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The quest for clean, efficient engine technologies is imperative in reducing transportation’s environmental impact. Hydrogen, as a zero-emission fuel, offers significant potential for internal combustion engines but faces challenges such as optimizing engine performance and longevity. Water injection is proposed as a solution, yet its effects on engine performance require thorough investigation. This study bridges the knowledge gap by examining various water injection ratios (WIRs) and their impact on engine performance, focusing on the balance between power output and engine longevity. We identified the existence of an optimum WIR (e.g., 10% in this study), which provides peak performance with minimal adverse effects on engine performance and health. Computational simulations of a single-cylinder engine revealed how WIRs influence in-cylinder temperature, pressure, and IMEP, emphasizing the nuanced benefits of water injection. Additionally, our analysis of turbulence, through TKE and dissipation rate, deepens the understanding of combustion and fuel efficiency in hydrogen engines. This research provides valuable guidance for optimizing engine operations and paves the way for advanced water injection systems in hydrogen engines, marking a significant step towards cleaner engine technology.

Desalination and atmospheric water harvesting technologies are highly desirable to produce freshwater for daily life activities and alleviate the global water crisis. Efforts to improve these have mostly been based on better engineering or materials design, but a comparison of their energy performance over a theoretical optimum is not well consolidated. This research conducts a meta-analysis that comparatively assesses existing atmospheric water harvesting and desalination technologies by evaluating the energy optimality in terms of the Gibbs free energy principle derived theoretical limit. After a review of the various existing technologies in these two classes, energy optimality, defined as the theoretical minimum specific energy consumption divided by the specific exergy consumption, is used as the metric to make a comprehensive and fair comparison of the various desalination and atmospheric water harvesting technologies. Results show that the vapor compression cycle and hybrid technologies-based atmospheric water harvesters have higher energy optimality of 12%, whereas others have much poorer performances of under 3%. For desalination, reverse osmosis yielded the highest energy optimality of 67.43%. Furthermore, the ideal energy optimality needed by atmospheric water harvesting to become comparable to desalination is at least 89.9%, which is almost impossible to practically achieve.

This paper presents a new adaptive small chattering sliding mode control (SCSMC) scheme that uses reinforcement learning (RL) and time-delay estimation (TDE) for the motion control of free-floating space robotic manipulators (FSRM) subject to model uncertainty and external disturbance. The proposed sliding mode control scheme can achieve small chattering effects and improve the tracking accuracy by using a new adaptive law for the switching gain and a RL-based robust term to handle the control inputs. In SCSMC, the complicated multiple-input-multiple-output (MIMO) uncertain system of FSRM is transformed into multiple single-input-single-output (SISO) known subsystems with bounded estimation errors by the TDE technique and state feedback compensation. Subsequently, once the sliding variable is inside the designed manifold, the derivative of the switching gain for each subsystem becomes a negative hyperbolic tangent function of the associated sliding variable, which offers the ability to reduce chattering by decreasing the switching gain. Moreover, the RL based robust term for each subsystem is designed to avoid the loss of tracking accuracy caused by the aforementioned switching gain drop. The tracking errors are proven to be uniformly-ultimately-bounded (UUB) with an arbitrarily small bound by using the Lyapunov theory. The effectiveness of the proposed control scheme is verified by numerical simulations.

This paper presents a large-scale Domain Decomposition Method (DDM) based Direct Numerical Simulation (DNS) for predicting the behavior of indoor airflow, where the aim is to design a comfortable and efficient indoor air environment of modern buildings. An analogy of the single-phase convection problems is applied, and the pressure stabilized domain decomposition method is used to symmetrize the linear systems of Navier-Stokes equations and the convection-diffusion equation. Furthermore, a balancing preconditioned conjugate gradient method is utilized to deal with the interface problem caused by domain decomposition. The entire simulation model is validated by comparing the numerical results with that of recognized experimental and numerical data from previous literature. The transient behavior of indoor airflow and its complexity in the ventilated room are discussed; the velocity and vortex distribution of airflow are investigated, and its possible influence on particle accumulation is classified.