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Comparison of Two Different (Quasi-) Dynamic Testing Methods for the Performance Evaluation of a Linear Fresnel Process Heat Collector

AbstractA small-scale Linear Fresnel Collector (LFC) for the generation of process heat has been tested by Fraunhofer ISE;its performance was evaluated by means of two different methods. The first is a quasi-dynamic testing method performed according to the testing standard ISO 9806:2013, with modificationsin the model to accurately describe LFCs. Due to the two-dimensional Incidence Angle Modifier (IAM) of an LFC, an iterative multi-linear regression (MLR)approach has been developed to be able to comprehensively evaluate the optical performance. The second method is a dynamic testing method based on a parameter identification incorporating a multi-node/plug-flow collector model without strict restraints on mass flow and inlet temperature stability.Both methods are briefly described in their conceptual design and theirbasic requirements, revealing their similarities and differences. Each method is then applied to real measurement datafrom an LFC, assessing practicability and identification accuracy. For both methods, the mean absolutedifferencebetween identifiedIAM values and results from ray tracing fell in a range of 0.013-0.017, leading toa similar accuracy inLFC performance evaluation. Differences in optical efficiency between the two methodsare smaller,with anaverage absolutedifferencebelow 0.0098, even whenusing different measurement data and simulation models. Thus the dynamic methodrepresents a good starting point for the further development of an alternative dynamic testing and evaluation method with more flexibility than the current testing standard. This will be significantwhenevaluating large-scale concentrating collectors and collectors with direct steam generation.
- Technische Universität Braunschweig Germany
- Fraunhofer Society Germany
- Fraunhofer Institute for Solar Energy Systems Germany
multi-linear regression, Linear Fresnel collector, performance evaluation, Energy(all), process heat collector, parameter indentification method, least-square algorithm
multi-linear regression, Linear Fresnel collector, performance evaluation, Energy(all), process heat collector, parameter indentification method, least-square algorithm
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