto the appropriate spline space Vjp,f→Sjpf and to decompose the spline pSj (f), in accordance with (4.1.1) we get an opportunity to process the signal in several frequency channels simultaneously. If need be, the channels obtain band-widths arranged according to the logarithmic scale which can be subdivided into more narrow multi-scale analysis channels by means of the so-called wavelet packets. Image enhancement is the desired improvement of image quality (Gonzalez & Woods, 1992), (e.g., for visual inspection).
