District heating networks transport thermal energy from one or more sources to a plurality of con-sumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losse...
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District heating networks transport thermal energy from one or more sources to a plurality of con-sumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losses and unlock the potential of low-temperature heat sources, such as waste heat. With an increasing share of uncontrolled heat sources in district heating networks, control strategies to co-ordinate energy supply and network operation become more important. This paper focuses on the modeling, control, and optimization of a low-temperature district heating network, presenting a case study with a high share of waste heat from high-performance computers. The network consists of heat pumps with temperature-dependent characteristics. In this paper, quadratic correlations are used to model temperature characteristics. Thus, a mixed-integer quadratically-constrained program is pre-sented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. The network operation is optimized for three sample days. The presented optimization model uses the flexibility of the thermal energy storages and thermal inertia of the network by controlling its flow and return temperatures. The results show savings of electrical energy consumption of 1.55%e5.49%, depending on heat and cool demand. (c) 2021 Elsevier Ltd. All rights reserved.
Many researchers and traffic engineers have been working on optimizing signal control with respect to its effects on traffic flow and externalities like emissions. As large field studies for traffic management are not...
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Many researchers and traffic engineers have been working on optimizing signal control with respect to its effects on traffic flow and externalities like emissions. As large field studies for traffic management are not sustainable and actuated control strategy becomes complex, its evaluation is done with microscopic traffic simulation. However, their integrated signal control model often impedes implementing the custom control strategy in contrast to signal planning software. Making both types of software cooperate offers valuable perspectives to develop and evaluate a custom control strategy more efficiently. In this work, an overview of signal control and how it can be modeled in microscopic traffic simulation is given. Then, the signal planning software LISA+ and a microscopic traffic simulation (Aimsun in this case) are connected to allow for external signal control. LISA+ offers a virtual signal controller to communicate with external applications via network. Aimsun sends information about the chosen signal program and the detected vehicles to the controller and receives back the signal states for the next simulation second. This concept is presented in detail taking into account the communication protocol and the implementation on the Aimsun side. A case study with time-dependent actuated signal control is included as well.
Driver Assistance Systems (DAS) have become one of the key elements in the automotive industry. Nevertheless, as they are relative young fields of competence, there is still room for improvement, both in its implement...
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Driver Assistance Systems (DAS) have become one of the key elements in the automotive industry. Nevertheless, as they are relative young fields of competence, there is still room for improvement, both in its implementation and development strategies. DAS will become more effective through data fusion. One of its standing applications is the implementation in a longitudinal control system, based on object detection performed by the complementary properties of radar and image processing. This paper summarizes the activities undertaken in this domain by CTAG, including development and testing methodologies based in specifically developed tools for software in the loop (SiL) and in-vehicle testing.
This paper presents guidelines to develop the Maximum Power Point Tracking controller, as developed in the automotive and aeronautical applications, this by following the V-cycle development process, which means that ...
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This paper presents guidelines to develop the Maximum Power Point Tracking controller, as developed in the automotive and aeronautical applications, this by following the V-cycle development process, which means that our controller will be validated by using Model In the loop/ software in the loop/Processor In the loop tests. In order to have the possibility of integrating the MPPT embedded software in automotive and aeronautical areas, and on the other hand to propose a low-cost option to test the hardware implementation of the MPPT algorithm. Therefore, a modified variable step Incremental Conductance algorithm is proposed in this study, which can reduce the steady-state oscillations and increase the tracking speed under sudden irradiance variation. Then, the Model-based design of the modified algorithm is developed and connected to the plant model (photovoltaic panel and Boost converter). Next, the system model is tested and validated by using Model In the loop process. After that, the software of this algorithm is automatically generated for the host computer using embedded coder tool, and this software is connected to the plant model and tested using software in the loop process in the host computer. Finally, the software is generated from the MPPT model for the STM32F4 discovery board in order to create the Processor In the loop block, which will be run in the STM32F4 discovery board, and the plant model will be simulated in the host computer, and the ST-LINK communication is used in order to connect the host computer and the embedded board.
Unmanned aerial vehicles (UAV) rely on a variety of sensors to perceive and navigate their airborne environment with precision. The autopilot software interprets this sensory data, acting as the control mechanism for ...
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Unmanned aerial vehicles (UAV) rely on a variety of sensors to perceive and navigate their airborne environment with precision. The autopilot software interprets this sensory data, acting as the control mechanism for autonomous flights. As UAVs are exposed to physical environment, they are vulnerable to potential impairments in their sensory mechanism. Their real-time interactions with the actual atmosphere make them susceptible to cyber exploitations as well, where sensory data alterations through counterfeit wireless signals pose a significant threat. In this context, sensor failures can result into unsafe flight conditions, as the fault handling logic may fail to anticipate the context of the issue, allowing autopilot to execute operations without necessary adjustments. Untimely control of sensor failures can result in mid-air collisions or crashes. To address these challenges, we created Biomisa Arducopter Sensory Critique (BASiC) dataset, a state-of-the-art resource for UAV sensor failure analysis. The BASiC dataset comprises 70 autonomous flight data, spanning over 7 hours. It encompasses 3 + hours of (each) pre-failure and post-failure data, along with 1 + hour of no-failure data. We selected the ArduPilot platform as our demonstration aerial vehicle to conduct the experiments. By engineering software in the loop (SITL) parameters, we effectively executed sensor failure test simulations. Our dataset incorporates six representative sensors failures which are critical to UAV operations: global positioning system (GPS) for precise aerial positioning, remote control for communication with the ground control station (GCS), accelerometer for measuring linear acceleration, gyroscope for rotational acceleration measurement, compass providing heading information, and barometer for maintaining flight height based on atmospheric pressure data. The availability of the BASiC dataset will benefit the research community, empowering researchers to explore and experiment with
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