Experimental and Theoretical Investigation of Vibro-Impact Motions of a Gear Pair Subjected to Torque Fluctuations to Define a Rattle Noise Severity Index

Vibro-impacts are common in various automotive engine and transmission gear applications. They are known to cause excessive noise levels, often called rattling or hammering. Input and output fluctuations acting on such systems cause tooth separations and sequences of impacts allowed by backlash at the gear mesh interfaces. The fluctuations leading gear rattling have often been studied for specific applications with the excitations produced typically by an internal combustion engine. As such, rattle evaluations have been often empirical and specific to the systems considered. In this study, an experimental test set-up of a gear pair is developed to emulate the same torque fluctuations in a laboratory environment. This set-up is used to establish an impact velocity-based rattle severity index defined by the measured torsional behavior of the drive train that is shown to correlate well with the measured sound pressure levels. With that, a validated dynamic model of the experimental setup is employed to predict the same index to allow estimation of rattle noise outcome solely from a torsional dynamic model of the drivetrain. Predicted rattle severity indexes are shown to agree well with the measured ones within wide ranges of torque fluctuations and backlash magnitudes, allowing an assessment of rattle performance of a drivetrain solely from a torsional model.

Study on control strategy of output stability of wind-solar reservoir thermal system

In view of the landscape reservoir heat output coordinated control demand, based on the topology of the hybrid energy storage system of the three ports heating model, using the sunlight with the electricity output of the complementary and heat accumulation can be regulatory, intends to research a kind of based on photo-thermal storage heat and scenery electricity heating output port control method, in order to achieve the goal of fast and smooth regulating heat output fluctuations, The coordinated output control of integrated wind-landscape storage and heat collection is realized.

Short-Term Cooperative Operational Scheme of Distribution System with High Hosting Capacity of Renewable-Energy-Based Distributed Generations

As the interconnection of renewable-energy-based distributed generations (DGs) to the distribution system increases, the local and temporary voltage and current problems, which are difficult to resolve with the existing operation method, are becoming serious. In this study, we propose a short-term operational method that can effectively resolve voltage and current violations caused by instantaneous output fluctuations of DGs in a system with a high hosting capacity of renewable energy sources. To achieve the objectives, a modified heuristic network reconfiguration method, and a method determining the maximum power output limit of individual DGs are proposed. We propose a cooperative method for controlling the power output fluctuations of renewable-energy-based DGs, which includes voltage control, network reconfiguration, and power curtailment. The proposed algorithm was verified through case studies by using a test system implemented in MATLAB environments. It can effectively resolve violations caused by DGs while minimizing the number of switching operations and power curtailment. The proposed method is an appropriate operation method to be applied to the real system as it can cope with the instantaneous output fluctuation of DGs, which was not dealt with in the existing operation method.

Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?

Uncertainty about the future rises in recessions. But is uncertainty a source of business cycles or an endogenous response to them, and does the type of uncertainty matter? We propose a novel SVAR identification strategy to address these questions via inequality constraints on the structural shocks. We find that sharply higher macroeconomic uncertainty in recessions is often an endogenous response to output shocks, while uncertainty about financial markets is a likely source of output fluctuations. (JEL D81, E23, E32, E44, G14)

Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

Analysis of Coordinated Operation of the Clean Energy System Based on the Multiobjective Optimization Model

With the increase in the proportion of clean energy connected to the grid, the effective coordination of the operation of various energy power has become a new challenge facing the current power system scheduling. The coordinated operation of the clean energy power generation system can alleviate the contradiction between power generation and output power fluctuations and overcome the bottleneck of new energy development. Considering the natural characteristics of clean energy, this paper aims to make full use of clean energy, reduce system operating costs, increase system power generation, and reduce output fluctuations; we establish a multiobjective optimization model for coordinated scheduling of clean energy power systems. The model seeks to maximize power generation and minimize output fluctuations, power purchase costs, and maintenance costs under the constraints of the grid structure. In this paper, the GA _ PSO joint algorithm has an accelerated effect on the target optimization calculation, and then the superiority of the GA _ PSO algorithm is verified by the I E E E 14 standard system. The standard IEEE39 node test system is used to verify the rationality and feasibility of the model built and provides a reference strategy for the coordinated operation mechanism of the clean energy system. According to the model, in the example in this paper, the maximum value of photovoltaic power prediction is 1290 MW, and the minimum value is 210 MW; the maximum value of wind power prediction is 780 MW, and the minimum value is 28 MW; the minimum cost of power purchase and maintenance is 56,950.395; the maximum generating capacity is 5.045 GW; the minimum output fluctuation is 0.120 GW.

A quantum heat engine driven by atomic collisions

Quantum heat engines are subjected to quantum fluctuations related to their discrete energy spectra. Such fluctuations question the reliable operation of thermal machines in the quantum regime. Here, we realize an endoreversible quantum Otto cycle in the large quasi-spin states of Cesium impurities immersed in an ultracold Rubidium bath. Endoreversible machines are internally reversible and irreversible losses only occur via thermal contact. We employ quantum control to regulate the direction of heat transfer that occurs via inelastic spin-exchange collisions. We further use full-counting statistics of individual atoms to monitor quantized heat exchange between engine and bath at the level of single quanta, and additionally evaluate average and variance of the power output. We optimize the performance as well as the stability of the quantum heat engine, achieving high efficiency, large power output and small power output fluctuations.