Conference Proceeding
Refine
Year of publication
Document Type
- Conference Proceeding (1016) (remove)
Language
- English (1016) (remove)
Has Fulltext
- no (1016) (remove)
Keywords
- Enterprise Architecture (5)
- Energy storage (4)
- Gamification (4)
- Natural language processing (4)
- Power plants (4)
- hydrogen (4)
- solar sail (4)
- Associated liquids (3)
- Concentrated solar power (3)
- Hybrid energy system (3)
- MASCOT (3)
- Out-of-plane load (3)
- earthquakes (3)
- Additive manufacturing (2)
- Adjacent buildings (2)
- Case Study (2)
- Clustering (2)
- Deep learning (2)
- Digital Twin (2)
- Diversity (2)
Institute
- Fachbereich Elektrotechnik und Informationstechnik (224)
- Fachbereich Luft- und Raumfahrttechnik (171)
- Fachbereich Energietechnik (158)
- Fachbereich Medizintechnik und Technomathematik (131)
- IfB - Institut für Bioengineering (109)
- Solar-Institut Jülich (108)
- Fachbereich Maschinenbau und Mechatronik (98)
- Fachbereich Bauingenieurwesen (70)
- ECSM European Center for Sustainable Mobility (50)
- Fachbereich Wirtschaftswissenschaften (42)
- MASKOR Institut für Mobile Autonome Systeme und Kognitive Robotik (42)
- INB - Institut für Nano- und Biotechnologien (33)
- Fachbereich Chemie und Biotechnologie (23)
- Kommission für Forschung und Entwicklung (16)
- Nowum-Energy (11)
- Fachbereich Architektur (7)
- Fachbereich Gestaltung (3)
- Institut fuer Angewandte Polymerchemie (2)
- ZHQ - Bereich Hochschuldidaktik und Evaluation (2)
- Arbeitsstelle fuer Hochschuldidaktik und Studienberatung (1)
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Effectiveness of the edge-based smoothed finite element method applied to soft biological tissues
(2012)
Flexible Fuel Operation of a Dry-Low-Nox Micromix Combustor with Variable Hydrogen Methane Mixtures
(2019)
The Dry-Low-NOx (DLN) Micromix combustion technology has been developed originally as a low emission alternative for industrial gas turbine combustors fueled with hydrogen. Currently the ongoing research process targets flexible fuel operation with hydrogen and syngas fuel.
The non-premixed combustion process features jet-in-crossflow-mixing of fuel and oxidizer and combustion through multiple miniaturized flames. The miniaturization of the flames leads to a significant reduction of NOx emissions due to the very short residence time of reactants in the flame.
The paper presents the results of a numerical and experimental combustor test campaign. It is conducted as part of an integration study for a dual-fuel (H2 and H2/CO 90/10 Vol.%) Micromix combustion chamber prototype for application under full scale, pressurized gas turbine conditions in the auxiliary power unit Honeywell Garrett GTCP 36-300.
In the presented experimental studies, the integration-optimized dual-fuel Micromix combustor geometry is tested at atmospheric pressure over a range of gas turbine operating conditions with hydrogen and syngas fuel. The experimental investigations are supported by numerical combustion and flow simulations. For validation, the results of experimental exhaust gas analyses are applied.
Despite the significantly differing fuel characteristics between pure hydrogen and hydrogen-rich syngas the evaluated dual-fuel Micromix prototype shows a significant low NOx performance and high combustion efficiency. The combustor features an increased energy density that benefits manufacturing complexity and costs.