Research output per year
Research output per year
- 19 results
Search results
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2024
Combustion Behavior of Carbon-free Fuels for Large Engines
Klawitter, M., Gößnitzer, C., Pirker, G., Wimmer, A., Wüthrich, S. & Herrmann, K., 20 Mar 2024, EPHyC 2024 Book of Abstracts. Nabizada et al., A. (ed.). Ghent, Belgium: Eigenverlag EPHyC, p. 98-103 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Utilizing neural networks to supplant chemical kinetics tabulation through mass conservation and weighting of species depletion
Rohrhofer, F. M., Posch, S., Gößnitzer, C., García-Oliver, J. M. & Geiger, B., May 2024, In: Energy and AI. 16, 10 p., 100341.Research output: Contribution to journal › Article › peer-review
Open Access -
2023
A Comparison of Virtual Sensors for Combustion Parameter Prediction of Gas Engines Based on Knock Sensor Signals
Kefalas, A., Ofner, A., Posch, S., Pirker, G., Gößnitzer, C., Geiger, B. & Wimmer, A., 11 Apr 2023, In: SAE Technical Papers. 12 p., 2023-01-0434.Research output: Contribution to journal › Conference article › peer-review
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Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes
Rohrhofer, F. M., Posch, S., Gößnitzer, C., García-Oliver, J. M. & Geiger, B., 12 May 2023, Scientific Computing 2023: Conference Proceedings. Granigg, W. (ed.). Graz: Verlag der FH JOANNEUM Gesellschaft mbH, p. 4 - 11 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Characterization of future fuels using an optically accessible rapid compression machine
Pirker, G., Klawitter, M., Ramachandran, A., Gößnitzer, C., Tilz, A. & Wimmer, A., 12 Jun 2023, CIMAC Congress 2023, Busan. 17 p. 362Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open AccessFile -
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
Rohrhofer, F. M., Posch, S., Gößnitzer, C. & Geiger, B. C., 7 Aug 2023, In: IEEE Access. 11, p. 86252-86261 10 p., 10210413.Research output: Contribution to journal › Article › peer-review
Open Access -
Finding the Optimum Design of Large Gas Engines Prechambers using CFD and Bayesian Optimization
Posch, S., Gößnitzer, C., Rohrhofer, F. M., Geiger, B. & Wimmer, A., 12 May 2023, Scientific Computing 2023: Conference Proceedings. Granigg, W. (ed.). Graz: Verlag der FH JOANNEUM Gesellschaft mbH, p. 160 - 168 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Green Tech Radar: Grünes Ammoniak: Schlüsselbaustein für die Dekarbonisierung von Energiewirtschaft, Schifffahrt, Landwirtschaft & Industrie
Klawitter, M., Pirker, G., Sauperl, I., Gößnitzer, C. & Simbürger, M., 23 Mar 2023, 8 p. Graz : Green Tech Valley Cluster GmbH.Research output: Other contribution › Brochure
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Numerical procedure for prechamber transfer from a single-cylinder engine to a rapid compression machine
Soni, R., Posch, S., Gößnitzer, C., Pirker, G. & Wimmer, A., Jul 2023, In: International Journal of Engine Research. 24, 7, p. 3130-3145 16 p.Research output: Contribution to journal › Article › peer-review
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On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks
Rohrhofer, F. M., Posch, S., Gößnitzer, C. & Geiger, B., 23 Jan 2023, In: Transactions on Machine Learning Research. 2023, 1, 22 p., 490.Research output: Contribution to journal › Article › peer-review
Open Access -
Partially Dissociated Ammonia as a Fuel: Experimental Study on Laminar Flame Characteristics of Premixed NH3/H2/N2/Air Mixtures
Klawitter, M., Gößnitzer, C., Tilz, A., Pirker, G. & Wimmer, A., 10 Jul 2023. 3 p.Research output: Contribution to conference › Paper
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Training gives me PINNs and needles – on the complexity of training physics-informed neural networks
Rohrhofer, F. M., Steger, S., Posch, S., Gößnitzer, C. & Geiger, B., 12 Jul 2023. 2 p.Research output: Contribution to conference › Abstract
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Training gives me PINNs and needles – on the complexity of training physics-informed neural networks
Rohrhofer, F. M., Steger, S., Posch, S., Gößnitzer, C. & Geiger, B., 30 May 2023. 2 p.Research output: Contribution to conference › Abstract
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2022
Development of a Framework for Internal Combustion Engine Simulations in OpenFOAM
Gößnitzer, C., Posch, S. & Pirker, G., 24 Nov 2022. 8 p.Research output: Contribution to conference › Paper › peer-review
Open Access -
Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder
Posch, S., Gößnitzer, C., Ofner, A., Pirker, G. & Wimmer, A., 1 Apr 2022, In: Energies. 15, 7, 16 p., 2325.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
2021
A New Method to Determine the Impact of Individual Field Quantities on Cycle-to-Cycle Variations in a Spark-Ignited Gas Engine
Gößnitzer, C. & Givler, S., 2 Jul 2021, In: Energies. 14, 14, 14 p., 4136.Research output: Contribution to journal › Article › peer-review
Open Access -
Numerical Investigation of Turbulence Anisotropy of In-Cylinder Flows with Multi-Cycle Large Eddy Simulation
Soni, R., Gößnitzer, C., Pirker, G. & Wimmer, A., 6 Apr 2021, In: SAE Technical Papers. 2021, 12 p., 2021-01-0416.Research output: Contribution to journal › Conference article › peer-review
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2020
Visualization of Turbulence Anisotropy in the In-cylinder Flow of Internal Combustion Engines
Soni, R., Gößnitzer, C., Pirker, G. & Wimmer, A., 14 Apr 2020, In: SAE Technical Papers. 2020-April, April, 9 p.Research output: Contribution to journal › Conference article › peer-review
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2018
Exact flame-front tracking in premixed low Mach number combustion
Gößnitzer, C. & Steinrück, H., 11 Jun 2018. 12 p.Research output: Contribution to conference › Paper › peer-review