Investigating the Feasibility of Integrating Pavement Friction and Texture Depth Data in Modeling for INDOT PMS

Under INDOT’s current friction testing program, the friction is measured annually on interstates but only once every three years on non-interstate roadways. The state’s Pavement Management System, however, would require current data if friction were to be included in the PMS. During routine pavement condition monitoring for the PMS, texture data is collected annually. This study explored the feasibility of using this pavement texture data to estimate the friction during those years when friction is not measured directly. After multi0ple approaches and a wide variety of ways of examining the currently available data and texture measuring technologies, it was determined that it is not currently feasible to use the texture data as a surrogate for friction testing. This is likely because the lasers used at this time are not capable of capturing the small-scale pavement microtexture. This situation may change, however, with advances in laser or photo interpretation technologies and improved access to materials data throughout the INDOT pavement network

Resource-Efficient Chemistry on Quantum Computers with the Variational Quantum Eigensolver and the Double Unitary Coupled-Cluster Approach.

Applications of quantum simulation algorithms to obtain electronic energies of molecules on noisy intermediate-scale quantum (NISQ) devices require careful consideration of resources describing the complex electron correlation effects. In modeling second-quantized problems, the biggest challenge confronted is that the number of qubits scales linearly with the size of the molecular basis. This poses a significant limitation on the size of the basis sets and the number of correlated electrons included in quantum simulations of chemical processes. To address this issue and enable more realistic simulations on NISQ computers, we employ the double unitary coupled-cluster (DUCC) method to effectively downfold correlation effects into the reduced-size orbital space, commonly referred to as the active space. Using downfolding techniques, we demonstrate that properly constructed effective Hamiltonians can capture the effect of the whole orbital space in small-size active spaces. Combining the downfolding preprocessing technique with the variational quantum eigensolver, we solve for the ground-state energy of H2, Li2, and BeH2 in the cc-pVTZ basis using the DUCC-reduced active spaces. We compare these results to full configuration-interaction and high-level coupled-cluster reference calculations

Neural network contour error prediction of a bi-axial linear motor positioning system

In the article a method of predicting contour error using artificial neural network for a bi-axial positioning system is presented. The machine consists of two linear stages with permanent magnet linear motors controlled by servo drives. The drives are controlled from a PC with real-time operating system via EtherCAT fieldbus. A randomly generated Non-Uniform Rational B-Spline (NURBS) trajectory is used to train offline a NARX-type artificial neural network for each axis. These networks allow prediction of following errors and contour errors of the motion trajectory. Experimental results are presented that validate the viability of the neural network based contour error prediction. The presented contour error predictor will be used in predictive control and velocity optimization algorithms of linear motor based CNC machines