Fatigue analysis-based numerical design of stamping tools made of cast iron

This work concerns stress and fatigue analysis of stamping tools made of cast iron with an essentially pearlitic matrix and containing foundry defects. Our approach consists at first, in coupling the stamping numerical processing simulations and structure analysis in order to improve the tool stiffness geometry for minimizing the stress state and optimizing their fatigue lifetime. The method consists in simulating the stamping process by considering the tool as a perfect rigid body. The estimated contact pressure is then used as boundary condition for FEM structure loading analysis of the tool. The result of this analysis is compared with the critical stress limit depending on the automotive model. The acceptance of this test allows calculating the fatigue lifetime of the critical zone by using the S–N curve of corresponding load ratio. If the prescribed tool life requirements are not satisfied, then the critical region of the tool is redesigned and the whole simulation procedures are reactivated. This method is applied for a cast iron EN-GJS-600-3. The stress-failure (S–N) curves for this material is determined at room temperature under push pull loading with different load ratios R0σmin/σmax0−2, R0−1 and R00.1. The effects of the foundry defects are determined by SEM observations of crack initiation sites. Their presence in tested specimens is associated with a reduction of fatigue lifetime by a factor of 2. However, the effect of the load ratio is more important

Geometric observation for the Bures fidelity between two states of a qubit

In this Brief Report, we present a geometric observation for the Bures fidelity between two states of a qubit.Comment: 4 pages, 1 figure, RevTex, Accepted by Phys. Rev.

Kneadings, Symbolic Dynamics and Painting Lorenz Chaos. A Tutorial

A new computational technique based on the symbolic description utilizing kneading invariants is proposed and verified for explorations of dynamical and parametric chaos in a few exemplary systems with the Lorenz attractor. The technique allows for uncovering the stunning complexity and universality of bi-parametric structures and detect their organizing centers - codimension-two T-points and separating saddles in the kneading-based scans of the iconic Lorenz equation from hydrodynamics, a normal model from mathematics, and a laser model from nonlinear optics.Comment: Journal of Bifurcations and Chaos, 201

Lurbinectedin in Refractory Diffuse Malignant Peritoneal Mesothelioma: Report of Two Cases.

Mesothelioma is a malignancy of serosal membranes. Parietal pleura is the most common site, with peritoneum being the second most frequent location. Malignant peritoneal mesothelioma (MPM) is a rare and aggressive disease. The prognosis is often very poor with median overall survival ranging from 6 to 18 months in patients who are not candidates for cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) due to non-resectable disease or comorbid conditions. For patients with resectable disease, CRS and HIPEC have become the standard of care. However, for patients with unresectable malignant mesothelioma there is unfortunately no effective systemic treatment beyond the first line. Based on the results of a recent phase II trial, lurbinectedin has clinical activity and acceptable toxicity in the second- and third-line treatment of malignant pleural mesothelioma. However, until present, no data have been available for patients with MPM and for patients who become refractory after multiple treatment lines. We report on two patients with metastatic MPM who achieved durable disease control of 10+ and 8 months with lurbinectedin in the fourth and fifth treatment line, respectively

Physics-Informed Neural Nets for Control of Dynamical Systems

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs neither can they simulate for long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to \emph{control} problems and able to simulate for longer-range time horizons that are not fixed beforehand. The framework has new inputs to account for the initial state of the system and the control action. In PINC, the response over the complete time horizon is split such that each smaller interval constitutes a solution of the ODE conditioned on the fixed values of initial state and control action for that interval. The whole response is formed by feeding back the predictions of the terminal state as the initial state for the next interval. This proposal enables the optimal control of dynamic systems, integrating a priori knowledge from experts and data collected from plants into control applications. We showcase our proposal in the control of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank system