Vaughn College of Aeronautics and Technology

About: Vaughn College of Aeronautics and Technology is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Gravitational microlensing & Planetary system. The organization has 727 authors who have published 708 publications receiving 14082 citations. The organization is also known as: College of Aeronautics.

Transient behavior of compressed magnetorheological brake excited by step currents.

Abstract: Transient behavior of a magnetorheological brake excited by step currents under compression-shear mode has been experimentally studied. The results show that the amplitude of the applied current had little effect on the rising time of transient torque, while the rising time was significantly affected by the rotational speed, the compressive speed and the compressive strain position. The falling time of transient torque was independent of the amplitude of the applied current, the compressive speed and the compressive strain position, and it was affected by the rotational speed. The falling time of the transient torque was much shorter than the rising time by a step current. The transient process of MR brake applied as a step current was different from a stable process pre-applied at constant current in different particle chain structure forming processes. In addition, the compressive processes applied in one step current and randomly on/off current were compared and experimentally verified: the particle chains in two processes both experienced the same evolutionary of transient torque. The results achieved in this study should be properly considered in the design and control of magnetorheological brake under compression-shear mode.

Research on the splitting tensile properties of basalt fiber reinforced concrete under impact loading

Abstract: The impact splitting tensile tests of basalt fiber reinforced concrete(BFRC) using flattened Brazilian disc samples were performed with split Hopkinson pressure bar(SHPB)system of 100 mm diameter.The splitting tensile characteristics of basalt fiber reinforced concrete under impact loading were investigated.The test results showed that the static splitting tensile strength and static compressive strength of basalt fiber reinforced concrete increase first and then decreased with the increase of fiber volumetric fraction.With the rising of impact velocity,the impact splitting tensile strength and impact splitting tensile toughness of basalt fiber reinforced concrete increased constantly,the impact strengthening effect was obvious.The addition of basalt fiber can effectively improve the splitting tensile properties of basalt fiber reinforced concrete and increase it's impact splitting tensile strength and impact splitting tensile toughness compared with plain concrete under the same impact velocity.The relatively best volumetric fraction of basalt fiber was 0.2% based on the test condition and mix proportions.

Design Philosophy of Man Powered Aircraft

Abstract: In 1961 I assisted Mr. G. M. Lilley in writing a survey of the design and performance of man powered aircraft. Our conclusions were that man powered flight is just possible if the aircraft is carefully designed to give excellent aerodynamic characteristics, with a stiff, light structure and flown by a pilot whose power output is comparable with that of a National Amateur Cycling Champion. Such a machine would not fly more than fifteen to twenty feet above the ground and could operate only in still air conditions. The type of aircraft which emerged from the calculations was similar in general principles to those being developed currently at Hatfield and Southampton. In this lecture I propose to investigate some of the basic relationships which are involved in the hope that possible ways of obtaining further improvements may emerge. Since the conclusion of our survey was that flight was marginally possible, slight improvements might well go a long way towards making man powered flight possible.

Maneuvering decision in short range air combat for unmanned combat aerial vehicles

Abstract: In order to the requirement of flexibility and real-time for maneuvering decision in short range air combat, a maneuvering decision model in short range air combat based on reinforcement learning is presented. The model has two characteristics, one is select the situational evaluation function to build the state space, what make the discrete state space get more representative. The other is use the thinking of Monte-Carlo reinforcement learning, the result of air combat was used as the basis for returning the reward, what ensure the continuity of maneuver movement. The model is use the control valve as the decision result and the decision time is less than 0.001 seconds. At last, the feasibility of the decision model was verified by two different experiments.

A deep reinforcement learning approach to seat inventory control for airline revenue management

Abstract: Commercial airlines use revenue management systems to maximize their revenue by making real-time decisions on the booking limits of different fare classes offered in each of its scheduled flights. Traditional approaches—such as mathematical programming, dynamic programming, and heuristic rule-based decision models—heavily rely on external mathematical models of demand and passenger arrival, choice, and cancelation, making their performance sensitive to the accuracy of these model estimates. Moreover, many of these approaches scale poorly with increase in problem dimensionality. Additionally, they lack the ability to explore and “directly” learn the true market dynamics from interactions with passengers and adapt to changes in market conditions on their own. To overcome these limitations, this research uses deep reinforcement learning (DRL), a model-free decision-making framework, for finding the optimal policy of the seat inventory control problem. The DRL framework employs a deep neural network to approximate the expected optimal revenues for all possible state-action combinations, allowing it to handle the large state space of the problem. Multiple fare classes with stochastic demand, passenger arrivals, and booking cancelations have been considered in the problem. An air travel market simulator was developed based on the market dynamics and passenger behavior for training and testing the agent. The results demonstrate that the DRL agent is capable of learning the optimal airline revenue management policy through interactions with the market, matching the performance of exact dynamic programming methods. The revenue generated by the agent in different simulated market scenarios was found to be close to the maximum possible flight revenues and surpass that produced by the expected marginal seat revenue-b (EMSRb) method.