Sensor placement in nuclear reactors based on the generalized empirical interpolation method

Abstract

In this paper, we apply the so-called generalized empirical interpolation method (GEIM) to address the problem of sensor placement in nuclear reactors. This task is challenging due to the accumulation of a number of difficulties like the complexity of the underlying physics and the constraints in the admissible sensor locations and their number. As a result, the placement, still today, strongly relies on the know-how and experience of engineers from different areas of expertise. The present methodology contributes to making this process become more systematic and, in turn, simplify and accelerate the procedure.

Design and Software Implementation of a Navigation Accuracy Evaluation Based on Error Model Solution

“…Considering that the test data are small sample unequal interval data, the sample size was expanded at proper intervals through interpolation. The common interpolation methods include linear interpolation, Lagrange interpolation, spline interpolation [14,15]. In actual engineering, Lagrange twopoint interpolation, Lagrange three-point quadratic interpolation (parabolic interpolation) and least squares approximation should be selected if the number of measuring points and test accuracy need to be considered [16].…”

Sensor Placement for Field Reconstruction in Rotating Electrical Machines

“…Some methods are based on the derivation of the sensor location from Partial Derivative Equations (PDE) model solved analytically [1] or numerically like the (Discrete) Empirical Interpolation Method ((D)EIM) [2,3]. This last method has been applied in the case of nuclear reactor [4]. Moreover, in the field of computational electromagnetics, the (D)EIM has been already applied with success to reconstruct the distribution of the electromagnetic field in the regions where the behavior law is nonlinear, when constructing a reduced order model from a Finite Element (FE) model [5][6].…”

Reducing sensors for transient heat transfer problems by means of variational data assimilation

“…It has been used for several applications. For instance, [38] applies the PBDW for structural health monitoring; [19] proposes a non-intrusive PBDW with application to urban dispersion modeling frameworks; and [1,2] exploit the generalized empirical interpolation method [27,28,29] in a data interpolation perspective. As a further step towards efficient industrial implementation, [31] develops a PBDW approach based on noisy observations and [32] introduces an adaptive PBDW approach with a user-defined update space.…”