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.

An eddy-viscosity limited algebraic stress model for shock-boundary-layer interaction

Abstract: We present a μ t -limited explicit algebraic stress model for shock-wave-turbulent boundary-layer interactions based on an investigation of various two-equation turbulence models. The results of the κ-ω model, the shear stress transport (SST) model, and a quadratic explicit algebraic stress model are presented and analysed for the Delery channel bump, and the Bachalo and Johnson axisymmetric bump test cases. While the κ-ω model failed to give good predictions, the SST model proved reasonably successful in predicting strong interaction problems. The non-linear quadratic explicit algebraic stress model gave improved results (when compared with the original κ-ω model) for the channel bump test cases, but was not as good as the SST model. Inspired by this investigation, a model formulation is proposed in which Bradshaw's assumption for eddy-viscosity limiting (as used in the SST model) is applied to a quadratic explicit algebraic stress model. The QSST model further improves the SST model for strong shock-boundary-layer interactions

Computer-aided design of single- and two-variable analogue function generators

Abstract: Values for the resistances shunting sections of tapped potentiometers are rapidly computed from simple data using two alternative digital-computer programs which provide potentiometer-network designs, having a specified accuracy, for arbitrary monotonic and nonmonotonic functions.

Synthesized Trust Learning from Limited Human Feedback for Human-Load-Reduced Multi-Robot Deployments

Abstract: Human multi-robot system (MRS) collaboration is demonstrating potentials in wide application scenarios due to the integration of human cognitive skills and a robot team’s powerful capability introduced by its multi-member structure. However, due to limited human cognitive capability, a human cannot simultaneously monitor multiple robots and identify the abnormal ones, largely limiting the efficiency of the human-MRS collaboration. There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads. Human trust in human MRS collaboration reveals human expectations on robot performance. Based on trust estimation, the work between a human and MRS will be reallocated that an MRS will self-monitor and only request human guidance in critical situations. Inspired by that, a novel Synthesized Trust Learning (STL) method was developed to model human trust in the collaboration. STL explores two aspects of human trust (trust level and trust preference), meanwhile accelerates the convergence speed by integrating active learning to reduce human workload. To validate the effectiveness of the method, tasks "searching victims in the context of city rescue" were designed in an open-world simulation environment, and a user study with 10 volunteers was conducted to generate real human trust feedback. The results showed that by maximally utilizing human feedback, the STL achieved higher accuracy in trust modeling with a few human feedback, effectively reducing human interventions needed for modeling an accurate trust, therefore reducing human cognitive load in the collaboration.

Study on Splitting Tensile Mechanical Properties of Carbon Fiber/Polymer Latex Powder Composite Modified Concrete

Abstract: The effects of carbon fiber and polymer latex powder on the split tensile mechanical properties of concrete in single and compound mixing conditions are studied. The results show that both carbon fiber or polymer latex powder can improve the split tensile strength and peak strain of concrete. When the carbon fiber content is 0.2%, the split tensile mechanical properties of carbon fiber reinforced concrete (CFRC) are the best. When the amount is 8%, the mechanical properties of polymer modified concrete (PMC) in split tension are the best. When the carbon fiber content is 0.3% and the polymer latex powder content is 0.8%, the splitting tensile mechanical properties of the carbon fiber/polymer latex powder composite modified concrete (CFRPMC) specimens are the best, and are better than CFRC specimens and PMC specimens.