7 Works

Deep Neural Network Predicting the EMI Induced Offset of a Differential Amplifier Stage including Training and Test Data

Dominik Zupan & Daniel Kircher
Implementation of a neural network for predicting the EMI induced offset of a differential amplifier stage. This neural network is  implemented in the Pyhton library Tensorflow. This dataset also includes data, that can be used as training and test data for the developed network.

TRNSYS Type 840: Simulation model for PCM/water storage tanks (Version 3.0)

Christoph Moser, Andreas Heinz & Hermann Schranzhofer
The simulation model Type 840 was developed at the Institute of Thermal Engineering, Graz University of Technology, within the framework of the European Project PAMELA (2004), the IEA SHC TASK 32 and a national research project (2006). The model enables a detailed simulation of water tanks with integrated PCM modules of different geometries (cylinders, spheres and plates) or tanks filled with a PCM slurry. An update was made by Christoph Moser in the European project...

Deep Neural Network Framework Predicting the EMI Induced Offset of a Differential Amplifier Stage including Training and Test Data

Dominik Zupan & Daniel Kircher
Implementation of a neural network for predicting the EMI induced offset of a differential amplifier stage. This neural network is  implemented in the Pyhton library Tensorflow. This dataset also includes data, that can be used as training and test data for the developed network.

Deep Neural Network Framework Predicting the EMI Induced Offset of a Differential Amplifier Stage including Training and Test Data

Dominik Zupan & Daniel Kircher
Implementation of a neural network for predicting the EMI induced offset of a differential amplifier stage. This neural network is  implemented in the Pyhton library Tensorflow. This dataset also includes data, that can be used as training and test data for the developed network.

Deep Neural Network Framework Predicting the EMI Induced Offset of a Differential Amplifier Stage including Training and Test Data

Dominik Zupan & Daniel Kircher
Implementation of a neural network for predicting the EMI induced offset of a differential amplifier stage. This neural network is  implemented in the Pyhton library Tensorflow. This dataset also includes data, that can be used as training and test data for the developed network.

TRNSYS Type 842: Model for the transient simulation of bulk PCM tanks with immersed fin and tube heat exchanger (Version 2.0)

Andreas Heinz
The simulation model Type 842 was developed at the Institute of Thermal Engineering, Graz University of Technology, within a national research project (2006). The model enables the detailed simulation of PCM (phase change material) tanks with an immersed fin and tube heat exchanger.    The storage is assumed to be of rectangular shape and charged/discharged via an integrated fin and tube heat exchanger. Heat transfer (conduction) within the PCM is only considered in the direction...

TRNSYS Type 840: Simulation model for PCM/water storage tanks (Version 2.0)

Christoph Moser, Andreas Heinz & Hermann Schranzhofer
The simulation model Type 840 was developed at the Institute of Thermal Engineering, Graz University of Technology, within the framework of the European Project PAMELA (2004), the IEA SHC TASK 32 and a national research project (2006). The model enables a detailed simulation of water tanks with integrated PCM modules of different geometries (cylinders, spheres and plates) or tanks filled with a PCM slurry. An update was made by Christoph Moser in the European project...

Registration Year

  • 2022
    7

Resource Types

  • Software
    7

Affiliations

  • Graz University of Technology
    7