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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.
Innovative interplanetary deep space missions, like a main belt asteroid sample
return mission, require ever larger velocity increments (∆V s) and thus ever
more demanding propulsion capabilities. Providing much larger exhaust velocities than chemical high-thrust systems, electric low-thrust space-propulsion
systems can significantly enhance or even enable such high-energy missions. In
1995, a European-Russian Joint Study Group (JSG) presented a study report
on “Advanced Interplanetary Missions Using Nuclear-Electric Propulsion”
(NEP). One of the investigated reference missions was a sample return (SR)
from the main belt asteroid (19) Fortuna. The envisaged nuclear power plant,
Topaz-25, however, could not be realized and also the worldwide developments
in space reactor hardware stalled. In this paper, we investigate, whether such
a mission is also feasible using a solar electric propulsion (SEP) system and
compare our SEP results to corresponding NEP results.
A melting probe equipped with autofluorescence-based detection system combined with a light scattering unit, and, optionally, with a microarray chip would be ideally suited to probe icy environments like Europa’s ice layer as well as the polar ice layers of Earth and Mars for recent and extinct live.
Manufacturing process simulation enables the evaluation and improvement of autoclave mold concepts early in the design phase. To achieve a high part quality at low cycle times, the thermal behavior of the autoclave mold can be investigated by means of simulations. Most challenging for such a simulation is the generation of necessary boundary conditions. Heat-up and temperature distribution in an autoclave mold are governed by flow phenomena, tooling material and shape, position within the autoclave, and the chosen autoclave cycle. This paper identifies and summarizes the most important factors influencing mold heat-up and how they can be introduced into a thermal simulation. Thermal measurements are used to quantify the impact of the various parameters. Finally, the gained knowledge is applied to develop a semi-empirical approach for boundary condition estimation that enables a simple and fast thermal simulation of the autoclave curing process with reasonably high accuracy for tooling optimization.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
A multi-functional device applying for the safe maintenance at high-altitude on wind turbines
(2015)
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.
Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan (distributed electric propulsion), leading to highly flexible dynamic systems that can exhibit aeroelastic instabilities. This paper introduces a validated methodology to investigate the aeroelastic instabilities of wing–propeller systems and to understand the dynamic mechanism leading to wing and whirl flutter and transition from one to the other. Factors such as nacelle positions along the wing span and chord and its propulsion system mounting stiffness are considered. Additionally, preliminary design guidelines are proposed for flutter-free wing–propeller systems applicable to novel aircraft designs. The study demonstrates how the critical speed of the wing–propeller systems is influenced by the mounting stiffness and propeller position. Weak mounting stiffnesses result in whirl flutter, while hard mounting stiffnesses lead to wing flutter. For the latter, the position of the propeller along the wing span may change the wing mode shapes and thus the flutter mechanism. Propeller positions closer to the wing tip enhance stability, but pusher configurations are more critical due to the mass distribution behind the elastic axis.
The impact of wake model effects is investigated for two highly
non-planar lifting systems. Dependent on the geometrical
arrangement of the configuration, the wake model shape is found
to considerably affect the estimation. Particularly at higher angles
of attack, an accurate estimation based on the common linear wake
model approaches is involved.
To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.
In the chapter “Son of Concorde, a Technology Challenge” one of the new challenges for a Supersonic Commercial Transport (SCT) is multi-point design for the four main design points:
- supersonic cruise
- transonic cruise
- take-off and landing
- transonic acceleration.
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.
The development of Gossamer sail structures for solar sails contributes to a large field of future space applications like thin film solar generators, membrane antennas and drag sails. The focus of this paper is the development of a drag sail based on solar sail technology that could contribute to a reduction of space debris in low Earth orbits. The drag sail design and its connections to solar sail development, a first test on a sounding rocket, as well as the ongoing integration of the drag sail into a triple CubeSat is presented.
In this paper, an approach to propulsion system modelling for hybrid-electric general aviation aircraft is presented. Because the focus is on general aviation aircraft, only combinations of electric motors and reciprocating combustion engines are explored. Gas turbine hybrids will not be considered. The level of the component's models is appropriate for the conceptual design stage. They are simple and adaptable, so that a wide range of designs with morphologically different propulsive system architectures can be quickly compared. Modelling strategies for both mass and efficiency of each part of the propulsion system (engine, motor, battery and propeller) will be presented.
An Interstellar – Heliopause mission using a combination of solar/radioisotope electric propulsion
(2011)
There is common agreement within the scientific community that in order to understand our local galactic environment it will be necessary to send a spacecraft into the region beyond the solar wind termination shock. Considering distances of 200 AU for a new mission, one needs a spacecraft travelling at a speed of close to 10 AU/yr in order to keep the mission duration in the range of less than 25 yrs, a transfer time postulated by ESA.Two propulsion options for the mission have been proposed and discussed so far: the solar sail propulsion and the ballistic/radioisotope electric propulsion. As a further alternative, we here investigate a combination of solar-electric propulsion and radioisotope-electric propulsion. The solar-electric propulsion stage consists of six 22 cm diameter “RIT-22”ion thrusters working with a high specific impulse of 7377 s corresponding to a positive grid voltage of 5 kV. Solar power of 53 kW BOM is provided by a light-weight solar array. The REP-stage consists of four space-proven 10 cm diameter “RIT-10” ion thrusters that will be operating one after the other for 9 yrs in total. Four advanced radioisotope generators provide 648 W at BOM. The scientific instrument package is oriented at earlier studies. For its mass and electric power requirement 35 kg and 35 W are assessed, respectively. Optimized trajectory calculations, treated in a separate contribution, are based on our “InTrance” method.The program yields a burn out of the REP stage in a distance of 79.6 AU for a usage of 154 kg of Xe propellant. With a C3 = 45,1 (km/s)2 a heliocentric probe velocity of 10 AU/yr is reached at this distance, provided a close Jupiter gravity assist adds a velocity increment of 2.7 AU/yr. A transfer time of 23.8 yrs results for this scenario requiring about 450 kg Xe for the SEP stage, jettisoned at 3 AU. We interpret the SEP/REP propulsion as a competing alternative to solar sail and ballistic/REP propulsion. Omiting a Jupiter fly-by even allows more launch flexibility, leaving the mission duration in the range of the ESA specification.
An overview on dry low NOx micromix combustor development for hydrogen-rich gas turbine applications
(2019)
We present a new approach to the problem of optimal control of solar sails for low-thrust trajectory optimization. The objective was to find the required control torque magnitudes in order to steer a solar sail in interplanetary space. A new steering strategy, controlling the solar sail with generic torques applied about the spacecraft body axes, is integrated into the existing low-thrust trajectory optimization software InTrance. This software combines artificial neural networks and evolutionary algorithms to find steering strategies close to the global optimum without an initial guess. Furthermore, we implement a three rotational degree-of-freedom rigid-body attitude dynamics model to represent the solar sail in space. Two interplanetary transfers to Mars and Neptune are chosen to represent typical future solar sail mission scenarios. The results found with the new steering strategy are compared to the existing reference trajectories without attitude dynamics. The resulting control torques required to accomplish the missions are investigated, as they pose the primary requirements to a real on-board attitude control system.