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Is part of the Bibliography
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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 number of case studies focusing on hybrid-electric aircraft is steadily increasing, since these configurations are thought to lead to lower operating costs and environmental impact than traditional aircraft. However, due to the lack of reference data of actual hybrid-electric aircraft, in most cases, the design tools and results are difficult to validate. In this paper, two independently developed approaches for hybrid-electric conceptual aircraft design are compared. An existing 19-seat commuter aircraft is selected as the conventional baseline, and both design tools are used to size that aircraft. The aircraft is then re-sized under consideration of hybrid-electric propulsion technology. This is performed for parallel, serial, and fully-electric powertrain architectures. Finally, sensitivity studies are conducted to assess the validity of the basic assumptions and approaches regarding the design of hybrid-electric aircraft. Both methods are found to predict the maximum take-off mass (MTOM) of the reference aircraft with less than 4% error. The MTOM and payload-range energy efficiency of various (hybrid-) electric configurations are predicted with a maximum difference of approximately 2% and 5%, respectively. The results of this study confirm a correct formulation and implementation of the two design methods, and the data obtained can be used by researchers to benchmark and validate their design tools.
In addition to very high safety and reliability requirements, the design of internal combustion engines (ICE) in aviation focuses on economic efficiency. The objective must be to design the aircraft powertrain optimized for a specific flight mission with respect to fuel consumption and specific engine power. Against this background, expert tools provide valuable decision-making assistance for the customer. In this paper, a mathematical calculation model for the fuel consumption of aircraft ICE is presented. This model enables the derivation of fuel consumption maps for different engine configurations. Depending on the flight conditions and based on these maps, the current and the integrated fuel consumption for freely definable flight emissions is calculated. For that purpose, an interpolation method is used, that has been optimized for accuracy and calculation time. The mission boundary conditions flight altitude and power requirement of the ICE form the basis for this calculation. The mathematical fuel consumption model is embedded in a parent program. This parent program presents the simulated fuel consumption by means of an example flight mission for a representative airplane. The focus of the work is therefore on reproducing exact consumption data for flight operations. By use of the empirical approaches according to Gagg-Farrar [1] the power and fuel consumption as a function of the flight altitude are determined. To substantiate this approaches, a 1-D ICE model based on the multi-physical simulation tool GT-Suite® has been created. This 1-D engine model offers the possibility to analyze the filling and gas change processes, the internal combustion as well as heat and friction losses for an ICE under altitude environmental conditions. Performance measurements on a dynamometer at sea level for a naturally aspirated ICE with a displacement of 1211 ccm used in an aviation aircraft has been done to validate the 1-D ICE model. To check the plausibility of the empirical approaches with respect to the fuel consumption and performance adjustment for the flight altitude an analysis of the ICE efficiency chain of the 1-D engine model is done. In addition, a comparison of literature and manufacturer data with the simulation results is presented.
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.
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.
This paper primarily presents an aerodynamic CFD analysis of a winged spaceplane geometry based on the Japanese Space Walker proposal. StarCCM was used to calculate aerodynamic coefficients for a typical space flight trajectory including super-, trans- and subsonic Mach numbers and two angles of attack. Since the solution of the RANS equations in such supersonic flight regimes is still computationally expensive, inviscid Euler simulations can principally lead to a significant reduction in computational effort. The impact on accuracy of aerodynamic properties is further analysed by comparing both methods for different flight regimes up to a Mach number of 4.
This paper presents a novel method for airfoil drag estimation at Reynolds numbers between 4×10⁵ and 4×10⁶. The novel method is based on a systematic study of 40 airfoils applying over 600 numerical simulations and considering natural transition. The influence of the airfoil thickness-to-chord ratio, camber, and freestream Reynolds number on both friction and pressure drag is analyzed in detail. Natural transition significantly affects drag characteristics and leads to distinct drag minima for different Reynolds numbers and thickness-to-chord ratios. The results of the systematic study are used to develop empirical correlations that can accurately predict an airfoil drag at low-lift conditions. The new approach estimates a transition location based on airfoil thickness-to-chord ratio, camber, and Reynolds number. It uses the transition location in a mixed laminar–turbulent skin-friction calculation, and corrects the skin-friction coefficient for separation effects. Pressure drag is estimated separately based on correlations of thickness-to-chord ratio, camber, and Reynolds number. The novel method shows excellent accuracy when compared with wind-tunnel measurements of multiple airfoils. It is easily integrable into existing aircraft design environments and is highly beneficial in the conceptual design stage.
Comparative assessment of parallel-hybrid-electric propulsion systems for four different aircraft
(2020)
As battery technologies advance, electric propulsion concepts are on the edge of disrupting aviation markets. However, until electric energy storage systems are ready to allow fully electric aircraft, the combination of combustion engine and electric motor as a hybrid-electric propulsion system seems to be a promising intermediate solution. Consequently, the design space for future aircraft is expanded considerably, as serial-hybrid-, parallel-hybrid-, fully-electric, and conventional propulsion systems must all be considered. While the best propulsion system depends on a multitude of requirements and considerations, trends can be observed for certain types of aircraft and certain types of missions. This paper provides insight into some factors that drive a new design towards either conventional or hybrid propulsion systems. General aviation aircraft, VTOL air taxis, transport aircraft, and UAVs are chosen as case studies. Typical missions for each class are considered, and the aircraft are analyzed regarding their take-off mass and primary energy consumption. For these case studies, a high-level approach is chosen, using an initial sizing methodology. Results indicate that hybrid-electric propulsion systems should be considered if the propulsion system is sized by short-duration power constraints (e.g. take-off, climb). However, if the propulsion system is sized by a continuous power requirement (e.g. cruise), hybrid-electric systems offer hardly any benefit.
Comparative assessment of parallel-hybrid-electric propulsion systems for four different aircraft
(2020)
Until electric energy storage systems are ready to allow fully electric aircraft, the combination of combustion engine and electric motor as a hybrid-electric propulsion system seems to be a promising intermediate solution. Consequently, the design space for future aircraft is expanded considerably, as serial hybrid-electric, parallel hybrid-electric, fully electric, and conventional propulsion systems must all be considered. While the best propulsion system depends on a multitude of requirements and considerations, trends can be observed for certain types of aircraft and certain types of missions. This Paper provides insight into some factors that drive a new design toward either conventional or hybrid propulsion systems. General aviation aircraft, regional transport aircraft vertical takeoff and landing air taxis, and unmanned aerial vehicles are chosen as case studies. Typical missions for each class are considered, and the aircraft are analyzed regarding their takeoff mass and primary energy consumption. For these case studies, a high-level approach is chosen, using an initial sizing methodology. Only parallel-hybrid-electric powertrains are taken into account. Aeropropulsive interaction effects are neglected. Results indicate that hybrid-electric propulsion systems should be considered if the propulsion system is sized by short-duration power constraints. However, if the propulsion system is sized by a continuous power requirement, hybrid-electric systems offer hardly any benefit.
In the friction tests between honeycomb with film adhesive and prepreg, the relative displacement occurs between the film adhesive and the prepreg. The film adhesive does not shift relative to the honeycomb. This is consistent with the core crush behavior where the honeycomb moves together with the film adhesive, as can be seen in Figure 2(a). The pull-through forces of the friction measurements between honeycomb and prepreg at 1 mm deformation are plotted in Figure 17(a). While the friction at 100°C is similar to the friction at 120°C, it decreases significantly at 130°C and exhibits a minimum at 140°C. At 150°C, the friction rises again slightly and then sharply at 160°C. Since the viscosity of the M18/1 prepreg resin drops significantly before it cures [23], the minimum friction at 140°C could result from a minimum viscosity of the mixture of prepreg resin and film adhesive before the bond subsequently cures. Figure 17(b) shows the mean value curve of the friction measurements at 140°C. The error bars, which represent the standard deviation, reveal the good repeatability of the tests. The force curve is approximately horizontal between 1 mm and 2 mm. The friction then slightly rises. As with interlaminar friction measurements, this could be due to the fact that resin is removed by friction and the proportion of boundary lubrication increases. Figure 18 shows the surfaces after the friction measurement. The honeycomb cell walls are clearly visible in the film adhesive. There are areas where the film adhesive is completely removed and the carrier material of the film adhesive becomes visible. In addition, the viscosity of the resin changes as the curing progresses during the friction test. This can also affect the force-displacement curve.