Evaluation of onboard sensors for track geometry monitoring against conventional track recording measurements

The main objective of this paper is to assess the feasibility and accuracy of inferring key track condition parameters, e.g., vertical alignment and horizontal alignment of the rails, using onboard micro-electro-mechanical-system (MEMS) accelerometers. To achieve this aim, a prototype of an onboard data acquisition system (DAQ) was designed and installed on a track recording car (TRC) and a measurement campaign was conducted on an extensive portion of the Brisbane Suburban railway network. Comparison of the accelerometer-based results vs TRC recordings have shown that accelerometers installed on the bogie are the best compromise between proximity to the source and insensitivity to impulsive noise. Moreover, it was found that two vertical bogie accelerometers (left and right side) provide a good quantitative estimate of vertical alignment and that strong correlations with TRC measurements exist for lateral MEMS accelerometer measurements (horizontal alignment). These findings suggest that two bogie MEMS accelerometers with two measurement axes (vertical and lateral) are an effective system and can provide quantitative estimates of vertical alignment and trends in the geographical distribution of horizontal alignment

A Stochastic-MILP dispatch optimization model for Concentrated Solar Thermal under uncertainty

Concentrated Solar Thermal (CST) offers a promising solution for large-scale solar energy utilization as Thermal Energy Storage (TES) enables electricity generation independently of daily solar fluctuations, shifting to high-priced electricity intervals. The development of dispatch planning tools is mandatory to account for uncertainties associated with solar irradiation and electricity price forecasts as well as limited storage capacity. This study proposes the Stochastic Mixed Integer Linear Program (SMILP) to maximize expected profit within a specified scenario space. The SMILP scenario space is generated by different Empirical Cumulative Distribution Function percentiles of the potential solar energy to accumulate in storage and the expected profit is estimated using the Sample Average Approximation (SAA) method. SMILP exhibits robust performance, however, its computational time poses a challenge. Thus, three heuristic solutions are developed which run a set of deterministic optimizations on different historical weather profiles to generate candidate dispatching plans (DPs). The candidate DP with the best average performance on all profiles is then selected. The new methods were applied to a case study for a 115 MW CST plant in South Australia. When the historical database has a limited set of historical weather profiles, the SMILP achieves 6% to 9% higher profit than the closest benchmark when the DP is applied to novel weather conditions. With a large historical weather data, the performance of SMILP and Heuristic-2 becomes nearly identical because the SMILP can only utilize a limited number of trajectories for optimization without becoming computationally infeasible. In this case, Heuristic-2 emerges a practical alternative, since it provides similar average profit in a reasonable time (saving about 7 hours in computing time).Comment: This paper is going to be submitted to "Energy Conversion and Management" for publication in Elsevier journa

Heliostat-field soiling predictions and cleaning resource optimization for solar tower plants

This paper presents a novel methodology for characterizing soiling losses through experimental measurements. Soiling predictions were obtained by calibrating a soiling model based on field measurements from a 50 MW modular solar tower project in Mount Isa, Australia. The study found that the mean predicted soiling rate for horizontally fixed mirrors was 0.12 percentage points per day (pp/d) during low dust seasons and 0.22 pp/d during high seasons. Autoregressive time series models were employed to extend two years of onsite meteorological measurements to a 10-year period, enabling the prediction of heliostat-field soiling rates. A fixed-frequency cleaning heuristic was applied to optimise the cleaning resources for various operational policies by balancing direct cleaning resource costs against the expected lost production, which was computed by averaging multiple simulated soiling loss trajectories. Analysis of resource usage showed that the cost of fuel and operator salaries contributed 42 % and 35 % respectively towards the cleaning cost. In addition, stowing heliostats in the horizontal position at night increased daily soiling rates by 114 % and the total cleaning costs by 51 % relative to vertically stowed heliostat-field. Under a simplified night-time-only power production configuration, the oversized solar field effectively charged the thermal storage during the day, despite reduced mirror reflectance due to soiling. These findings suggest that the plant can maintain efficient operation even with a reduced cleaning rate. Finally, it was observed that performing cleaning operations during the day led to a 7 % increase in the total cleaning cost compared to a night-time cleaning policy. This was primarily attributed to the need to park operational heliostats for cleaning

A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems

In this paper, a dual-mode model predictive/linear control method is presented, which extends the concept of dual-mode model predictive control (MPC) to trajectory tracking control of nonlinear dynamic systems described by discrete-time state-space models. The dual-mode controller comprises of a time-varying linear control law, implemented when the states lie within a sufficiently small neighborhood of the reference trajectory, and a model predictive control strategy driving the system toward that neighborhood. The boundary of this neighborhood is characterized so as to ensure stability of the closed-loop system and terminate the optimization procedure in a finite number of iterations, without jeopardizing the stability of the closed-loop system. The developed controller is applied to the central air handling unit (AHU) of a two-zone variable air volume (VAV) heating, ventilation, and air conditioning (HVAC) system

Variability and associated uncertainty in image analysis for soiling characterization in solar energy systems

The accumulation of soiling on photovoltaic modules and on the mirrors of concentrating solar power systems causes non-negligible energy losses with economic consequences. These challenges can be mitigated, or even prevented, through appropriate actions if the magnitude of soiling is known. Particle counting analysis is a common procedure to characterize soiling, as it can be easily performed on micrographs of glass coupons or solar devices that have been exposed to the environment. Particle counting does not, however, yield invariant results across institutions. The particle size distribution analysis is affected by the operator of the image analysis software and the methodology utilized. The results of a round-robin study are presented in this work to explore and elucidate the uncertainty related to particle counting and its effect on the characterization of the soiling of glass surfaces used in solar energy conversion systems. An international group of soiling experts analysed the same 8 micrographs using the same open-source ImageJ software package. The variation in the particle analyses results were investigated to identify specimen characteristics with the lowest coefficient of variation (CV) and the least uncertainty among the various operators. The mean particle diameter showed the lowest CV among the investigated characteristics, whereas the number of particles exhibited the largest CV. Additional parameters, such as the fractional area coverage by particles and parameters related to the distribution's shape yielded intermediate CV values. These results can provide insights on the magnitude inter-lab variability and uncertainty for optical and microscope-based soiling monitoring and characterization

Neurologic Involvement in Children and Adolescents Hospitalized in the United States for COVID-19 or Multisystem Inflammatory Syndrome

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Importance Coronavirus disease 2019 (COVID-19) affects the nervous system in adult patients. The spectrum of neurologic involvement in children and adolescents is unclear. Objective To understand the range and severity of neurologic involvement among children and adolescents associated with COVID-19. Setting, Design, and Participants Case series of patients (age <21 years) hospitalized between March 15, 2020, and December 15, 2020, with positive severe acute respiratory syndrome coronavirus 2 test result (reverse transcriptase-polymerase chain reaction and/or antibody) at 61 US hospitals in the Overcoming COVID-19 public health registry, including 616 (36%) meeting criteria for multisystem inflammatory syndrome in children. Patients with neurologic involvement had acute neurologic signs, symptoms, or diseases on presentation or during hospitalization. Life-threatening involvement was adjudicated by experts based on clinical and/or neuroradiologic features. Exposures Severe acute respiratory syndrome coronavirus 2. Main Outcomes and Measures Type and severity of neurologic involvement, laboratory and imaging data, and outcomes (death or survival with new neurologic deficits) at hospital discharge. Results Of 1695 patients (909 [54%] male; median [interquartile range] age, 9.1 [2.4-15.3] years), 365 (22%) from 52 sites had documented neurologic involvement. Patients with neurologic involvement were more likely to have underlying neurologic disorders (81 of 365 [22%]) compared with those without (113 of 1330 [8%]), but a similar number were previously healthy (195 [53%] vs 723 [54%]) and met criteria for multisystem inflammatory syndrome in children (126 [35%] vs 490 [37%]). Among those with neurologic involvement, 322 (88%) had transient symptoms and survived, and 43 (12%) developed life-threatening conditions clinically adjudicated to be associated with COVID-19, including severe encephalopathy (n = 15; 5 with splenial lesions), stroke (n = 12), central nervous system infection/demyelination (n = 8), Guillain-Barré syndrome/variants (n = 4), and acute fulminant cerebral edema (n = 4). Compared with those without life-threatening conditions (n = 322), those with life-threatening neurologic conditions had higher neutrophil-to-lymphocyte ratios (median, 12.2 vs 4.4) and higher reported frequency of D-dimer greater than 3 μg/mL fibrinogen equivalent units (21 [49%] vs 72 [22%]). Of 43 patients who developed COVID-19–related life-threatening neurologic involvement, 17 survivors (40%) had new neurologic deficits at hospital discharge, and 11 patients (26%) died. Conclusions and Relevance In this study, many children and adolescents hospitalized for COVID-19 or multisystem inflammatory syndrome in children had neurologic involvement, mostly transient symptoms. A range of life-threatening and fatal neurologic conditions associated with COVID-19 infrequently occurred. Effects on long-term neurodevelopmental outcomes are unknown

Predicting maintenance requirements for school assets in Queensland

In this paper, a maintenance prediction model is developed for school building assets using a large data set provided by the Queensland Department of Education and Training (DET). DET data on the asset condition, historical maintenance expenditure, and asset characteristics, was analyzed to evaluate which characteristics affect the maintenance needs of the school assets. The condition of the assets was quantified using data on the estimated maintenance backlog. Using statistical methods, models for key building element groups were constructed and the statistical significance of each factor was evaluated. It was found that the school region, the gross floor area, and the maintenance expenditure significantly affected the degradation of key building element groups

Potentials of condition based monitoring in semiconductor manufacturing

Today, the majority of semiconductor fabrication plants (Fabs) conduct equipment preventive maintenance based on statistically derived time-based or wafer count based intervals. While these practices have had relative success in managing equipment availability and managing product yield, the costs, both in time and materials remains high. Condition Based Monitoring (CBM) has been successfully adopted in several industries, where costs associated with equipment downtime range from loss of life to unacceptable affects to companies' bottom line. In this paper, we will investigate a method of CBM to semiconductor manufacturing that addresses some of the issues of CBM in complex systems with multiple operating regimes</p

Optimizing the unrestricted wind turbine placements with different turbine hub heights

Wind farm layout optimization is an effective means to mitigate the wind power losses caused by the wake interventions between wind turbines. Most of the researches in the field are conducted on the basis of fixed wind turbine hub height, while it has been proved that different hub height turbines may contribute to the reduction of wake power losses and increase the wind farm energy production. To demonstrate this effect, the results of simple two-wind-turbine model are reported by fixing the first wind turbine hub height while varying the second one. Then the optimization results for a wind farm are reported under three different wind conditions, consisting of constant wind speed and wind direction, constant wind speed and variable wind directions, and variable wind speeds and variable wind directions. Unlike the previous researches using the grid based method to conduct the optimization studies, the wind farm layout optimization with differing hub heights is carried out using the unrestricted coordinate method in this paper for the first time. Different optimization methods are applied for the wind farm optimization study to investigate their effectiveness by comparison. It shows that the selection of the identical wind turbine hub height yields the least power production with the most intensive wake effect. The value of optimum wind turbine hub height is dependent on several factors including the surface roughness length, spacing between the two wind turbines and the blowing wind direction. The simultaneous optimization method is more effective for the complex wind conditions than for the simple constant wind condition