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Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.
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.
High aerodynamic efficiency requires propellers with high aspect ratios, while propeller sweep potentially reduces noise. Propeller sweep and high aspect ratios increase elasticity and coupling of structural mechanics and aerodynamics, affecting the propeller performance and noise. Therefore, this paper analyzes the influence of elasticity on forward-swept, backward-swept, and unswept propellers in hover conditions. A reduced-order blade element momentum approach is coupled with a one-dimensional Timoshenko beam theory and Farassat's formulation 1A. The results of the aeroelastic simulation are used as input for the aeroacoustic calculation. The analysis shows that elasticity influences noise radiation because thickness and loading noise respond differently to deformations. In the case of the backward-swept propeller, the location of the maximum sound pressure level shifts forward by 0.5 °, while in the case of the forward-swept propeller, it shifts backward by 0.5 °. Therefore, aeroacoustic optimization requires the consideration of propeller deformation.
Next-generation aircraft designs often incorporate multiple large propellers attached along the wingspan. These highly flexible dynamic systems can exhibit uncommon aeroelastic instabilities, which should be carefully investigated to ensure safe operation. The interaction between the propeller and the wing is of particular importance. It is known that whirl flutter is stabilized by wing motion and wing aerodynamics. This paper investigates the effect of a propeller onto wing flutter as a function of span position and mounting stiffness between the propeller and wing. The analysis of a comparison between a tractor and pusher configuration has shown that the coupled system is more stable than the standalone wing for propeller positions near the wing tip for both configurations. The wing fluttermechanism is mostly affected by the mass of the propeller and the resulting change in eigenfrequencies of the wing. For very weak mounting stiffnesses, whirl flutter occurs, which was shown to be stabilized compared to a standalone propeller due to wing motion. On the other hand, the pusher configuration is, as to be expected, the more critical configuration due to the attached mass behind the elastic axis.
Flexible fuel operation of a Dry-Low-NOx Micromix Combustor with Variable Hydrogen Methane Mixture
(2022)
The role of hydrogen (H2) as a carbon-free energy carrier is discussed since decades for reducing greenhouse gas emissions. As bridge technology towards a hydrogen-based energy supply, fuel mixtures of natural gas or methane (CH4) and hydrogen are possible.
The paper presents the first test results of a low-emission Micromix combustor designed for flexible-fuel operation with variable H2/CH4 mixtures. The numerical and experimental approach for considering variable fuel mixtures instead of recently investigated pure hydrogen is described.
In the experimental studies, a first generation FuelFlex Micromix combustor geometry is tested at atmospheric pressure at gas turbine operating conditions corresponding to part- and full-load. The H2/CH4 fuel mixture composition is varied between 57 and 100 vol.% hydrogen content.
Despite the challenges flexible-fuel operation poses onto the design of a combustion system, the evaluated FuelFlex Micromix prototype shows a significant low NOx performance
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).
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 traveling 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 European Space Agency (ESA). Two propulsion options for the mission have been proposed and discussed so far: the solar sail propulsion and the ballistic/radioisotope-electric propulsion (REP). As a further alternative, we here investigate a combination of solar-electric propulsion (SEP) and REP. The SEP stage consists of six 22-cms 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 at begin of mission (BOM) is provided by a lightweight solar array.
The scientific interest for near-Earth asteroids as well as the interest in potentially hazardous asteroids from the perspective of planetary defense led the space community to focus on near-Earth asteroid mission studies. A multiple near-Earth asteroid rendezvous mission with close-up observations of several objects can help to improve the characterization of these asteroids. This work explores the design of a solar-sail spacecraft for such a mission, focusing on the search of possible sequences of encounters and the trajectory optimization. This is done in two sequential steps: a sequence search by means of a simplified trajectory model and a set of heuristic rules based on astrodynamics, and a subsequent optimization phase. A shape-based approach for solar sailing has been developed and is used for the first phase. The effectiveness of the proposed approach is demonstrated through a fully optimized multiple near-Earth asteroid rendezvous mission. The results show that it is possible to visit five near-Earth asteroids within 10 years with near-term solar-sail technology.