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A two-component system for control and monitoring of solar power towers with molten salt receivers is proposed. The control component consists of a model predictive control applica-tion (MPC) with a flexible objective function and on-line tunable weights, which runs on a In-dustrial PC and uses a reduced order dynamic model of the receiver’s thermal and flow dy-namics. The second component consists of a service-life monitoring unit, which estimates the service-life consumption of the absorber tubes depending on the current mode of operation based on thermal stresses and creep fatigue in the high temperature regime. The calculation of stresses is done based on a detailed finite element study, in which a digital twin of the re-ceiver was developed. By parallelising the model solver, the estimation of service-life con-sumption became capable of real-time operation. The system has been implemented at a test facility in Jülich, Germany, and awaits field experiments. In this paper, the modeling and archi-tecture are presented along simulation results, which were validated on a hardware-in-the-loop test bench. The MPC showed good disturbance rejection while respecting process variable constraints during the simulation studies.
Magnetic Resonance (MR) elastography is a method for measuring tissue elasticity via phase images acquired with an MR scanner. The propagation of periodic mechanical waves through the tissue can be captured by means of a modified phase contrast sequence. These waves are generated with a mechanical oscillator (actuator) and coupled into the tissue through the skin. The actuator must be capable of generating a sinusoidal excitation with excellent phase and amplitude stability, while not disturbing the MR imaging process. In this work, an actuator based on a piezoelectric principle was developed. Based on the imaging evaluation of several material samples, the housing for the piezoelectric ceramic was constructed of aluminum. Smaller parts of the housing were manufactured from brass and titanium to fulfill the mechanical constraints. A lever was used to transfer the oscillation generated by the piezoelectric ceramic to the point of excitation. The lever amplifies the piezoelectric motion, allowing for a more compact design. Three different lever designs were characterized by an acceleration sensor both outside and inside the magnet. It was shown that the rigidity of the lever, as determined by its material and form, was decisive in determining the resonant frequency of the system and therefore the maximum practical frequency of operation. It was also shown that the motion of the oscillator is unaffected by the electromagnetic fields of the MR imager. The final design can be placed directly in the magnet bore within a few centimeters of the tissue volume to be imaged without generating significant artifacts. An amplitude range of 0–1 mm in the frequency range from 0 to over 300 Hz was achieved, sufficient for performing most MR elastography applications. © 2002 Wiley Periodicals, Inc. Concepts in Magnetic Resonance (Magn Reson Engineering) 15: 239–254, 2002
Men afflicted with hypogonadism have reduced fat-free mass and in some cases a loss in muscle strength. We hypothesize that muscle elasticity of the lower extremities differs between patients with hypogonadism and healthy controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in applied force.
Men afflicted with chronic obstructive pulmonary disease (COPD) show signs of muscle dysfunction, such as increased muscle fatigue and acidosis during exercise. We hypothesize that muscle elasticity of the lower extremities differs between patients with COPD and age-related controls. The shear modulus of the soleus muscle was measured non-invasively using MR elastography (MRE) at 0%, 5%, 10%, 15%, and 20% of the subject’s maximum applied force. The results from this study showed statistical differences between the patients and controls, even though there were no statistical differences in the maximum applied force.
The Chylla–Haase polymerization reactor is widely accepted as a benchmark process for the evaluation of control strategies for batch reactors. In this contribution a control concept based on Exact I/O-Linearization is proposed and compared to a conventional cascade control structure. In order to adapt the exact linearization control strategy to various polymerization products and batch conditions, an advanced probabilistic inference algorithm (Sigma-Point Kalman Filter) is applied and investigated. Sigma-Point Kalman Filters have the major improvement of simplified implementation compared to local linearization methods (i.e. Extended Kalman Filter) because no analytical Jacobians are required. Stochastic simulation studies are introduced and show the effectiveness, accuracy and benefit of the control concept. Within several scenarios a satisfying robustness against structural errors in the underlying model equations for the nonlinear control law and the inference algorithm is demonstrated. Furthermore it is pointed out, that with little effort in reassembling the plant design, control performance can be improved significantly.
This article deals with the cost optimal commitment of power systems. System stability is often only considered after solving the commitment problem. We show by example that a cost optimization may result in a mode of operation that, while economically optimal, is unstable or has otherwise unacceptable dynamics. As a remedy, we propose to use the so-called normal vector method, which has been developed for the optimization of nonlinear dynamical systems with stability boundaries and uncertain parameters. We apply the method to a small sample power grid, where the optimization goal is to minimize the energy production costs for a given power consumption.
Stability and cost optimality of power generation and supply systems must not be considered independently from one another. Simple examples show that optimizing cost without considering stability may result in modes of operation that, while economically optimal, are unstable. We demonstrate that stability, robustness, and optimality can be considered systematically and simultaneously by combining bifurcation theory and nonlinear optimization. Essentially, the proposed method enforces a backoff distance between the optimal point of operation and operational or stability boundaries in the space of the optimization variables, where bifurcation theory is used to describe nonlinear stability boundaries.
We present a control scheme for a parabolic trough power plant that is equipped with a molten salt thermal energy storage system. We show that the multivariable control problem for the solar field can be decoupled by controlling the three way valve that splits the heat transfer fluid between the storage system and the steam generator. The steam generation cycle is regulated with inlet pressure control. The subsystems of the plant are modeled based on first principles, where we focus on the steam generation cycle. A multi-group extraction turbine with pre- and reheater is considered, which is suitable for solar power plants. We demonstrate the benefits of the proposed control scheme with a day-to-day simulation.
We derive a nonlinear model of the steam cycle of a solar power plant, estimate its unknown parameters with measured data, and design a linear model predictive controller based on the resulting model. Simulations show that the linear model predictive controller is able to regulate the electrical power of the nonlinear steam process of the plant to a given reference trajectory very well. Moreover, it results in very good disturbance rejection.
Eye tracking is a measurement technique for determining the orientation and viewing direction of an individual’s eyes. It allows for an evaluation of the individual’s gaze and other related information.There is a wide variety of application areas for eye tracking, however the usage in engineeringindustry is still limited. This paper presents a case study for the use of eye tracking in a testingstation for electrical circuit breakers. The case study aims to evaluate the measurement and analysispossible with the Tobii wearable eye tracking system. From the case study, possibilities, limitationsand considerations for eye tracking in industrial settings are identified and discussed. The paperconcludes that while eye tracking can be used to effectively capture and visualize insightfulinformation, it faces major challenges related to subjectivity in analysis and labour intensiveness.
Process models in industrial applications, e.g. predictive maintenance or automation, are subject to both divergence from the underlying system due to their time-variant nature and to high complexity resulting from a wide operational range being covered. Hence, regression models require high accuracy for the present system state and at the same time need to be valid across the whole system operating space. While accuracy for the current system state can be gained by updating the model on the current data, the overall validity must often be retrieved from historical or design data. We propose a method to find an appropriate compromise for these two demands. A pre-trained artificial neural network (ANN) is continually updated on the current sensor data stream using convex optimisation. Thus, a unique and optimal solution is generated in each update step, while robust regression accuracy on the domain that is not covered by the arriving data subset is maintained. This is achieved by introducing a data management system to provide some historical data, constraining the optimisation problem and manipulating the architecture of the ANN. Models updated with this method show reasonable stability but display plastic behaviour at the current operating point.
Higher control engineering concepts, including artificial intelligence and model-based control, are recently becoming more popular in complex industrial applications because they enable a significant increase in efficiency. E.g., a model-based approach can be used to enhance the control and monitoring of several coupled components of a larger turbomachinery train. This requires dynamic models of the components, including all machine characteristics, which may experience a shift in their behavior over the course of their lifetime. Such changes occur due to natural fouling, wear of sub-components or external effects induced by maintenance measures. To overcome this problem, we model machine-characteristic maps with artificial neural networks, which may be used as part of prediction models in a model predictive control unit. If an appropriate data set is available, this allows us to adapt the model to the current behavior of the machine without requiring in-depth knowledge about the underlying physics of this “concept drift”, as it is known in literature. However, the training of neural networks is usually connected with high computational effort while the task needs to be executed in real-time during operation. Furthermore, in real operating conditions, machine sensors can only provide labeled data from the current operating point of the machine, which can be stationary for long operation periods of up to several months. In general, this yields only an unrepresentative data subset of the machine-characteristics, which is not sufficient to retrain the whole model to a new state. To solve these problems, we firstly reduce the model adaptation to a convex optimization problem, which can be efficiently solved in real-time conditions. Secondly, we use a specialized data management system with which we can integrate historical data to supplement the unrepresentative data subset. In this paper we show the application of the outlined method on a compressor map and discuss the advantages and requirements of the method in the context of modeling applications for industrial turbomachinery.
Suffizienz als Ausweg?!
(2024)
Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blur