INFLUENCE OF ALLOYING ELEMENTS ON THE MICROSTRUCTURE OF NEW COBALT MATRIX SUPERALLOYS REINFORCED WITH PHASE – L12

The article presents selected results of research on the primary structure of a new generation of cobalt-based Co-20Ni-10Al-5Mo-2Nb superalloys. Research on this group of materials was started in 2006 by J. Sato. These materials are anticipated to be able to replace nickel-based superalloys in the future due to their superior elevated temperature properties compared to the nickel-based superalloys. Today, γ’ nickel-based superalloys are still unrivalled in aerospace applications, however, cobalt-based superalloys are a response to their existing limitations, which do not allow the current pace of aircraft engine development to be maintained.

Experimental Investigation of a Radial Wave Engine Utilizing a Rotary Valved Pressure Gain Combustor

In this paper a deflagrative pressure gain combustor employing rotary valves is introduced that can reduce combustor length by allowing the combustion process to take place in the radial direction rather than in the axial direction with sufficient resident time. The engine is described in some detail with respect to geometry, components, and potential benefits. A demonstrator rig was designed, built, and tested for concept verification. A series of initial tests sweeping equivalence ratio from 0.4 to 0.8 using ethylene as fuel and mass flows from 75 g/s to 150 g/s is presented. The turbine rotor was driven to above 200 RPM with just four of the combustor passages operating. The initial results indicate the feasibility of the rotary valve radial engine concept and identified key challenges associated with the concept. Further studies are needed address existing challenges and to promote the engine development.

Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine

The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.