@inproceedings{SchwagerAngeleSchwarzboezletal.2023, author = {Schwager, Christian and Angele, Florian and Schwarzb{\"o}zl, Peter and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf}, title = {Model predictive assistance for operational decision making in molten salt receiver systems}, series = {SolarPACES: Solar Power \& Chemical Energy Systems}, booktitle = {SolarPACES: Solar Power \& Chemical Energy Systems}, number = {2815 / 1}, publisher = {AIP conference proceedings / American Institute of Physics}, address = {Melville, NY}, isbn = {978-0-7354-4623-6}, issn = {1551-7616 (online)}, doi = {10.1063/5.0151514}, pages = {8 Seiten}, year = {2023}, abstract = {Despite the challenges of pioneering molten salt towers (MST), it remains the leading technology in central receiver power plants today, thanks to cost effective storage integration and high cost reduction potential. The limited controllability in volatile solar conditions can cause significant losses, which are difficult to estimate without comprehensive modeling [1]. This paper presents a Methodology to generate predictions of the dynamic behavior of the receiver system as part of an operating assistance system (OAS). Based on this, it delivers proposals if and when to drain and refill the receiver during a cloudy period in order maximize the net yield and quantifies the amount of net electricity gained by this. After prior analysis with a detailed dynamic two-phase model of the entire receiver system, two different reduced modeling approaches where developed and implemented in the OAS. A tailored decision algorithm utilizes both models to deliver the desired predictions efficiently and with appropriate accuracy.}, language = {en} } @inproceedings{SchwagerAngeleNourietal.2022, author = {Schwager, Christian and Angele, Florian and Nouri, Bijan and Schwarzb{\"o}zl, Peter and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf}, title = {Impact of DNI forecast quality on performance prediction for a commercial scale solar tower: Application of nowcasting DNI maps to dynamic solar tower simulation}, series = {SolarPACES 2022 conference proceedings}, booktitle = {SolarPACES 2022 conference proceedings}, number = {1}, publisher = {TIB Open Publishing}, address = {Hannover}, issn = {2751-9899 (online)}, doi = {10.52825/solarpaces.v1i.675}, pages = {9 Seiten}, year = {2022}, abstract = {Concerning current efforts to improve operational efficiency and to lower overall costs of concentrating solar power (CSP) plants with prediction-based algorithms, this study investigates the quality and uncertainty of nowcasting data regarding the implications for process predictions. DNI (direct normal irradiation) maps from an all-sky imager-based nowcasting system are applied to a dynamic prediction model coupled with ray tracing. The results underline the need for high-resolution DNI maps in order to predict net yield and receiver outlet temperature realistically. Furthermore, based on a statistical uncertainty analysis, a correlation is developed, which allows for predicting the uncertainty of the net power prediction based on the corresponding DNI forecast uncertainty. However, the study reveals significant prediction errors and the demand for further improvement in the accuracy at which local shadings are forecasted.}, language = {en} } @inproceedings{SchulteSchwagerFrantzetal.2022, author = {Schulte, Jonas and Schwager, Christian and Frantz, Cathy and Schloms, Felix and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf}, title = {Control concept for a molten salt receiver in star design: Development, optimization and testing with cloud passage scenarios}, series = {SolarPACES 2022 conference proceedings}, booktitle = {SolarPACES 2022 conference proceedings}, number = {1}, publisher = {TIB Open Publishing}, address = {Hannover}, issn = {2751-9899 (online)}, doi = {10.52825/solarpaces.v1i.693}, pages = {9 Seiten}, year = {2022}, abstract = {A promising approach to reduce the system costs of molten salt solar receivers is to enable the irradiation of the absorber tubes on both sides. The star design is an innovative receiver design, pursuing this approach. The unconventional design leads to new challenges in controlling the system. This paper presents a control concept for a molten salt receiver system in star design. The control parameters are optimized in a defined test cycle by minimizing a cost function. The control concept is tested in realistic cloud passage scenarios based on real weather data. During these tests, the control system showed no sign of unstable behavior, but to perform sufficiently in every scenario further research and development like integrating Model Predictive Controls (MPCs) need to be done. The presented concept is a starting point to do so.}, language = {en} } @inproceedings{LahrsKrisamHerrmann2023, author = {Lahrs, Lennart and Krisam, Pierre and Herrmann, Ulf}, title = {Envisioning a collaborative energy system planning platform for the energy transition at the district level}, series = {ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems}, booktitle = {ECOS 2023. The 36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems}, publisher = {Procedings of ECOS 2023}, doi = {10.52202/069564-0284}, pages = {3163 -- 3170}, year = {2023}, abstract = {Residential and commercial buildings account for more than one-third of global energy-related greenhouse gas emissions. Integrated multi-energy systems at the district level are a promising way to reduce greenhouse gas emissions by exploiting economies of scale and synergies between energy sources. Planning district energy systems comes with many challenges in an ever-changing environment. Computational modelling established itself as the state-of-the-art method for district energy system planning. Unfortunately, it is still cumbersome to combine standalone models to generate insights that surpass their original purpose. Ideally, planning processes could be solved by using modular tools that easily incorporate the variety of competing and complementing computational models. Our contribution is a vision for a collaborative development and application platform for multi-energy system planning tools at the district level. We present challenges of district energy system planning identified in the literature and evaluate whether this platform can help to overcome these challenges. Further, we propose a toolkit that represents the core technical elements of the platform. Lastly, we discuss community management and its relevance for the success of projects with collaboration and knowledge sharing at their core.}, language = {en} } @inproceedings{SchulzeMuehleisenFeyerl2018, author = {Schulze, Sven and M{\"u}hleisen, M. and Feyerl, G{\"u}nter}, title = {Adaptive energy management strategy for a heavy-duty truck with a P2-hybrid topology}, series = {18. Internationales Stuttgarter Symposium. Proceedings}, booktitle = {18. Internationales Stuttgarter Symposium. Proceedings}, publisher = {Springer Vieweg}, address = {Wiesbaden}, doi = {10.1007/978-3-658-21194-3}, pages = {75 -- 89}, year = {2018}, language = {en} } @inproceedings{MerkensHebel2021, author = {Merkens, Torsten and Hebel, Christoph}, title = {Sharing mobility concepts - flexible, sustainable, smart}, series = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG) International Conference}, booktitle = {Proceedings of the 1st UNITED - Southeast Asia Automotive Interest Group (SAIG) International Conference}, isbn = {978-3-902103-94-9}, pages = {43 -- 44}, year = {2021}, language = {en} } @inproceedings{WuKemper2016, author = {Wu, Ziyi and Kemper, Hans}, title = {The optimal 48 V - battery pack for a specific load profile of a heavy duty vehicle}, series = {8. Internationale Fachtagung Kraftwerk Batterie : 26. - 27. April 2016, M{\"u}nster, Deutschland}, booktitle = {8. Internationale Fachtagung Kraftwerk Batterie : 26. - 27. April 2016, M{\"u}nster, Deutschland}, year = {2016}, language = {en} } @inproceedings{NeuJanserKhatibietal.2016, author = {Neu, Eugen and Janser, Frank and Khatibi, Akbar A. and Orifici, Adrian C.}, title = {In-flight vibration-based structural health monitoring of aircraft wings}, series = {30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea}, booktitle = {30th Congress of the internatonal council of the aeronautical sciences : 25.-30. September 2016, Daejeon, Korea}, pages = {10 Seiten}, year = {2016}, abstract = {This work presents a methodology for automated damage-sensitive feature extraction and anomaly detection under multivariate operational variability for in-flight assessment of wings. The method uses a passive excitation approach, i. e. without the need for artificial actuation. The modal system properties (natural frequencies and damping ratios) are used as damage-sensitive features. Special emphasis is placed on the use of Fiber Bragg Grating (FBG) sensing technology and the consideration of Operational and Environmental Variability (OEV). Measurements from a wind tunnel investigation with a composite cantilever equipped with FBG and piezoelectric sensors are used to successfully detect an impact damage. In addition, the feasibility of damage localisation and severity estimation is evaluated based on the coupling found between damageand OEV-induced feature changes.}, language = {en} } @inproceedings{DinghoferHartung2020, author = {Dinghofer, Kai and Hartung, Frank}, title = {Analysis of Criteria for the Selection of Machine Learning Frameworks}, series = {2020 International Conference on Computing, Networking and Communications (ICNC)}, booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/ICNC47757.2020.9049650}, pages = {373 -- 377}, year = {2020}, abstract = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.}, language = {en} } @inproceedings{KreyerMuellerEsch2020, author = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas}, title = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude}, publisher = {DGLR}, address = {Bonn}, doi = {10.25967/490162}, pages = {13 Seiten}, year = {2020}, abstract = {In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented.}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.}, language = {en} } @inproceedings{ChristianMontagSchubaetal.2018, author = {Christian, Esser and Montag, Tim and Schuba, Marko and Allhof, Manuel}, title = {Future critical infrastructure and security - cyberattacks on charging stations}, series = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)}, booktitle = {31st International Electric Vehicle Symposium \& Exhibition and International Electric Vehicle Technology Conference (EVS31 \& EVTeC 2018)}, publisher = {Society of Automotive Engineers of Japan (JSAE)}, address = {Tokyo}, isbn = {978-1-5108-9157-9}, pages = {665 -- 671}, year = {2018}, language = {en} } @inproceedings{GaldiHartungDugelay2017, author = {Galdi, Chiara and Hartung, Frank and Dugelay, Jean-Luc}, title = {Videos versus still images: Asymmetric sensor pattern noise comparison on mobile phones}, series = {Electronic Imaging}, booktitle = {Electronic Imaging}, publisher = {Society for Imaging Science and Technology}, address = {Springfield, Virginia}, issn = {2470-1173}, doi = {10.2352/ISSN.2470-1173.2017.7.MWSF-331}, pages = {100 -- 103}, year = {2017}, abstract = {Nowadays, the most employed devices for recoding videos or capturing images are undoubtedly the smartphones. Our work investigates the application of source camera identification on mobile phones. We present a dataset entirely collected by mobile phones. The dataset contains both still images and videos collected by 67 different smartphones. Part of the images consists in photos of uniform backgrounds, especially collected for the computation of the RSPN. Identifying the source camera given a video is particularly challenging due to the strong video compression. The experiments reported in this paper, show the large variation in performance when testing an highly accurate technique on still images and videos.}, language = {en} } @inproceedings{HauggKreyerKemperetal.2020, author = {Haugg, Albert Thomas and Kreyer, J{\"o}rg and Kemper, Hans and Hatesuer, Katerina and Esch, Thomas}, title = {Heat exchanger for ORC. adaptability and optimisation potentials}, series = {IIR International Rankine 2020 Conference}, booktitle = {IIR International Rankine 2020 Conference}, doi = {10.18462/iir.rankine.2020.1224}, pages = {10 Seiten}, year = {2020}, abstract = {The recovery of waste heat requires heat exchangers to extract it from a liquid or gaseous medium into another working medium, a refrigerant. In Organic Rankine Cycles (ORC) on Combustion Engines there are two major heat sources, the exhaust gas and the water/glycol fluid from the engine's cooling circuit. A heat exchanger design must be adapted to the different requirements and conditions resulting from the heat sources, fluids, system configurations, geometric restrictions, and etcetera. The Stacked Shell Cooler (SSC) is a new and very specific design of a plate heat exchanger, created by AKG, which allows with a maximum degree of freedom the optimization of heat exchange rate and the reduction of the related pressure drop. This optimization in heat exchanger design for ORC systems is even more important, because it reduces the energy consumption of the system and therefore maximizes the increase in overall efficiency of the engine.}, language = {en} } @inproceedings{ThomaStiemerBraunetal.2023, author = {Thoma, Andreas and Stiemer, Luc and Braun, Carsten and Fisher, Alex and Gardi, Alessandro G.}, title = {Potential of hybrid neural network local path planner for small UAV in urban environments}, series = {AIAA SCITECH 2023 Forum}, booktitle = {AIAA SCITECH 2023 Forum}, publisher = {AIAA}, address = {Reston, Va.}, doi = {10.2514/6.2023-2359}, pages = {13 Seiten}, year = {2023}, abstract = {This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17\%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.}, language = {en} } @inproceedings{StarkRiepingEsch2023, author = {Stark, Ralf and Rieping, Carla and Esch, Thomas}, title = {The impact of guide tubes on flow separation in rocket nozzles}, series = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS}, booktitle = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS}, pages = {8 Seiten}, year = {2023}, abstract = {Rocket engine test facilities and launch pads are typically equipped with a guide tube. Its purpose is to ensure the controlled and safe routing of the hot exhaust gases. In addition, the guide tube induces a suction that effects the nozzle flow, namely the flow separation during transient start-up and shut-down of the engine. A cold flow subscale nozzle in combination with a set of guide tubes was studied experimentally to determine the main influencing parameters.}, language = {en} } @inproceedings{StarkBartelDitscheetal.2023, author = {Stark, Ralf and Bartel, Sebastian and Ditsche, Florian and Esch, Thomas}, title = {Design study of a 30kN LOX/LCH4 aerospike rocket engine for lunar lander application}, series = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS}, booktitle = {Aerospace Europe Conference 2023 - 10th EUCASS - 9th CEAS}, pages = {9 Seiten}, year = {2023}, abstract = {Based on lunar lander concept EL3, various LOX/CH4 aerospike engines were studied. A distinction was made between single and cluster configurations as well as ideal and non-ideal contour concepts. It could be shown that non-ideal aerospike engines promise a significant payload gain.}, language = {en} } @inproceedings{WalterElsenMuelleretal.1999, author = {Walter, Peter and Elsen, Ingo and M{\"u}ller, Holger and Kraiss, Karl-Friedrich}, title = {3D object recognition with a specialized mixtures of experts architecture}, series = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings}, booktitle = {IJCNN'99. International Joint Conference on Neural Networks. Proceedings}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-5529-6}, issn = {1098-7576}, doi = {10.1109/IJCNN.1999.836243}, pages = {3563 -- 3568}, year = {1999}, abstract = {Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.}, language = {en} } @inproceedings{Elsen1998, author = {Elsen, Ingo}, title = {A pixel based approach to view based object recognition with self-organizing neural networks}, series = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, booktitle = {IECON'98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-4503-7}, doi = {10.1109/IECON.1998.724032}, pages = {2040 -- 2044}, year = {1998}, abstract = {This paper addresses the pixel based classification of three dimensional objects from arbitrary views. To perform this task a coding strategy, inspired by the biological model of human vision, for pixel data is described. The coding strategy ensures that the input data is invariant against shift, scale and rotation of the object in the input domain. The image data is used as input to a class of self organizing neural networks, the Kohonen-maps or self-organizing feature maps (SOFM). To verify this approach two test sets have been generated: the first set, consisting of artificially generated images, is used to examine the classification properties of the SOFMs; the second test set examines the clustering capabilities of the SOFM when real world image data is applied to the network after it has been preprocessed to be invariant against shift, scale and rotation. It is shown that the clustering capability of the SOFM is strongly dependant on the invariance coding of the images.}, language = {en} } @inproceedings{ElsenKraissKrumbiegel1997, author = {Elsen, Ingo and Kraiss, Karl-Friedrich and Krumbiegel, Dirk}, title = {Pixel based 3D object recognition with bidirectional associative memories}, series = {International Conference on Neural Networks 1997}, booktitle = {International Conference on Neural Networks 1997}, publisher = {IEEE}, address = {New York}, isbn = {0-7803-4122-8}, pages = {1679 -- 1684}, year = {1997}, abstract = {This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.}, language = {en} }