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Experimental and numerical investigation on the effect of pressure on micromix hydrogen combustion
(2021)
The micromix (MMX) combustion concept is a DLN gas turbine combustion technology designed for high hydrogen content fuels. Multiple non-premixed miniaturized flames based on jet in cross-flow (JICF) are inherently safe against flashback and ensure a stable operation in various operative conditions.
The objective of this paper is to investigate the influence of pressure on the micromix flame with focus on the flame initiation point and the NOx emissions. A numerical model based on a steady RANS approach and the Complex Chemistry model with relevant reactions of the GRI 3.0 mechanism is used to predict the reactive flow and NOx emissions at various pressure conditions. Regarding the turbulence-chemical interaction, the Laminar Flame Concept (LFC) and the Eddy Dissipation Concept (EDC) are compared. The numerical results are validated against experimental results that have been acquired at a high pressure test facility for industrial can-type gas turbine combustors with regard to flame initiation and NOx emissions.
The numerical approach is adequate to predict the flame initiation point and NOx emission trends. Interestingly, the flame shifts its initiation point during the pressure increase in upstream direction, whereby the flame attachment shifts from anchoring behind a downstream located bluff body towards anchoring directly at the hydrogen jet. The LFC predicts this change and the NOx emissions more accurately than the EDC. The resulting NOx correlation regarding the pressure is similar to a non-premixed type combustion configuration.
Kawasaki Heavy Industries, LTD. (KHI) has research and development projects for a future hydrogen society. These projects comprise the complete hydrogen cycle, including the production of hydrogen gas, the refinement and liquefaction for transportation and storage, and finally the utilization in a gas turbine for electricity and heat supply. Within the development of the hydrogen gas turbine, the key technology is stable and low NOx hydrogen combustion, namely the Dry Low NOx (DLN) hydrogen combustion.
KHI, Aachen University of Applied Science, and B&B-AGEMA have investigated the possibility of low NOx micro-mix hydrogen combustion and its application to an industrial gas turbine combustor. From 2014 to 2018, KHI developed a DLN hydrogen combustor for a 2MW class industrial gas turbine with the micro-mix technology. Thereby, the ignition performance, the flame stability for equivalent rotational speed, and higher load conditions were investigated. NOx emission values were kept about half of the Air Pollution Control Law in Japan: 84ppm (O2-15%). Hereby, the elementary combustor development was completed.
From May 2020, KHI started the engine demonstration operation by using an M1A-17 gas turbine with a co-generation system located in the hydrogen-fueled power generation plant in Kobe City, Japan. During the first engine demonstration tests, adjustments of engine starting and load control with fuel staging were investigated. On 21st May, the electrical power output reached 1,635 kW, which corresponds to 100% load (ambient temperature 20 °C), and thereby NOx emissions of 65 ppm (O2-15, 60 RH%) were verified. Here, for the first time, a DLN hydrogen-fueled gas turbine successfully generated power and heat.
This study investigates the influence of pressure on the temperature distribution of the micromix (MMX) hydrogen flame and the NOx emissions. A steady computational fluid dynamic (CFD) analysis is performed by simulating a reactive flow with a detailed chemical reaction model. The numerical analysis is validated based on experimental investigations. A quantitative correlation is parametrized based on the numerical results. We find, that the flame initiation point shifts with increasing pressure from anchoring behind a downstream located bluff body towards anchoring upstream at the hydrogen jet. The numerical NOx emissions trend regarding to a variation of pressure is in good agreement with the experimental results. The pressure has an impact on both, the residence time within the maximum temperature region and on the peak temperature itself. In conclusion, the numerical model proved to be adequate for future prototype design exploration studies targeting on improving the operating range.
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.
A new method for improved autoclave loading within the restrictive framework of helicopter manufacturing is proposed. It is derived from experimental and numerical studies of the curing process and aims at optimizing tooling positions in the autoclave for fast and homogeneous heat-up. The mold positioning is based on two sets of information. The thermal properties of the molds, which can be determined via semi-empirical thermal simulation. The second information is a previously determined distribution of heat transfer coefficients inside the autoclave. Finally, an experimental proof of concept is performed to show a cycle time reduction of up to 31% using the proposed methodology.
For typical cases of non-isolated lightning
protection systems (LPS) the impulse currents are investigated which may flow through a human body directly touching a structural part of the LPS. Based on a basic LPS model with conventional down-conductors especially the cases of external and internal steel columns and metal façades are considered and compared. Numerical simulations of the line quantities voltages and currents in the time domain are performed with an equivalent circuit of the entire LPS.
As a result it can be stated that by increasing the number of conventional down-conductors and external steel columns the threat for a human being can indeed be reduced, but not down to an acceptable limit. In case of internal steel columns used as natural down-conductors the threat can be reduced sufficiently, depending on the low-resistive connection of the steel columns to the lightning equipotential bonding or the earth termination system, resp. If a metal façade is used the threat for human beings touching is usually very low, if the façade is sufficiently interconnected and multiply connected to the lightning equipotential bonding or the earth termination system, resp.
The course Physics for Electrical Engineering is part of the curriculum of the
bachelor program Electrical Engineering at University of Applied Science Aachen.
Before covid-19 the course was conducted in a rather traditional way with all parts
(lecture, exercise and lab) face-to-face. This teaching approach changed
fundamentally within a week when the covid-19 limitations forced all courses to
distance learning. All parts of the course were transformed to pure distance learning
including synchronous and asynchronous parts for the lecture, live online-sessions
for the exercises and self-paced labs at home. Using these methods, the course was
able to impart the required knowledge and competencies. Taking the teacher’s
observations of the student’s learning behaviour and engagement, the formal and
informal feedback of the students and the results of the exams into account, the new
methods are evaluated with respect to effectiveness, sustainability and suitability for
competence transfer. Based on this analysis strong and weak points of the concept
and countermeasures to solve the weak points were identified. The analysis further
leads to a sustainable teaching approach combining synchronous and asynchronous
parts with self-paced learning times that can be used in a very flexible manner for
different learning scenarios, pure online, hybrid (mixture of online and presence
times) and pure presence teaching.
Adapting Augmented Reality Systems to the users’ needs using Gamification and error solving methods
(2021)
Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.
Component failures within water supply systems can lead to significant performance losses. One way to address these losses is the explicit anticipation of failures within the design process. We consider a water supply system for high-rise buildings, where pump failures are the most likely failure scenarios. We explicitly consider these failures within an early design stage which leads to a more resilient system, i.e., a system which is able to operate under a predefined number of arbitrary pump failures. We use a mathematical optimization approach to compute such a resilient design. This is based on a multi-stage model for topology optimization, which can be described by a system of nonlinear inequalities and integrality constraints. Such a model has to be both computationally tractable and to represent the real-world system accurately. We therefore validate the algorithmic solutions using experiments on a scaled test rig for high-rise buildings. The test rig allows for an arbitrary connection of pumps to reproduce scaled versions of booster station designs for high-rise buildings. We experimentally verify the applicability of the presented optimization model and that the proposed resilience properties are also fulfilled in real systems.
One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.