Deviation of light path in Born-Infeld type gravitational field

The discovery of ＂twin quasi-stellar objects＂ arose interests among astronomers and astrophysicists to study gravitational leasing problem. Deviation of light from straight path is caused by the presence of massive objects, i.e., the presence of gravitational field according to the general theory of relativity. It is shown that the low energy effective field theory on D-branes is of the Born-Infeld type. In this work a Born-Infeld type gravitational field is pasttflated. An explicit representation of the angular deviation of light path is derived based on the space time metric in the Born-Infeld theory.

ON FATIGUE CRACK PATH DEVIATION AT ELEVATED TEMPERATURE IN ELECTRON BEAM REPAIRED WELDMENTS OF TURBINE DISK

Fatigue crack growth behaviors in electron beam weldments of a nickel-base superalloy are studied. The objective of this paper is to discuss effects of the inhomogeneity of mechanical performance on fatigue crack growth (FCG) rate and crack path deviation (CPD). The base metal served in a turbine disk of aerospace engine was selected to fabricate bead-on-plate weldments by using electron beam welding. Some wedge-type opening loading specimens, notched in three different zone of weld metal, HAZ and base metal, were employed and performed fatigue crack growth tests at 650℃. The results show that the fatigue crack growth of electron beam welded joints is instable due to the influence of mechanical heterogeneities. Owing to the crack deviation at the weld metal and heat-affected-zone (HAZ), the effective growth driving force at the tip of fatigue crack was reduced with the reduction of the effective stress intensity factor (SIF) which finally causes fatigue crack rate decrease. Fatigue crack was strongly affected by size and the symmetrical characteristics of the plastic zone at the crack tip, which means that the integrity of the welded structure containing the fatigue crack mainly depended on the toughness of the low strength zone.

Knowledge and data jointly driven aeroengine gas path performance assessment method

Aeroengines,as the sole power source for aircraft,play a vital role in ensuring flight safety.The gas path,which represents the fundamental pathway for airflow within an aeroengine,directly impacts the aeroengine's performance,fuel efficiency,and safety.Therefore,timely and accurate evaluation of gas path performance is of paramount importance.This paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method,combining Fingerprint and gas path parameter deviation values.Firstly,Fingerprint is used to correct gas path parameter deviation values,eliminating parameter shifts caused by non-component performance degradation.Secondly,coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding window.Thirdly,an Ensemble Empirical Mode Decomposition and Non-Local Means(EEMD-NLM)filtering method is designed to“clean”data noise,completing the preprocessing for gas path parameter deviation values.Afterward,based on the characteristics of gas path parameter deviation values,a Dynamic Temporary Blended Network(DTBN)model is built to extract its temporal features,cascaded with Multi-Layer Perceptron(MLP),and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder(DTB-AutoEncoder).Eventually,by training this improved autoencoder,the aeroengine gas path multi-component performance assessment model is formed,which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values,thereby achieving the performance assessment of engine gas path components.Through practical application cases,the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.

An Intelligent Multi-robot Path Planning in a Dynamic Environment Using Improved Gravitational Search Algorithm

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm(IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization(PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution(DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.