Browse Topic: Engines
To address the complexity of the influence of expansion-section geometry on the thrust performance of solid rocket motor nozzles and the low efficiency of numerical-simulation-based optimization, this study proposes a deep-learning-based method for rapid nozzle thrust prediction. With a representative 70-lb BATES nozzle as the research object, the expansion-section contour is parameterized using cubic spline functions. Combined with Latin hypercube sampling and two-dimensional inviscid numerical simulations, a nozzle geometry– thrust dataset covering a wide range of contour characteristics is constructed. On this basis, surrogate models based on a fully connected neural network, a one-dimensional convolutional neural network, and a two-dimensional convolutional neural network are developed, and the applicability of different network architectures to the nozzle thrust prediction task is systematically compared. The results demonstrate that convolutional neural networks outperform fully connected networks in terms of geometric feature representation capability and prediction stability. In particular, by explicitly modeling the spatial structural features of the nozzle contour, the two-dimensional convolutional model achieves a coefficient of determination close to unity on the test set, with an average relative error of 0.03%. Further hyperparameter sensitivity analysis clarifies the effects of batch size, number of training epochs, and learning rate on model convergence behavior and generalization performance. This work provides a feasible technical support for rapid thrust evaluation and efficient optimization-oriented nozzle design.
To achieve the key control objectives of thrust regulation and inlet protection for a rocket-based combined cycle (RBCC) engine under wide flight envelope, high-speed operating conditions, this study is based on a nonlinear component-level RBCC engine model developed in MATLAB/Simulink. A state space model is established using a least squares system identification method. By incorporating actuator dynamics, an augmented system model is formulated, and a Differential Evolution (DE) optimized Linear Quadratic Regulator (LQR) multivariable closed-loop control strategy is proposed. Simulation results show that the proposed strategy significantly improves both response speed and disturbance-rejection capability. Compared with the conventional LQR controller, the maximum oscillation of the inlet stability margin is reduced from 11% to 4.4%, and the thrust settling time is shortened by 83%. These results verify the feasibility of the proposed control method under complex operating conditions.
In recent years, global automotive manufacturers have been steadfastly engaged in the pursuit of advanced technical solutions to address the increasingly stringent noise pollution standards mandated by national and international regulatory authorities for their domestic and export markets respectively. Aligned with this overarching objective, the Engine Design and Development team at Maruti Suzuki India Limited (MSIL) has undertaken focused efforts to reduce powertrain-induced noise and vibration (NV). Recognizing that the engine unit’s principal static subsystems, namely the intake and exhaust assemblies and critical structural components such as the cylinder head (CH) and cylinder block (CB), serve as primary sources of acoustic emissions, special emphasis was placed on their refinement. Additionally, particular attention was directed toward the engine’s rotating and reciprocating components, especially the camshaft and crankshaft assemblies, to minimize overall measured sound pressure levels.
This paper addresses non-sustained ignition in numerical simulations of hydrogen/air turbulent diffusion flames. A reproducible LES-PaSR case study was constructed on the OpenFOAM platform, and a box-shaped ignition core was applied using setFields. The work is positioned as a numerical diagnostic and benchmark workflow, not as an experimentally validated prediction of a stable flame. The evidence chain, consisting of three diagnostic indicators - the domain-wide maximum temperature, centerline statistics along the ignition axis, and slices at critical moments - tracks the complete ignition-peak decay process. Calculations show that the domain-wide maximum temperature (Tmax) reaches a peak of 2852.5 K at t = 3.13×10^–4 s and then rapidly drops to 956.4 K at t = 0.0015 s. The duration of Tmax ≥ 2000 K is 0.818 ms, while Tmax ≥ 1000 K lasts for 1.388 ms. Comparison between the centerline and cross-sections further indicates that the high-temperature OH reaction zone is mainly limited to the ignition core and its downstream neighboring region. Under the combined effects of convective transport and turbulent mixing, this region rapidly cools and dissipates, failing to form a stable self-sustained exothermic structure. Finally, three physically plausible explanatory pathways and corresponding sensitivity-case designs are proposed, providing a practical basis for subsequent parameter scanning, mechanism replacement, and future comparison with experimental or literature-reported data. A supplementary sensitivity case with the ignition-kernel temperature increased from 2200 K to 2400 K exhibits a higher peak temperature and delayed threshold crossings, yet the reacting structure remains non-sustained within the simulated time window. Tmax ≥ 2000KTmax ≥ 1000K
Graphene materials have recently been shown to produce measurable propulsion when exposed to light, raising the possibility of using them to enhance solar sail performance for satellite deorbiting. The pendulum-based experiments on graphene samples under controlled laser illumination inside a vacuum chamber and a quantitative model that describes the propulsion force as a function of laser power, illuminated area, incident angle, and ambient pressure were carried out. The measured pressure dependence and its shift with sample characteristic size point to Knudsen forces as the dominant mechanism. Orbital dynamics simulations incorporating this model show that a graphene-based sail can deorbit a 100 kg satellite from 660 km altitude roughly 21% faster than a conventional membrane sail of the same area, because the graphene configuration combines atmospheric drag with light-induced propulsion in a hybrid decay process.
In response of the stringent requirements of high-resolution optical remote sensing satellites for attitude control stability and micro-vibration environment, this study focuses on the friction dynamics modeling and simulation analysis of large-angular-momentum flywheels across a wide speed range. To quantify the impact of flywheel friction disturbances on satellite attitude pointing accuracy, friction torque characteristic data were first acquired through ground testing over the full speed range, and a high-fidelity static fitting mathematical model was constructed using the least squares method. To further elucidate the nonlinear friction mechanisms in the low-speed regime—particularly during speed zero-crossings—the LuGre dynamic friction model was introduced, and its core parameters were identified and optimized. Comparative results demonstrate that the model exhibits high-precision characterization capabilities, with a deviation of less than 0.0015 Nm from measured data within the critical low-speed range of ±50 rpm. Subsequently, the established friction model was integrated into a satellite attitude control closed-loop system for digital simulation to quantitatively evaluate the disturbance of friction torque on system steady-state accuracy. The results indicate that under the closed-loop control compensation mechanism, the impact of flywheel friction on satellite attitude pointing accuracy is effectively suppressed, with the attitude angle deviation maintained within 0.002°, thereby meeting the engineering requirements for high-precision remote sensing imaging. This research provides a theoretical basis and data support for the optimization design of flywheel systems and the refined design of satellite attitude controllers.
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