A Quasi-Dimensional Fuel Distribution Model for a Radially Stratified Engine

2021 ◽  
Author(s):  
Dennis Robertson ◽  
Patrick O'Donnell ◽  
Benjamin Lawler ◽  
Robert Prucka
Keyword(s):  
Reference Model ◽  
Computation Time ◽  
Distribution Model ◽  
Strategy Development ◽  
Real Time Control ◽  
Spray Penetration ◽  
Fuel Distribution ◽  
Time Control ◽  
Fuel Stratification ◽  
Execution Speed

Abstract Several combustion strategies leverage radial fuel stratification to adapt combustion performance between the center of the chamber and the outer regions independently. Spark-assisted compression ignition (SACI) relies on careful tuning of this radial stratification to maximize the combined performance of flame propagation and autoignition. Established techniques for determining in-cylinder fuel stratification are computationally intensive, limiting their feasibility for control strategy development and real-time control. A simplified model for radial fuel stratification is developed for control-oriented objectives. The model consists of three submodels: spray penetration, fuel distribution along the spray axis, and post-injection mixing. The spray penetration model is adapted from fuel spray models presented in the literature. The fuel distribution and mixing submodels are validated against injection spray results from an LES 3-D computational fluid dynamics (CFD) reference model for three test points as a function of crank angle. The quasi-one-dimensional model matches the CFD results with a root mean square error (RMSE) for equivalence ratio of 0.08?0.11. This is a 50% reduction from the 0.16?0.20 RMSE for a model that assumes a uniform fuel distribution immediately after injection. The computation time is 230 ms on an Intel Xeon E5-1620 v3 to solve each case without significant optimization for code execution speed.

Determination of the Minimum Distance Between Moving Objects Including Velocity Information

1993 ◽  
Author(s):  
Meyer Nahon
Keyword(s):  
Minimum Distance ◽  
Moving Objects ◽  
Rapid Determination ◽  
Computation Time ◽  
Optimization Techniques ◽  
Real Time Control ◽  
Time Step ◽  
Time Control ◽  
Velocity Information

Abstract The rapid determination of the minimum distance between objects is of importance in collision avoidance for a robot maneuvering among obstacles. Currently, the fastest algorithms for the solution of this problem are based on the use of optimization techniques to minimize a distance function. Furthermore, to date this problem has been approached purely through the position kinematics of the two objects. However, although the minimum distance between two objects can be found quickly on state-of-the-art hardware, the modelling of realistic scenes entails the determination of the minimum distances between large numbers of pairs of objects, and the computation time to calculate the overall minimum distance between any two objects is significant, and introduces a delay which has serious repercussions on the real-time control of the robot. This paper presents a technique to modify the original optimization problem in order to include velocity information. In effect, the minimum distance calculation is performed at a future time step by projecting the effect of present velocity. This method has proven to give good results on a 6-dof robot maneuvering among obstacles, and has allowed a complete compensation of the lags incurred due to computational delays.

Control Design for Automated Vehicle Emergency Safe Stop System Based on Differential Dynamic Programming

2020 ◽  
Author(s):  
Lisheng Yang ◽  
Tomonari Furukawa ◽  
Lei Zuo ◽  
Zachary Doerzaph
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Control Algorithm ◽  
Computation Time ◽  
Grid Cell ◽  
System Control ◽  
Real Time Control ◽  
Differential Dynamic Programming ◽  
Time Control ◽  
Emergency Stop

Abstract This paper presents the control algorithm and system design for a newly proposed automated emergency stop system, which aims to navigate the vehicle out of its travel lane to a safe road-side location when an emergency (e.g. driver fails to take control during fallback of the Dynamic Driving Task) occurs. To address the unique requirements of such a system, control techniques based on differential dynamic programming are developed. Optimal control sequence computation is broken down into step-by-step quadratic optimization and solved iteratively. Control constraints are addressed efficiently by a tailored Projected-Newton algorithm. The iterative control algorithm is then integrated into a real-time control system which considers both computation delay and modeling errors. The system employs a novel grid-based storage structure for recording all acceptable control commands computed within the iteration and uses a high frequency estimator for self-localization. During operation, the real-time control thread will extract commands from the grid cell corresponding to current states. Simulation results show strong potential of the proposed system for addressing the engineering challenges of the automated emergency stop function. The robustness of the system in presence of computation time delay and modelling errors is also demonstrated.

Java-Based Tools for Development and Diagnosis of Real-Time Control Systems

1998 ◽  
Author(s):  
Will Shackleford ◽  
Fredrick M. Proctor
Keyword(s):  
Control System ◽  
Control Systems ◽  
Real Time ◽  
Reference Model ◽  
Real Time Control ◽  
Building Control ◽  
The Real ◽  
Time Control ◽  
Control Modules ◽  
Work Done

Abstract The National Institute of Standards and Technology (NIST) has been using the Real-time Control System (RCS) Reference Model Architecture for building control systems based on a hierarchy of cyclically executing control modules. This paper describes the work done to build Java tools that allow developers to lay out their hierarchy according to RCS tenets and view or change the inputs and outputs of each module at run-time.

Development of a semi-automated model identification and calibration tool for conceptual modelling of sewer systems

2013 ◽  
Vol 68 (1) ◽  
pp. 167-175 ◽  
Author(s):  
Vincent Wolfs ◽  
Mauricio Florencio Villazon ◽  
Patrick Willems
Keyword(s):  
Model Identification ◽  
Computation Time ◽  
Conceptual Modelling ◽  
Storm Events ◽  
Real Time Control ◽  
Design Storm ◽  
Time Control ◽  
Long Time ◽  
Modelling Methodology ◽  
Time Required

Applications such as real-time control, uncertainty analysis and optimization require an extensive number of model iterations. Full hydrodynamic sewer models are not sufficient for these applications due to the excessive computation time. Simplifications are consequently required. A lumped conceptual modelling approach results in a much faster calculation. The process of identifying and calibrating the conceptual model structure could, however, be time-consuming. Moreover, many conceptual models lack accuracy, or do not account for backwater effects. To overcome these problems, a modelling methodology was developed which is suited for semi-automatic calibration. The methodology is tested for the sewer system of the city of Geel in the Grote Nete river basin in Belgium, using both synthetic design storm events and long time series of rainfall input. A MATLAB/Simulink® tool was developed to guide the modeller through the step-wise model construction, reducing significantly the time required for the conceptual modelling process.

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