Helmholtz AI consultants @ DLR

Helmholtz AI consultants @ German Aerospace Center

Andrés Camero Unzueta

Team leader

 

Aeronautics, space and transportation-focused AI consultants

The Helmholtz AI consultant team @ DLR provides expertise from Earth observation, robotics and computer vision and an HPC/HPDA support unit. 

The team covers a comprehensive package of AI-related services. It is supposed to support both projects within LRV as well as cooperative projects with other Helmholtz centers.

Questions or ideas? consultant-helmholtz.ainoSp@m@dlr.de

Team members

Andrés Camero Unzueta

Andrés Camero Unzueta

Helmholtz AI consultant team leader @ DLR

Andrés Camero Unzueta

Andrés Camero Unzueta

Helmholtz AI consultant team leader @ DLR

Maximilian Denninger

Maximilian Denninger

Helmholtz AI consultant @ DLR

Maximilian Denninger

Maximilian Denninger

Helmholtz AI consultant @ DLR

Daniela Espinoza

Daniela Espinoza

Helmholtz AI consultant @ DLR

Daniela Espinoza

Daniela Espinoza

Helmholtz AI consultant @ DLR

Tobias Hecking

Tobias Hecking

Helmholtz AI consultant @ DLR

Tobias Hecking

Tobias Hecking

Helmholtz AI consultant @ DLR

Nico Hochgeschwender

Nico Hochgeschwender

Helmholtz AI consultant @ DLR

Nico Hochgeschwender

Nico Hochgeschwender

Helmholtz AI consultant @ DLR

Danfeng Hong

Danfeng Hong

Helmholtz AI consultant @ DLR

Danfeng Hong

Danfeng Hong

Helmholtz AI consultant @ DLR

Ridvan Salih Kuzu

Ridvan Salih Kuzu

Helmholtz AI consultant @ DLR

Ridvan Salih Kuzu

Ridvan Salih Kuzu

Helmholtz AI consultant @ DLR

Jongseok Lee

Jongseok Lee

Helmholtz AI consultant @ DLR

Jongseok Lee

Jongseok Lee

Helmholtz AI consultant @ DLR

Alexander Ruettgers

Alexander Ruettgers

Helmholtz AI consultant @ DLR

Alexander Ruettgers

Alexander Ruettgers

Helmholtz AI consultant @ DLR

Sivasurya Santhanam

Sivasurya Santhanam

Helmholtz AI consultant @ DLR

Sivasurya Santhanam

Sivasurya Santhanam

Helmholtz AI consultant @ DLR

Martin Siggel

Martin Siggel

Helmholtz AI consultant @ DLR

Martin Siggel

Martin Siggel

Helmholtz AI consultant @ DLR

Rudolph Triebel

Rudolph Triebel

Helmholtz AI consultant @ DLR

Rudolph Triebel

Rudolph Triebel

Helmholtz AI consultant @ DLR

Yuanyuan Wang

Yuanyuan Wang

Helmholtz AI consultant @ DLR

Yuanyuan Wang

Yuanyuan Wang

Helmholtz AI consultant @ DLR

Ongoing voucher projects

ATTENTIVE

  • Challenge: Driving a train is a stressful task, thus it is very important to monitor the condition of the driver to ensure the security of the convoy and its passengers. The goal of this project is to estimate the stress and strain by tracking the eyes of professional drivers, along with contextual information (e.g., actual and target speed, timetable related information regarding energy-saving driving, and the active train protection system).
  • Approach: Work is underway to conceptualize and implement an integrated artificial intelligence approach to integrate heterogeneous data.

Stab-FCT

  • Challenge: By controlling the temperature of fuel cells in vehicles, one can improve significantly its life time. One possible approach for maintaining the desired temperature of the fuel cell is converting the pressure energy of the hydrogen tank to thermal energy by a metal hydride reaction. The temperature of the reaction is determined by the applied hydrogen pressure, and the corresponding hydrogen pressure level for the required temperature has to be predicted in order to enable fast thermal management in this narrow temperature range. One challenge that arises, however, is that processes within metal hydrides are highly non-linear and the emitted heat and its transfer through the reactor depend on various factors such as hydrogen conversion, temperature and time. Furthermore, not all internal processes and properties of the reactor are known and describable which makes an exact calculation of the pressure needed to achieve a specific cell temperature impossible.
  • Approach: To overcome this problem, this project aims at employing machine learning to build a regression model from large-scale experimental data that can predict the required pressure within the metal hydride reactor to keep the temperature of a fuel cell in the optimal range under many different conditions. Also, verification techniques for estimating the robustness and worst case error, such as interval bound propagation, will be applied.

Automated tuft recognition for flow visualization

  • Challenge: One of the simplest and oldest ways to visualize fluid flow in experiments is the application of tufts. Tufts are small pieces of rope/wire that align themselves with the local flow direction. In regions of separated flow the tufts typically display a very unsteady behavior. Tufts or flow cones are used in both flight test and wind tunnel experiments to determine the local flow on a surface to improve the understanding of the flow and provide validation data for numerical models. The tufts/flow cones are filmed by a camera. Afterwards the images are processed in order to obtain the local flow direction in terms of angle, shape, temporal behavior, etc. of the tuft. In order to evaluate the temporal behavior of a tuft it is paramount to compare the status in one image to the status of the same tuft in another image. Thus, it is proposed to develop a fully automatic post processing chain for tufts, that should work for a wide variety of images from different campaigns (differences in lighting, perspective, contrast, background, etc.).
  • Approach: The consultant team will design an implement a ML pipeline to detect and segment the tufts and cones in the raw images. Then, this information is going to be used to detect the direction, shape, and length of these elements.

ANSIM

  • Challenge: The Department of Gravitational Biology at the Institute of Aerospace Medicine (DLR) investigates human performance in extreme environments to gain deeper insights in physiology and develop new applications for manned space exploration and clinical practice. In this project, high resolution cardiovascular data will be used to find early markers and changes that could indicate a developing orthostatic instability or pre-syncope symptoms. Despite a long history of medical research, the underlying causes of syncope are unknown in half of the cases. The Institute has a large pool of cardiovascular data from tilt table or human centrifuge studies. Also, this data my be used in combination with data from bed rest induced deconditioning ageing effects. A practical application of such a solution can be found in wearable devices that continuously monitor cardiovascular data of risk patients as well as older people as preventive measure.
  • Approach: To tackle this problem, we will design and train a model using this data to find early signs of performance drops and cardiovascular events before vasovagal symptoms occur.

Consulting and training on perspectives for the application of machine learning in the research fields of the institute

  • Challenge: The Institute of Vehicle Concepts (DLR) has several research domains, where a huge amount of data is generated. Currently, most researchers lack of machine learning knowledge. Therefore, to start filling this gap, we would like to receive training, aiming to explaining the requirements for input data, how data processing takes place, and which results could be expected. Also, we would like to have a series of workshops with the consultants to discuss potential applications, and to prioritize them and start planning realization vouchers.

Completed voucher projects

ML for aviation fuels

  • Challenge: One of the main topics of the Multiphase Flows and Alternative Fuels Department at the DLR is to investigate alternative aviation fuels to mitigate climate impact. Particularly, the Department develops physical-based models to predict the properties and performance of aviation fuels, which are a mixture of up to hundreds of different hydrocarbon species. As a new field, the prediction capability of different Machine Learning algorithms regarding fuels properties is investigated. The results so far are very promising. For some fuels and properties the applied AI algorithm shows even smaller errors than the quite sophisticated physical based models. Therefore, the Department is interested in studying the the prediction of model uncertainty.

Advancement of fundamental understanding of turbulence–chemistry–phase interactions viaML

  • Challenge: The confluence of climate change, environmental protection and diminishing resources have promoted the development of low carbon footprint and sustainable technologies. The efficiency and functional capability of many engineering devices and industrial processes is strongly influenced by turbulent reacting multi-phase flows. Practical examples include (i) aeroengines where interactions of turbulent flow, fuel spray and combustion chemistry determine engine operability, thermodynamic efficiency and emissions, (ii) industrial sprays where an insufficient atomisation and droplet dispersion can impair product quality and yield and (iii) agricultural applications where an inhomogeneous distribution of herbicides and pesticides can lead to loss of crops or wastage and excessive water and ground degradation. Thus, the accurate description of interactions of turbulent flow, reaction chemistry and multiple phases is of paramount importance to develop clean and environmentally friendly technologies. This requires improved control and a more profound fundamental understanding. However, the advancement of the latter is impeded by the lack of quantitative data due to difficulties of automatically identifying physical and chemical states. State-of-the-art detection algorithms rely on predefined conditions that are cumbersome to develop and implement. A primary application of machine learning is the identification of features with common attributes in images. The objective of this voucher is to develop an automated image segregation tool. This tool should recognize physical and chemical states in scientific images. Thelong-term objective of related machine learning activities is the development of a general framework for the identification of physical and chemical states that accelerates quantitative data analysis. The efficient and reliable evaluation of data provides a competitive edge to research projects and offers new funding routes. Moreover, the data facilitate, in combination with the simultaneously measured turbulent flow field and additional scalars, a quantitative description of turbulence–chemistry–phase interactions and close the knowledge gap by advancing fundamental understanding. The acquired data will further support the development of more comprehensive numerical models, novel combustion concepts and industrial processes.
  • Approach: The consultant team implement a segmentation network (Mask R-CNN) to learn to detect and segment targets in images. The instance segmentation results can be used for a quantitative understanding of turbulence-chemistry interactions. 

ML for rocket combustion data

  • Challenge: The Propellants Department of the Institute of Space Propulsion (DLR) is investigating the combustion processes in a paraffin-based hybrid rocket engine under different operating conditions. More than 300 combustion videos have been recorded with a high-speed video camera. To process all this data the Department wants to develop an automatic analysis procedure to identify essential flow structures and different combustion phases. Particularly, the aim of this voucher is to implement and to evaluate advanced distance and dissimilarity measures (e.g., based on structural similarity).
  • Approach: The team implemented a solution using HEAT, an Open Source Software package (https://github.com/helmholtz-analytics/heat) for distributed data analysis and machine learning developed by KIT and the DLR, based on k-means++ and spectral to identify short-term turbulences.

Selected publications

  • Debus, C., Ruettgers, A., Petrarolo, A., Kobald, M. and Siggel, M., 2020. High-performance data analytics of hybrid rocket fuel combustion data using different machine learning approaches. In AIAA Scitech 2020 Forum (p. 1161).
  • Mou, L., Lu, X., Li, X. and Zhu, X.X., 2020. Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing., doi: 10.1109/TGRS.2020.2973363.
  • Lee, J., Humt, M., Feng, J. And Triebel, R., 2020. Estimating Model Uncertainty of Neural Networks in Sparse Information Form. In Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020.
  • Mou, L., Hua, Y. and Zhu, X.X., 2019. A relation-augmented fully convolutional network for semantic segmentation in aerial scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 12416-12425).
  • Denninger, M. and Triebel, R., 2020. 3D Scene Reconstruction from a Single Viewport. In Proceedings of the European Conference on Computer Vision (ECCV)