TY - CHAP A1 - Borggrafe, Andreas A1 - Ohndorf, Andreas A1 - Dachwald, Bernd A1 - Seboldt, Wolfgang T1 - Analysis of interplanetary solar sail trajectories with attitude dynamics T2 - Dynamics and Control of Space Systems 2012 N2 - We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system. Y1 - 2012 SN - 978-0-87703-587-9 SP - 1553 EP - 1569 PB - Univelt Inc CY - San Diego ER - TY - CHAP A1 - Dachwald, Bernd T1 - Low-Thrust Mission Analysis and Global Trajectory Optimization Using Evolutionary Neurocontrol: New Results T2 - European Workshop on Space Mission Analysis ESA/ESOC, Darmstadt, Germany 10 { 12 Dec 2007 N2 - Interplanetary trajectories for low-thrust spacecraft are often characterized by multiple revolutions around the sun. Unfortunately, the convergence of traditional trajectory optimizers that are based on numerical optimal control methods depends strongly on an adequate initial guess for the control function (if a direct method is used) or for the starting values of the adjoint vector (if an indirect method is used). Especially when many revolutions around the sun are re- quired, trajectory optimization becomes a very difficult and time-consuming task that involves a lot of experience and expert knowledge in astrodynamics and optimal control theory, because an adequate initial guess is extremely hard to find. Evolutionary neurocontrol (ENC) was proposed as a smart method for low-thrust trajectory optimization that fuses artificial neural networks and evolutionary algorithms to so-called evolutionary neurocontrollers (ENCs) [1]. Inspired by natural archetypes, ENC attacks the trajectoryoptimization problem from the perspective of artificial intelligence and machine learning, a perspective that is quite different from that of optimal control theory. Within the context of ENC, a trajectory is regarded as the result of a spacecraft steering strategy that maps permanently the actual spacecraft state and the actual target state onto the actual spacecraft control vector. This way, the problem of searching the optimal spacecraft trajectory is equivalent to the problem of searching (or "learning") the optimal spacecraft steering strategy. An artificial neural network is used to implement such a spacecraft steering strategy. It can be regarded as a parameterized function (the network function) that is defined by the internal network parameters. Therefore, each distinct set of network parameters defines a different network function and thus a different steering strategy. The problem of searching the optimal steering strategy is now equivalent to the problem of searching the optimal set of network parameters. Evolutionary algorithms that work on a population of (artificial) chromosomes are used to find the optimal network parameters, because the parameters can be easily mapped onto a chromosome. The trajectory optimization problem is solved when the optimal chromosome is found. A comparison of solar sail trajectories that have been published by others [2, 3, 4, 5] with ENC-trajectories has shown that ENCs can be successfully applied for near-globally optimal spacecraft control [1, 6] and that they are able to find trajectories that are closer to the (unknown) global optimum, because they explore the trajectory search space more exhaustively than a human expert can do. The obtained trajectories are fairly accurate with respect to the terminal constraint. If a more accurate trajectory is required, the ENC-solution can be used as an initial guess for a local trajectory optimization method. Using ENC, low-thrust trajectories can be optimized without an initial guess and without expert attendance. Here, new results for nuclear electric spacecraft and for solar sail spacecraft are presented and it will be shown that ENCs find very good trajectories even for very difficult problems. Trajectory optimization results are presented for 1. NASA's Solar Polar Imager Mission, a mission to attain a highly inclined close solar orbit with a solar sail [7] 2. a mission to de ect asteroid Apophis with a solar sail from a retrograde orbit with a very-high velocity impact [8, 9] 3. JPL's \2nd Global Trajectory Optimization Competition", a grand tour to visit four asteroids from different classes with a NEP spacecraft Y1 - 2007 ER - TY - CHAP A1 - Dachwald, Bernd A1 - Xu, Changsheng A1 - Feldmann, Marco A1 - Plescher, Engelbert T1 - IceMole : Development of a novel subsurface ice probe and testing of the first prototype on the Morteratsch Glacier T2 - EGU General Assembly 2011 Vienna | Austria | 03 – 08 April 2011 N2 - We present the novel concept of a combined drilling and melting probe for subsurface ice research. This probe, named “IceMole”, is currently developed, built, and tested at the FH Aachen University of Applied Sciences’ Astronautical Laboratory. Here, we describe its first prototype design and report the results of its field tests on the Swiss Morteratsch glacier. Although the IceMole design is currently adapted to terrestrial glaciers and ice shields, it may later be modified for the subsurface in-situ investigation of extraterrestrial ice, e.g., on Mars, Europa, and Enceladus. If life exists on those bodies, it may be present in the ice (as life can also be found in the deep ice of Earth). Y1 - 2011 ER - TY - CHAP A1 - Grundmann, Jan Thimo A1 - Biele, Jens A1 - Dachwald, Bernd A1 - Grimm, Christian A1 - Lange, Caroline A1 - Ulamec, Stephan T1 - Small spacecraft for small solar system body science, planetary defence and applications T2 - IEEE Aerospace Conference 2016 N2 - Following the recent successful landings and occasional re-awakenings of PHILAE, the lander carried aboard ROSETTA to comet 67P/Churyumov-Gerasimenko, and the launch of the Mobile Asteroid Surface Scout, MASCOT, aboard the HAYABUSA2 space probe to asteroid (162173) Ryugu we present an overview of the characteristics and peculiarities of small spacecraft missions to small solar system bodies (SSSB). Their main purpose is planetary science which is transitioning from a ‘pure’ science of observation of the distant to one also supporting in-situ applications relevant for life on Earth. Here we focus on missions at the interface of SSSB science and planetary defence applications. We provide a brief overview of small spacecraft SSSB missions and on this background present recent missions, projects and related studies at the German Aerospace Center, DLR, that contribute to the worldwide planetary defence community. These range from Earth orbit technology demonstrators to active science missions in interplanetary space. We provide a summary of experience from recently flown missions with DLR participation as well as a number of studies. These include PHILAE, the lander of ESA’s ROSETTA comet rendezvous mission now on the surface of comet 67P/Churyumov-Gerasimenko, and the Mobile Asteroid Surface Scout, MASCOT, now in cruise to the ~1 km diameter C-type near-Earth asteroid (162173) Ryugu aboard the Japanese sample-return probe HAYABUSA2. We introduce the differences between the conventional methods employed in the design, integration and testing of large spacecraft and the new approaches developed by small spacecraft projects. We expect that the practical experience that can be gained from projects on extremely compressed timelines or with high-intensity operation phases on a newly explored small solar system body can contribute significantly to the study, preparation and realization of future planetary defence related missions. One is AIDA (Asteroid Impact & Deflection Assessment), a joint effort of ESA, JHU/APL, NASA, OCA and DLR, combining JHU/APL’s DART (Double Asteroid Redirection Test) and ESA’s AIM (Asteroid Impact Monitor) spacecraft in a mission towards near-Earth binary asteroid system (65803) Didymos. DLR is currently applying MASCOT heritage and lessons learned to the design of MASCOT2, a lander for the AIM mission to support a bistatic low frequency radar experiment with PHILAE/ROSETTA CONSERT heritage to explore the inner structure of Didymoon which is the designated impact target for DART. Y1 - 2016 SP - 1 EP - 20 ER - TY - CHAP A1 - Peloni, Alessandro A1 - Dachwald, Bernd A1 - Ceriotti, Matteo T1 - Multiple NEA rendezvous mission: Solar sailing options T2 - Fourth International Symposium on Solar Sailing N2 - The scientific interest in near-Earth asteroids (NEAs) and the classification of some of those as potentially hazardous asteroid for the Earth stipulated the interest in NEA exploration. Close-up observations of these objects will increase drastically our knowledge about the overall NEA population. For this reason, a multiple NEA rendezvous mission through solar sailing is investigated, taking advantage of the propellantless nature of this groundbreaking propulsion technology. Considering a spacecraft based on the DLR/ESA Gossamer technology, this work focuses on the search of possible sequences of NEA encounters. The effectiveness of this approach is demonstrated through a number of fully-optimized trajectories. The results show that it is possible to visit five NEAs within 10 years with near-term solar-sail technology. Moreover, a study on a reduced NEA database demonstrates the reliability of the approach used, showing that 58% of the sequences found with an approximated trajectory model can be converted into real solar-sail trajectories. Lastly, this second study shows the effectiveness of the proposed automatic optimization algorithm, which is able to find solutions for a large number of mission scenarios without any input required from the user. KW - Multiphase KW - Trajectory Optimization KW - Automated Optimization KW - Gossamer KW - Sequence-Search Y1 - 2017 N1 - Fourth International Symposium on Solar Sailing (ISSS 2017), Kyoto, Japan, 17-20 Jan 2017. http://www.jsforum.or.jp/ISSS2017/ SP - 1 EP - 11 ER - TY - JOUR A1 - Fayyazi, Mojgan A1 - Sardar, Paramjotsingh A1 - Thomas, Sumit Infent A1 - Daghigh, Roonak A1 - Jamali, Ali A1 - Esch, Thomas A1 - Kemper, Hans A1 - Langari, Reza A1 - Khayyam, Hamid T1 - Artificial intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell vehicles N2 - Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed. KW - optimization system KW - intelligent control KW - fuel cell vehicle KW - machine learning KW - artificial intelligence KW - intelligent energy management Y1 - 2023 U6 - https://doi.org/10.3390/su15065249 N1 - This article belongs to the Special Issue "Circular Economy and Artificial Intelligence" VL - 15 IS - 6 SP - 38 PB - MDPI CY - Basel ER - TY - JOUR A1 - Möhren, Felix A1 - Bergmann, Ole A1 - Janser, Frank A1 - Braun, Carsten T1 - On the influence of elasticity on propeller performance: a parametric study JF - CEAS Aeronautical Journal N2 - The aerodynamic performance of propellers strongly depends on their geometry and, consequently, on aeroelastic deformations. Knowledge of the extent of the impact is crucial for overall aircraft performance. An integrated simulation environment for steady aeroelastic propeller simulations is presented. The simulation environment is applied to determine the impact of elastic deformations on the aerodynamic propeller performance. The aerodynamic module includes a blade element momentum approach to calculate aerodynamic loads. The structural module is based on finite beam elements, according to Timoshenko theory, including moderate deflections. Several fixed-pitch propellers with thin-walled cross sections made of both isotropic and non-isotropic materials are investigated. The essential parameters are varied: diameter, disc loading, sweep, material, rotational, and flight velocity. The relative change of thrust between rigid and elastic blades quantifies the impact of propeller elasticity. Swept propellers of large diameters or low disc loadings can decrease the thrust significantly. High flight velocities and low material stiffness amplify this tendency. Performance calculations without consideration of propeller elasticity can lead to decreased efficiency. To avoid cost- and time-intense redesigns, propeller elasticity should be considered for swept planforms and low disc loadings. KW - Propeller KW - Finite element method KW - Blade element method KW - Propeller elasticity KW - Aeroelasticity Y1 - 2023 U6 - https://doi.org/10.1007/s13272-023-00649-y SN - 1869-5590 (Online) SN - 1869-5582 (Print) N1 - Corresponding author: Felix Möhren VL - 14 SP - 311 EP - 323 PB - Springer Nature CY - Berlin ER - TY - GEN A1 - Eickmann, Matthias A1 - Esch, Thomas A1 - Funke, Harald A1 - Abanteriba, Sylvester A1 - Roosen, Petra T1 - Biofuels in Aviation – Safety Implications of Bio-Ethanol Usage in General Aviation Aircraft N2 - Up in the clouds and above fuels and construction materials must be very carefully selected to ensure a smooth flight and touchdown. Out of around 38,000 single and dual-engined propeller aeroplanes, roughly a third are affected by a new trend in the fuel sector that may lead to operating troubles or even emergency landings: The admixture of bio-ethanol to conventional gasoline. Experiences with these fuels may be projected to alternative mixtures containing new components. Y1 - 2014 N1 - 2. International Conference of the Cluster of Excellence Tailor-Made Fuels from Biomass, Aachen 2013 ER - TY - JOUR A1 - Dickhoff, Jens A1 - Horikawa, Atsushi A1 - Funke, Harald T1 - Hydrogen Combustion - new DLE Combustor Addresses NOx Emissions and Flashback JF - Turbomachinery international : the global journal of energy equipment Y1 - 2021 SN - 2767-2328 SN - 0149-4147 VL - 62 IS - 4 SP - 26 EP - 27 PB - MJH Life Sciences CY - Cranbury ER - TY - CHAP A1 - Thoma, Andreas A1 - Stiemer, Luc A1 - Braun, Carsten A1 - Fisher, Alex A1 - Gardi, Alessandro G. T1 - Potential of hybrid neural network local path planner for small UAV in urban environments T2 - AIAA SCITECH 2023 Forum N2 - 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. Y1 - 2023 U6 - https://doi.org/10.2514/6.2023-2359 N1 - AIAA SCITECH 2023 Forum, 23-27 January 2023, National Harbor, Md & Online PB - AIAA CY - Reston, Va. ER -