Refine
Year of publication
Institute
- ECSM European Center for Sustainable Mobility (43) (remove)
Document Type
- Article (43) (remove)
Keywords
- Brake set-up (2)
- Freight rail (2)
- Obstacle avoidance (2)
- Path planning (2)
- UAV (2)
- Active humidity control (1)
- Aircraft sizing (1)
- Automotive safety approach (1)
- Autonomy (1)
- Bio-inspired systems (1)
In order to realistically predict and optimize the actual performance of a concentrating solar power (CSP) plant sophisticated simulation models and methods are required. This paper presents a detailed dynamic simulation model for a Molten Salt Solar Tower (MST) system, which is capable of simulating transient operation including detailed startup and shutdown procedures including drainage and refill. For appropriate representation of the transient behavior of the receiver as well as replication of local bulk and surface temperatures a discretized receiver model based on a novel homogeneous two-phase (2P) flow modelling approach is implemented in Modelica Dymola®. This allows for reasonable representation of the very different hydraulic and thermal properties of molten salt versus air as well as the transition between both. This dynamic 2P receiver model is embedded in a comprehensive one-dimensional model of a commercial scale MST system and coupled with a transient receiver flux density distribution from raytracing based heliostat field simulation. This enables for detailed process prediction with reasonable computational effort, while providing data such as local salt film and wall temperatures, realistic control behavior as well as net performance of the overall system. Besides a model description, this paper presents some results of a validation as well as the simulation of a complete startup procedure. Finally, a study on numerical simulation performance and grid dependencies is presented and discussed.
The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different unmanned aircraft used for surveillance, reconnaissance, and search and rescue missions. The aircraft is simulated using a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV’s parasitic drag is significantly influenced by the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components are responsible for almost half of an unmanned aircraft’s total parasitic drag. The new equivalent skin friction coefficient accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.
The predictive control of commercial vehicle energy management systems, such as vehicle thermal management or waste heat recovery (WHR) systems, are discussed on the basis of information sources from the field of environment recognition and in combination with the determination of the vehicle system condition.
In this article, a mathematical method for predicting the exhaust gas mass flow and the exhaust gas temperature is presented based on driving data of a heavy-duty vehicle. The prediction refers to the conditions of the exhaust gas at the inlet of the exhaust gas recirculation (EGR) cooler and at the outlet of the exhaust gas aftertreatment system (EAT). The heavy-duty vehicle was operated on the motorway to investigate the characteristic operational profile. In addition to the use of road gradient profile data, an evaluation of the continuously recorded distance signal, which represents the distance between the test vehicle and the road user ahead, is included in the prediction model. Using a Fourier analysis, the trajectory of the vehicle speed is determined for a defined prediction horizon.
To verify the method, a holistic simulation model consisting of several hierarchically structured submodels has been developed. A map-based submodel of a combustion engine is used to determine the EGR and EAT exhaust gas mass flows and exhaust gas temperature profiles. All simulation results are validated on the basis of the recorded vehicle and environmental data. Deviations from the predicted values are analyzed and discussed.
The paper presents an aerodynamic investigation of 70 different streamlined bodies with fineness ratios ranging from 2 to 10. The bodies are chosen to idealize both unmanned and small manned aircraft fuselages and feature cross-sectional shapes that vary from circular to quadratic. The study focuses on friction and pressure drag in dependency of the individual body’s fineness ratio and cross section. The drag forces are normalized with the respective body’s wetted area to comply with an empirical drag estimation procedure. Although the friction drag coefficient then stays rather constant for all bodies, their pressure drag coefficients decrease with an increase in fineness ratio. Referring the pressure drag coefficient to the bodies’ cross-sectional areas shows a distinct pressure drag minimum at a fineness ratio of about three. The pressure drag of bodies with a quadratic cross section is generally higher than for bodies of revolution. The results are used to derive an improved form factor that can be employed in a classic empirical drag estimation method. The improved formulation takes both the fineness ratio and cross-sectional shape into account. It shows superior accuracy in estimating streamlined body drag when compared with experimental data and other form factor formulations of the literature.
Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation
(2020)
To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.
Berücksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen
(2020)
Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen für hohe Kosten. Diese sind häufig auf unpräzise Fehlerbeschreibungen zurückzuführen. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.