AErOmAt - Aerodynamische Energie-Optimierung durch Metamodell-gestützte Adaption von Strukturen

Forschungsprojekt im Überblick

Die effiziente Nutzung verfügbarer Energie ist eine der großen Herausforderungen unserer Zeit. Eine besondere Bedeutung kommt dabei der Analyse und Optimierung von Formen und Strukturen hinsichtlich ihrer aerodynamischen Eigenschaften zu. Wichtige Anwendungsgebiete sind die Entwicklung energieeffizienter Fahrzeuge und die Windenergietechnik.

Fachbereiche und Institute

Fachbereich Ingenieurwissenschaften und Kommunikation Fachbereich Informatik Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE)

Förderungsart

Öffentliche Forschung

Zeitraum

01.10.2016 to 30.09.2019

Projektleitung an der H-BRS

Dirk Reith

Projektbeschreibung

Die effiziente Nutzung verfügbarer Energie ist eine der großen Herausforderungen unserer Zeit. Eine besondere Bedeutung kommt dabei der Analyse und Optimierung von Formen und Strukturen hinsichtlich ihrer aerodynamischen Eigenschaften zu. Wichtige Anwendungsgebiete sind die Entwicklung energieeffizienter Fahrzeuge und die Windenergietechnik.

Rechnergestützte Ansätze durch Simulation und automatische Optimierung sind heute selbstverständlicher Teil der technischen Entwicklung und haben zu erheblichen Verbesserungen geführt. Die hohe Komplexität sowohl hinsichtlich
der Rechenzeit als auch bezüglich des Speicherbedarfs limitiert jedoch bislang solche Ansätze. Zusammen mit unseren Forschungs- und Entwicklungspartnern bauen wir auf bewährten Ansätzen der Datenreduktion und der Surrogatmodellierung auf, um dieses Problem signifikant zu verkleinern:

Durch Kombination mit indirekten Oberflächencodierungen werden neue Methoden entwickelt um einen erheblichen Teil der Simulationen einzusparen und so komplexere Anwendungsfragestellungen zu lösen.
Mit einer komponentenbasierten Architektur und der Weiterentwicklung zugrundeliegender Softwaremethoden wird vermieden, dass die Kombination unterschiedlicher Verfahren durch Implementierungsaspekte oder Qualitätseinbußen limitiert wird.

Dies unterstreicht die Einsatzmöglichkeiten dieser neuen Technologie und ermöglicht weitreichende Verbesserungen in diesen gesellschaftlich bedeutsamen Anwendungsgebieten.

Aerodynamic Design Exploration through Surrogate-Assisted
Illumination
(Best Student Paper -- Multidisciplinary Design Optimization)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. AIAA Aviation and Aeronautics Forum 2017

Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted
Illumination
(Best Paper -- Complex Systems)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. Genetic and Evolutionary Computation Conference 2017

screen_shot_2017-06-27_at_10.16.27_am.png (DE)

Hierarchical Surrogate Modeling for Illumination Algorithms
Alexander Hagg. Genetic and Evolutionary Computation Conference 2017

Projektveröffentlichungen

Ergebnisse

Aerodynamic Design Exploration through Surrogate-Assisted
Illumination
(Best Student Paper -- Multidisciplinary Design Optimization)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. AIAA Aviation and Aeronautics Forum 2017

Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted
Illumination
(Best Paper -- Complex Systems)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. Genetic and Evolutionary Computation Conference 2017

Hierarchical Surrogate Modeling for Illumination Algorithms
Alexander Hagg. Genetic and Evolutionary Computation Conference 2017

Projektveröffentlichungen

Publikationen

Aerodynamic Design Exploration through Surrogate-Assisted
Illumination
(Best Student Paper -- Multidisciplinary Design Optimization)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. AIAA Aviation and Aeronautics Forum 2017

Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted
Illumination
(Best Paper -- Complex Systems)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. Genetic and Evolutionary Computation Conference 2017

Hierarchical Surrogate Modeling for Illumination Algorithms
Alexander Hagg. Genetic and Evolutionary Computation Conference 2017

Projektveröffentlichungen

2021 | 2020 | 2019 | 2018 | 2017

2021

Alexander Hagg: Discovering the preference hypervolume: an interactive model for real world computational co-creativity. Dissertation, Leiden University, 2021
URL BibTeX | RIS

Alexander Hagg: Phenotypic Niching Using Quality Diversity Algorithms. In: Preuss, Epitropakis et al. (Eds.): Metaheuristics for Finding Multiple Solutions, Cham: Springer, 2021, S. 287-315
doi:10.1007/978-3-030-79553-5_12 BibTeX | RIS

2020

Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck: An Analysis of Phenotypic Diversity in Multi-solution Optimization. In: Filipič, Minisci et al. (Eds.): Bioinspired Optimization Methods and Their Applications. 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19-20, 2020, Proceedings, 2020, S. 43-55
doi:10.1007/978-3-030-63710-1_4 BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Discovering Representations for Black-box Optimization. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, July 8–12, 2020, Cancún, Mexico, 2020, S. 103-111
doi:10.1145/3377930.3390221 arXiv BibTeX | RIS

Alexander Asteroth, Adam Gaier, Alexander Hagg, Jakob Meng, Andreas Priesnitz, Lea Prochnau, Dirk Reith: AErOmAt Abschlussbericht. Hochschule Bonn-Rhein-Sieg / Institut für Technik, Ressourcenschonung und Energieeffizienz, Hochschule Bonn-Rhein-Sieg, 2020
PDF Download doi:10.18418/opus-4850 urn:nbn:de:hbz:1044-opus-48506 BibTeX | RIS

2019

Alexander Hagg, Martin Zaefferer, Jörg Stork, Adam Gaier: Prediction of neural network performance by phenotypic modeling. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 1576-1582
doi:10.1145/3319619.3326815 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Are quality diversity algorithms better at generating stepping stones than objective-based search?. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 115-116
doi:10.1145/3319619.3321897 BibTeX | RIS

Alexander Hagg, Alexander Asteroth, Thomas Bäck: Modeling User Selection in Quality Diversity. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 116-124
doi:10.1145/3321707.3321823 arXiv BibTeX | RIS

2018

Alexander Hagg, Alexander Asteroth, Thomas Bäck: Prototype Discovery Using Quality-Diversity. In: Auger, Fonseca et al. (Eds.): Parallel Problem Solving from Nature – PPSN XV (PPSN 2018). Lecture Notes in Computer Science, vol 11101, 2018, S. 500-511
doi:10.1007/978-3-319-99253-2_40 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-Efficient Design Exploration through Surrogate-Assisted Illumination. In: Evolutionary Computation, 26(3), 2018, S. 381-410
doi:10.1162/evco_a_00231 PMID arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-efficient Neuroevolution with Kernel-Based Surrogate Models. In: GECCO ’18: Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, Japan, 2018, S. 85-92 (Best Paper Award)
doi:10.1145/3205455.3205510 arXiv BibTeX | RIS

2017

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 99-106 (Best paper award in the complex systems category)
doi:10.1145/3071178.3071282 arXiv URL BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Aerodynamic Design Exploration through Surrogate-Assisted Illumination. In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum. 5-9 June 2017, Denver, CO, USA, 2017 (First price in the Multidisciplinary Analysis and Optimization (MDO) Student Paper Competition)
doi:10.2514/6.2017-3330 URL BibTeX | RIS

Alexander Hagg: Hierarchical Surrogate Modeling for Illumination Algorithms. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 1407-1410
doi:10.1145/3067695.3082495 arXiv BibTeX | RIS

Alexander Hagg, Maximilian Mensing, Alexander Asteroth: Evolving Parsimonious Networks by Mixing Activation Functions. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 425-432
doi:10.1145/3071178.3071275 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Feature Space Modeling Through Surrogate Illumination. Preprint, 2017 (Published in GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference, July 2017, Pages 99–106, https://doi.org/10.1145/3071178.3071282)
arXiv doi:10.48550/arXiv.1702.03713 BibTeX | RIS

Aerodynamic Design Exploration through Surrogate-Assisted
Illumination
(Best Student Paper -- Multidisciplinary Design Optimization)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. AIAA Aviation and Aeronautics Forum 2017

Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted
Illumination
(Best Paper -- Complex Systems)
Gaier, Adam, Alexander Asteroth, and Jean-Baptiste Mouret. Genetic and Evolutionary Computation Conference 2017

Hierarchical Surrogate Modeling for Illumination Algorithms
Alexander Hagg. Genetic and Evolutionary Computation Conference 2017

Projektveröffentlichungen

2021 | 2020 | 2019 | 2018 | 2017

2021

Alexander Hagg: Discovering the preference hypervolume: an interactive model for real world computational co-creativity. Dissertation, Leiden University, 2021
URL BibTeX | RIS

Alexander Hagg: Phenotypic Niching Using Quality Diversity Algorithms. In: Preuss, Epitropakis et al. (Eds.): Metaheuristics for Finding Multiple Solutions, Cham: Springer, 2021, S. 287-315
doi:10.1007/978-3-030-79553-5_12 BibTeX | RIS

2020

Alexander Hagg, Mike Preuss, Alexander Asteroth, Thomas Bäck: An Analysis of Phenotypic Diversity in Multi-solution Optimization. In: Filipič, Minisci et al. (Eds.): Bioinspired Optimization Methods and Their Applications. 9th International Conference, BIOMA 2020, Brussels, Belgium, November 19-20, 2020, Proceedings, 2020, S. 43-55
doi:10.1007/978-3-030-63710-1_4 BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Discovering Representations for Black-box Optimization. In: GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, July 8–12, 2020, Cancún, Mexico, 2020, S. 103-111
doi:10.1145/3377930.3390221 arXiv BibTeX | RIS

Alexander Asteroth, Adam Gaier, Alexander Hagg, Jakob Meng, Andreas Priesnitz, Lea Prochnau, Dirk Reith: AErOmAt Abschlussbericht. Hochschule Bonn-Rhein-Sieg / Institut für Technik, Ressourcenschonung und Energieeffizienz, Hochschule Bonn-Rhein-Sieg, 2020
PDF Download doi:10.18418/opus-4850 urn:nbn:de:hbz:1044-opus-48506 BibTeX | RIS

2019

Alexander Hagg, Martin Zaefferer, Jörg Stork, Adam Gaier: Prediction of neural network performance by phenotypic modeling. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 1576-1582
doi:10.1145/3319619.3326815 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Are quality diversity algorithms better at generating stepping stones than objective-based search?. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 115-116
doi:10.1145/3319619.3321897 BibTeX | RIS

Alexander Hagg, Alexander Asteroth, Thomas Bäck: Modeling User Selection in Quality Diversity. In: GECCO '19: Genetic and Evolutionary Computation Conference, Prague, Czech Republic, July 13-17, 2019, 2019, S. 116-124
doi:10.1145/3321707.3321823 arXiv BibTeX | RIS

2018

Alexander Hagg, Alexander Asteroth, Thomas Bäck: Prototype Discovery Using Quality-Diversity. In: Auger, Fonseca et al. (Eds.): Parallel Problem Solving from Nature – PPSN XV (PPSN 2018). Lecture Notes in Computer Science, vol 11101, 2018, S. 500-511
doi:10.1007/978-3-319-99253-2_40 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-Efficient Design Exploration through Surrogate-Assisted Illumination. In: Evolutionary Computation, 26(3), 2018, S. 381-410
doi:10.1162/evco_a_00231 PMID arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-efficient Neuroevolution with Kernel-Based Surrogate Models. In: GECCO ’18: Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, Japan, 2018, S. 85-92 (Best Paper Award)
doi:10.1145/3205455.3205510 arXiv BibTeX | RIS

2017

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Data-Efficient Exploration, Optimization, and Modeling of Diverse Designs through Surrogate-Assisted Illumination. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 99-106 (Best paper award in the complex systems category)
doi:10.1145/3071178.3071282 arXiv URL BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Aerodynamic Design Exploration through Surrogate-Assisted Illumination. In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA AVIATION Forum. 5-9 June 2017, Denver, CO, USA, 2017 (First price in the Multidisciplinary Analysis and Optimization (MDO) Student Paper Competition)
doi:10.2514/6.2017-3330 URL BibTeX | RIS

Alexander Hagg: Hierarchical Surrogate Modeling for Illumination Algorithms. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 1407-1410
doi:10.1145/3067695.3082495 arXiv BibTeX | RIS

Alexander Hagg, Maximilian Mensing, Alexander Asteroth: Evolving Parsimonious Networks by Mixing Activation Functions. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference. Berlin, Germany, July 15-19, 2017, 2017, S. 425-432
doi:10.1145/3071178.3071275 arXiv BibTeX | RIS

Adam Gaier, Alexander Asteroth, Jean-Baptiste Mouret: Feature Space Modeling Through Surrogate Illumination. Preprint, 2017 (Published in GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference, July 2017, Pages 99–106, https://doi.org/10.1145/3071178.3071282)
arXiv doi:10.48550/arXiv.1702.03713 BibTeX | RIS