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A Stagnation Point Actuator is used to control the lateral dynamics of vortices generated over a sharp-pointed forebody, at high angles of attack, and the resulting rolling moment is studied. Effective roll control is demonstrated, including the ability to suppress the wing rock phenomenon. Piecewise-linear transfer functions are developed from experimental data for the changes in roll moment and pressure difference with actuator frequency content. These transfer functions are reduced to compact form in the frequency domain, and then to a time-domain model using 2 gains and 2 time scales. The roll response is classified according to angle of attack range. Some long time scales are observed in the surface pressure, velocity field and rolling moment, making the response relatively insensitive to speed. Thus over substantial speed ranges, linear transfer functions are shown to effectively describe the roll response to motion of the Stagnation Point Actuator.

Neural network augmented model inversion control is used to provide a civilian tilt-rotor aircraft with consistent response characteristics throughout its operating envelope, including conversion flight. The implemented response types are Attitude Command Attitude Hold in the longitudinal channel, and Rate Command Attitude Hold about the roll and yaw axes. This article describes the augmentation in the roll channel and the augmentation for the yaw motion including Heading Hold at low airspeeds and automatic Turn Coordination at cruise flight. Conventional methods require extensive gain scheduling with tilt-rotor nacelle angle and airspeed. A control architecture is developed that can alleviate this requirement and thus has the potential to reduce development time. It also facilitates the implementation of desired handling qualities, and permits compensation for partial failures.

This paper critically evaluates the use of Neural Networks (NNs) as metamodels for design applications. The specifics of implementing a NN approach are researched and discussed, including the type and architecture appropriate for design-related tasks, the processes of collecting training and validation data, and training the network, resulting in a sound process, which is described. This approach is then contrasted to the Response Surface Methodology (RSM). As illustrative problems, two equations to be approximated and a real-world problem from a Stability and Controls scenario, where it is desirable to predict the static longitudinal stability for a High Speed Civil Transport (HSCT) at takeoff, are presented. This research examines Response Surface Equations (RSEs) as Taylor series approximations, and explains their high performance as a proven approach to approximate functions that are known to be quadratic or near quadratic in nature.

Heterogeneous physical systems can often be considered as highly complex, consisting of a large number of subsystems and components, along with the associated interactions and hierarchies amongst them. The simulation of a large-scale, complex system can be computationally expensive and the dynamic interactions may be highly nonlinear. One approach to address these challenges is to increase the computing power or resort to a distributed computing environment. An alternative to improve the simulation computational performance and efficiency is to reduce CPU required time through the application of surrogate models. Surrogate modeling techniques for dynamic simulation models can be developed based on Recurrent Neural Networks (RNN).This study will present a method to improve the overall speed of a multi-physics time-domain simulation of a complex naval system using a surrogate modeling technique.

Test methods and data acquisition system specifications are described for measurements of the energy consumption of the control system of a servo-pump continuously variable transmission (CVT). Dynamic measurements of the power consumption of the servo-pump CVT control system show that the control system draws approximately 18.9 W-hrs of electrical energy over the HWFET cycle and 13.6 W-hrs over the 505 cycle. Sample results are presented of the dynamic power consumption of the servo-pump system under drive cycle conditions. Steady state measurements of the control power draw of the servo-pump CVT show a peak power consumption of 271 W, including lubrication power. The drive-cycle averaged and steady state energy consumption of the servo-pump CVT are compared to conventional CVT pump technologies.

The present use of the carburetor to supply fuel to the Otto cycle engine has placed it in a difficult competitive position with the diesel engine, which has successfully operated with a fuel injection system. The purpose of this study was to consider the feasibility of utilizing a low pressure injection system for the Otto cycle engine. The proposed design is discussed in detail. As the author points out, this system will allow design changes in the engine that would be impossible if the carburetor were retained, and thus considerable improvement in performance and efficiency can be realized for the Otto cycle engine.

Accurate modeling of the internal flow and spray characteristics in fuel injectors is a critical aspect of direct injection engine design. However, such high-fidelity computational fluid dynamics (CFD) models are often computationally expensive due to the requirement of resolving fine temporal and spatial scales. This paper addresses the computational bottleneck issue by proposing a machine learning-based emulator framework, which learns efficient surrogate models for spatiotemporal flow distributions relevant for static coupling injection maps, namely total void fraction, velocity, and mass, within a design space of interest. Different design points involving variations of needle lift, fuel viscosity, and level of non-condensable gas in the fuel were explored in this study. An interpretable Bayesian learning strategy was employed to understand the effect of the design parameters on the void fraction fields at the exit of the injector orifice.

An intelligent connected vehicle (ICV) swarm system that includes N vehicles is considered. Based on the special properties of potential functions, a kinematic model describing the swarm performances is proposed, which allows all vehicles to enclose the tracking target and show both tracking and formation characteristics. Treating the performances as the desired constraints, the analytical form of constraint forces can be obtained inspired by the Udwadia-Kalaba approaches. A special approach of uncertainty decomposition to deal with uncertain interferences is proposed, and a switching-type robust control method is addressed for each vehicle agent in the swarm system. The features and validity of the addressed control are demonstrated in the numerical simulations.