Search

Motivated by some landslide models, and related estimation challenges, this paper presents an optimal estimation method for state and parameter in a special class of so-called ODE–PDE system based on the adjoint method for discrete-time asynchronous measurements. This system is described by a pair of coupled Ordinary Differential Equation (ODE) and Partial Differential Equation (PDE), with a mixed boundary condition for the PDE. The coupling appears both in the ODE and in the Neuman boundary condition of the PDE. For this system, initial conditions or state variables and some empirical parameters are assumed to be unknown and need to be estimated. The Lagrangian multiplier method is used to connect the dynamics of the system and the cost function defined as the least square error between the simulation values and the available measurements. The adjoint state method is applied to the objective functional to get the adjoint system and the gradients with respect to parameters and initial state. The cost functional is optimised, employing the steepest descent method to estimate parameters and initial state. This general approach is illustrated by two application examples corresponding to two different landslide models that validate the presented optimal estimation approach. The first one is about state and parameter estimation in an extended sliding-consolidation landslide model, and the second one is in the viscoplastic sliding-consolidation landslide model. [ABSTRACT FROM AUTHOR]

When studying a mechanical system involving contact between two bodies such as a disc and brake pad system, finite element simulations are often used to predict the phenomena involved. However, due to model size and calculation time problems, when modeling this type of mechanical system on a scale of about 100 mm, it is difficult to model as well a layer (for example a third body layer) on a scale of approximately 10 [micro]m. In quasi-static problems it is possible to simulate the contact between an elastic body and a thin elastic layer bonded to a rigid surface, by considering the contact between this elastic body and a rigid surface with a specific contact law. This paper shows that it is possible to implement this specific contact law in a dynamic finite element code to simulate thin layers undergoing quasi-static and dynamic problems without physical contact instabilities. This specific contact law saves a large amount of calculation time. Once the specific contact law has been validated, the influence of the layer thickness is studied.

This work shows that passive radio-frequency identification (RFID) tags can be used as low-cost contactless sensors, to measure the variations in snow water equivalent (SWE) of a snowpack. RFID tags are produced massively to remotely identify industrial goods, hence are available commercially off-the-shelf at very low-cost. The introduced measurement system consists of a vertical profile of RFID tags installed before the first snowfall, interrogated continuously by a 865–868 MHz reader that remains above the snowpack. The system deduces the SWE variations from the increase of phase delay induced by the new layers of fresh snow which slows the propagation of the waves. The method is tested both in a controlled laboratory environment, and outdoors on the French national reference center of Col de Porte, to cross-check the results against a solid reference dataset (cosmic rays, precipitation weighting, temperature monitoring, and snow pit surveys). The technical challenges solved concern multipathing interferences, snowmelt acceleration during reheats, measurement discontinuity, and wet snow influence. This non-contact and non-destructive RFID technique can estimate the SWE of dry snow, with the accuracy of ±3−30 kg/m2 depending on the number of tags and antennas. In addition, the system can monitor the snow temperature with 1 °C accuracy and spatialization, using dedicated sensors embedded in the tags. [ABSTRACT FROM AUTHOR]