Simulation-Based Evaluation of Variation in Left-Turn Paths in the Coordinated Intersection Management

Coordinated intersection management (CIM) has gained more attention with the advance of connected and autonomous vehicle technology. The optimization of passing schedules and conflict separation between conflicting vehicles are usually conducted based on the predefined travelling paths through the intersection area in the CIM. In real-world implementation, however, the diversity of turn paths exists due to multiple factors such as various vehicle sizes and automation control algorithms. The aim of this paper is to investigate how the variation in left-turn paths affects the feasibility and viability of optimal passing schedules, as well as the safety and efficiency of intersection operation. To do this, we start with identifying six typical left-turn paths to represent the variation. A scenario-based simulation is first conducted by using each of the paths as the nominal path. The optimal schedules and the corresponding alternative schedules are generated to calculate indicators for nominal performance, average performance, and robustness. The best path is selected in terms of schedule optimality and robustness. With schedules obtained by solving CIM models using the selected path, the left-turning CAVs are assumed to travel along one of the six paths randomly to simulate the path divergence. A surrogate safety measure, PET, is utilized to assess the safety of the intersection under CIM. The theoretical PET with the nominal path and the actual PET with the random path are calculated for each conflict event. Comparisons of two PET sets show the increase in conflict risk and vehicle delay. The conclusion can be drawn that the variation in left-turn paths causes the decline in safety level and travelling efficiency and should be considered in the CIM model to ensure safe and efficient implementation in the intersection.

A New Modeling Framework for Geothermal Operational Optimization with Machine Learning (GOOML)

Geothermal power plants are excellent resources for providing low carbon electricity generation with high reliability. However, many geothermal power plants could realize significant improvements in operational efficiency from the application of improved modeling software. Increased integration of digital twins into geothermal operations will not only enable engineers to better understand the complex interplay of components in larger systems but will also enable enhanced exploration of the operational space with the recent advances in artificial intelligence (AI) and machine learning (ML) tools. Such innovations in geothermal operational analysis have been deterred by several challenges, most notably, the challenge in applying idealized thermodynamic models to imperfect as-built systems with constant degradation of nominal performance. This paper presents GOOML: a new framework for Geothermal Operational Optimization with Machine Learning. By taking a hybrid data-driven thermodynamics approach, GOOML is able to accurately model the real-world performance characteristics of as-built geothermal systems. Further, GOOML can be readily integrated into the larger AI and ML ecosystem for true state-of-the-art optimization. This modeling framework has already been applied to several geothermal power plants and has provided reasonably accurate results in all cases. Therefore, we expect that the GOOML framework can be applied to any geothermal power plant around the world.

Obtaining Robust Performance of a Current Fed Voltage Source Inverter for Virtual Inertia Response in a Low Short Circuit Ratio Condition

Low inertia levels are typical in island power systems due to the relatively small rotational generation. Displacing rotational generation units with static inertia-less PV power results in a significant increase in the frequency volatility. Virtual inertia provided by inverter-storage systems can resolve this issue. However, a low short circuit ratio (SCR) at the point of common coupling together with a fast phase locked loop (PLL) will compromise the response performance of the system. To address this issue, a robust PI controller (RPI) for the inner current-loop of a current fed grid-connected inverter is proposed. The PLL disturbance and grid impedance are incorporated into a single model and recast to a generalized representation of the system, thereby allowing easy tuning of the RPI by the mixed sensitivity H∞ method. The performance of the RPI is compared with that of a PI controller (PI) tuned by the regular loop-shaping method. The results show that when the SCR is above 10, the performance of both controllers is equivalent. However, lowering of the SCR compromises the performance of the system with PI and it becomes underdamped at SCR < 2. On the contrary, the system with the RPI is capable of maintaining the nominal performance throughout the same SCR decrease.

A Unified Framework for the H∞ Mixed-Sensitivity Design of Fixed Structure Controllers through Putinar Positivstellensatz

In this paper, we present a novel technique to design fixed structure controllers, for both continuous-time and discrete-time systems, through an H∞ mixed sensitivity approach. We first define the feasible controller parameter set, which is the set of the controller parameters that guarantee robust stability of the closed-loop system and the achievement of the nominal performance requirements. Then, thanks to Putinar positivstellensatz, we compute a convex relaxation of the original feasible controller parameter set and we formulate the original H∞ controller design problem as the non-emptiness test of a set defined by sum-of-squares polynomials. Two numerical simulations and one experimental example show the effectiveness of the proposed approach.

Uncertainty Quantification and Reduction Using Sensitivity Analysis and Hessian Derivatives

We study the use of Hessian interaction terms to quickly identify design variables that reduce variability of system performance. To start we quantify the uncertainty and compute the variance decomposition to determine noise variables that contribute most, all at an initial design. Minimizing the uncertainty is next sought, though probabilistic optimization becomes computationally difficult, whether by including distribution parameters as an objective function or through robust design of experiments. Instead, we consider determining the more easily computed Hessian interaction matrix terms of the variance-contributing noise variables and the variables of any proposed design change. We also relate the Hessian term coefficients to subtractions in Sobol indices and reduction in response variance. Design variable changes that can reduce variability are thereby identified quickly as those with large Hessian terms against noise variables. Furthermore, the Jacobian terms of these design changes can indicate which design variables can shift the mean response, to maintain a desired nominal performance target. Using a combination of easily computed Hessian and Jacobian terms, design changes can be proposed to reduce variability while maintaining a targeted nominal. Lastly, we then recompute the uncertainty and variance decomposition at the more robust design configuration to verify the reduction in variability. This workflow therefore makes use of UQ/SA methods and computes design changes that reduce uncertainty with a minimal 4 runs per design change. An example is shown on a Stirling engine design where the top four variance-contributing tolerances are matched with two design changes identified through Hessian terms, and a new design found with 20% less variance.

Monte Carlo transmission line modelling of multilayer optical coatings for performance sensitivity of exoplanet spectroscopy

&lt;p&gt;Multilayer optical coatings are widely used on the surface of optical components to enhance the transmittance of light in certain spectral regions while reducing it in other regions. Discrepancies between the measured and predicted spectral performance of optical components with such coatings can primarily be attributed to deposition errors and uncertainties in the refractive indices of the materials used for these coatings. Our simulation uses two-dimensional transmission line modelling to evaluate the transmittance of light at a given angle of incidence through multilayer coatings deposited on a substrate material. We perform a number of Monte Carlo simulations to obtain statistical information about the tolerance of the coating performance to systematic and random uncertainties in deposition thickness, refractive index and operating temperature. We present the posterior distributions of the deviations from the nominal performance that result from the propagation of each of these uncertainties for a number of hypothetical scenarios. We find that these uncertainties have the potential to cause significant differences between the designed and achieved performance. Our results indicate that the sensitivity of each layer to the various sources of uncertainties can vary on a case-by-case basis. With the aid of accurate manufacturing recipes and uncertainty amplitudes from commercial manufacturers, this simulation can provide a proficient tool to predict variations in the performance of multilayer optical coatings used in exoplanet spectroscopy.&lt;/p&gt;

Optimal model predictive rejection control for nonlinear parabolic trough collector with lumped disturbances

It is challenging and crucial to achieve unbiased tracking control for parabolic trough collector field as it is vulnerable to various types of disturbances or uncertainties such as unmeasured external disturbances, parameter perturbation and model mismatch. To solve this issue, an optimal model predictive rejection control strategy is put forward in a composite designed manner, in which all disturbances/uncertainties are dealt with as lumped disturbances. A generalized extended state observer is firstly employed to estimate the lumped disturbances, and then a feedback controller is devised based on optimal model predictive control to compensate the influences of the lumped disturbances on output. Stability analysis of the closed-loop system has been presented. It shows that the proposed composite controller can track given references without offset in the presence of lumped disturbances while not sacrificing its nominal performance in the absence of disturbances. Simulations conducted on a numerical example and a practical application for parabolic trough collector validate our conclusions.