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47 results on '"Neumayer, Markus"'

25. Enhanced Diagnosis for Small Engines

Author

Basso, Riccardo, primary, Schacht, Hans-Jürgen, additional, Stephan, Schmidt, additional, Kirchberger, Roland, additional, Rath, Matthias, additional, Neumayer, Markus, additional, and Reisenberger, Christian, additional

Published
2017
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46. Prior design for tomographic volume fraction estimation in pneumatic conveying systems from capacitive data.

Author

Tan, Chao, Suppan, Thomas, Neumayer, Markus, Bretterklieber, Thomas, and Puttinger, Stefan

Subjects
*PNEUMATIC-tube transportation, *PNEUMATICS, *ELECTRICAL capacitance tomography, *IMAGE reconstruction algorithms, *BULK solids, *STATISTICS, *ZETA potential
Abstract

Pneumatic conveying systems have become a standard technique for the transport of bulk materials such as powdery or granulates. The spatial dependence of the material density and the stream velocity in such transport systems require a volumetric measurement principle for flow measurement. In this paper we analyse the capability to estimate the volume fraction from capacitive sensing data using electrical capacitance tomography (ECT). In particular, we investigate the capability of back-projection type imaging algorithms. The ill-posed nature of the imaging problem of ECT require the incorporation of prior knowledge in the design of the estimator. We analyse the different flow profiles in pneumatic conveying in order to generate specific sample-based prior information to improve the estimation performance and robustness. We discuss the construction of different linear image reconstruction algorithms and present a framework, which allows a detailed statistical analysis of the estimator performance. Simulation studies show the estimation behaviour of different algorithms with respect to the incorporated prior information. We demonstrate, that the incorporation of specific prior knowledge leads to an improved estimator behaviour; for example, reduced variance and unbiased estimates. We implemented laboratory experiments in order to analyse the presented approach for the application in real pneumatic conveying processes. We demonstrate the improved robust estimation behaviour by means of comparative reconstruction results obtained with different algorithms and priors. Furthermore, the uncertainty of the estimated volume fraction is analysed in steady state conveying processes. Hereby, it is demonstrated, that appropriate prior information improves the estimation performance also for measurements coming from real pneumatic conveying processes, making ECT a suitable tool for the volume fraction estimation in such transport systems. [ABSTRACT FROM AUTHOR]

Published
2020
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47. Accelerated Bayesian Inference for the Estimation of Spatially Varying Heat Flux in a Heat Conduction Problem.

Author

Orlande, HelcioR. B., Dulikravich, GeorgeS., Neumayer, Markus, Watzenig, Daniel, and Colaço, MarceloJ.

Subjects
*BAYESIAN analysis, *HEAT flux, *HEAT conduction, *MONTE Carlo method, *GAUSSIAN processes, *ERROR analysis in mathematics, *HEATING
Abstract

This article aims at the acceleration of an inverse heat transfer problem solution within the Bayesian framework. The physical problem involves a spatially varying heat flux, which can reach very large magnitudes in small regions, such as in the heating imposed by high-power lasers. The inverse problem of estimating the imposed heat flux is solved by using the Markov chain Monte Carlo method, with simulated transient temperature measurements. The solution of the inverse problem is based on a reduced model, which consists of an improved lumped formulation of a linearized version of the original nonlinear problem. Two different priors are considered for the sought heat flux, including a total variation density and a Gaussian density. The Gaussian prior is based on the physics of the heat conduction problem. Parameters appearing in both priors are also estimated as part of the inference problem in hyperprior models. The Delayed Acceptance Metropolis-Hastings (DAMH) Algorithm and the Enhanced Approximation Error Model (AEM) are applied with the objective to improve the accuracy of the inverse problem solution. [ABSTRACT FROM AUTHOR]

Published
2014
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