Integrated approach for prediction of centrifugal fertilizer spread patterns

The present paper proposes a numerical approach for prediction of behavior of those fertilizers spreaders based on centrifugal disc functioning. In particular results from finite element multi-body simulations provided by commercial software are used in order to define boundary conditions of field tests carried out concurrently. Results are then integrated into a mathematical model to rapidly generate distribution charts and distribution diagrams. Such integrated approach can be implemented to effectively calibrate a theoretical model which provides simulations on different machine settings conditions: as a final point simulations allow fast testing of different distribution conditions, helping definition of those which minimize the variability of the distribution itself

Signal noise in pure fluid amplifiers research report no. 1

Signal noise in pure fluid amplifiers, signal function generator, fluid transmission lines, closed loop control, and related instrumentation and equipmen

COMPUTATIONAL FLUID DYNAMICS (CFD) MODELING AND VALIDATION OF DUST CAPTURE BY A NOVEL FLOODED BED DUST SCRUBBER INCORPORATED INTO A LONGWALL SHEARER OPERATING IN A US COAL SEAM

Dust is a detrimental, but unavoidable, consequence of any mining process. It is particularly problematic in underground coal mining, where respirable coal dust poses the potential health risk of coal workers’ pneumoconiosis (CWP). Float dust, if not adequately diluted with rock dust, also creates the potential for a dust explosion initiated by a methane ignition. Furthermore, recently promulgated dust regulations for lowering a miner’s exposure to respirable coal dust will soon call for dramatic improvements in dust suppression and capture. Computational fluid dynamics (CFD) results are presented for a research project with the primary goal of applying a flooded-bed dust scrubber, with high capture and cleaning efficiencies, to a Joy 7LS longwall shearer operating in a 7-ft (2.1 m) coal seam. CFD software, Cradle is used to analyze and evaluate airflow patterns and dust concentrations, under various arrangements and conditions, around the active mining zone of the shearer for maximizing the capture efficiency of the scrubber

Structural graph matching using the EM algorithm and singular value decomposition

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of the graph and does not draw on node or edge attributes. We make two contributions: 1) commencing from a probability distribution for matching errors, we show how the problem of graph matching can be posed as maximum-likelihood estimation using the apparatus of the EM algorithm; and 2) we cast the recovery of correspondence matches between the graph nodes in a matrix framework. This allows one to efficiently recover correspondence matches using the singular value decomposition. We experiment with the method on both real-world and synthetic data. Here, we demonstrate that the method offers comparable performance to more computationally demanding method

Money growth and inflation: a regime switching approach

We develop a time-varying transition probabilities Markov Switching model in which inflation is characterised by two regimes (high and low inflation). Using Bayesian techniques, we apply the model to the euro area, Germany, the US, the UK and Canada for data from the 1960s up to the present. Our estimates suggest that a smoothed measure of broad money growth, corrected for real-time estimates of trend velocity and potential output growth, has important leading indicator properties for switches between inflation regimes. Thus money growth provides an important early warning indicator for risks to price stability. JEL Classification: C11, C53, E31Bayesian inference, early warning, inflation regimes, Markov Switching model, money growth, time varying transition probabilities

Stellar Intensity Interferometry: Prospects for sub-milliarcsecond optical imaging

Using kilometric arrays of air Cherenkov telescopes, intensity interferometry may increase the spatial resolution in optical astronomy by an order of magnitude, enabling images of rapidly rotating stars with structures in their circumstellar disks and winds, or mapping out patterns of nonradial pulsations across stellar surfaces. Intensity interferometry (pioneered by Hanbury Brown and Twiss) connects telescopes only electronically, and is practically insensitive to atmospheric turbulence and optical imperfections, permitting observations over long baselines and through large airmasses, also at short optical wavelengths. The required large telescopes with very fast detectors are becoming available as arrays of air Cherenkov telescopes, distributed over a few square km. Digital signal handling enables very many baselines to be synthesized, while stars are tracked with electronic time delays, thus synthesizing an optical interferometer in software. Simulated observations indicate limiting magnitudes around m(v)=8, reaching resolutions ~30 microarcsec in the violet. The signal-to-noise ratio favors high-temperature sources and emission-line structures, and is independent of the optical passband, be it a single spectral line or the broad spectral continuum. Intensity interferometry provides the modulus (but not phase) of any spatial frequency component of the source image; for this reason image reconstruction requires phase retrieval techniques, feasible if sufficient coverage of the interferometric (u,v)-plane is available. Experiments are in progress; test telescopes have been erected, and trials in connecting large Cherenkov telescopes have been carried out. This paper reviews this interferometric method in view of the new possibilities offered by arrays of air Cherenkov telescopes, and outlines observational programs that should become realistic already in the rather near future.Comment: New Astronomy Reviews, in press; 101 pages, 11 figures, 185 reference

Longwall mining-induced fracture characterisation based on seismic monitoring

Despite several technological advancements, mining-induced fractures are still critical for the safety of underground coal mines. Rocking fracturing as a natural response to mining activities can pose a potential hazard to mine operators, equipment, and infrastructures. The fractures occur not only around the working face that can be visually measured but also above and in front of the working face and where geological structures are affected by mining activities. Therefore, it is of importance to detect and investigate the properties of mining-induced fractures. Mining-induced seismicity has been generated due to rock fracturing during progressive mining activities and can provide critical fracture information. Currently, the application of using seismic monitoring to characterise fractures has remained relatively challenged in mining because mining-induced fractures are initiated by stress change and strata movement after mineral extraction. Compared to seismic monitoring in the oil and gas industry, the fractures and seismic responses may show different characteristics. Therefore, seismic monitoring in mines lacks a comprehensive investigation of received seismic signals to the properties of induced fractures and the effect on mine workings by these fractures. Additionally, constraints such as the quality of seismic signals and the deficiency of correlation analysis of seismic events in underground mining pose great challenges in using seismic data for hazard prediction. This thesis aims to address these challenges in using seismic monitoring to understand and characterise mining-induced fractures by (1) calculating fracture properties related to seismic source location, magnitude and mechanism based on uniaxial seismic data, (2) spatial and temporal correlation analysis of seismic events, and (3) inspecting fracture distributions and simulation of the fractured zone in longwall coal mines. Firstly, since cheap and easily removable uniaxial geophones close to production areas are preferable in coal mines, a novel method to use uniaxial signal and moment tensor inversion to generate synthetic triaxial waves is designed for a comprehensive description of the fracture properties, including location, radius, aperture and orientation. Secondly, to apply seismic data for advanced analysis, such as rockburst prediction and caving assessment, the correlation of seismic events is proved to be quantitatively assessable, and their correlations may vary throughout the mineral extraction process. The spatial and temporal correlation of seismic event energy is quantitatively analysed using various statistical methods, including autocorrelation function (ACF), semivariogram and Moran's I analysis. In addition, based on the integrated spatial-temporal (ST) correlation assessment, seismic events are further classified into seven clusters to assess the correlations within individual clusters. Finally, several source parameters such as seismic moment (M0), seismic source radius (R), fracture aperture (τ), failure type and fracture orientation were used to characterise fractures induced by longwall mining. This thesis also presents the fracture patterns induced caused progressive longwall mining for the first time. Besides, a discrete element method (DEM) model with seismic-derived fractures is generated and proves the impact of mining-induced fractures on altering stress conditions during mineral extraction. In addition, with the analysis of the seismic source mechanism and a synthetic triaxial method, a discrete fracture network (DFN) is generated from monitored seismic events to restore complete induced fractures. Overall, the outcomes of this study lead to a comprehensive assessment of mining-induced fracture properties based on real-time seismic monitoring, demonstrating its significant potential for hazard prediction and improving the safety of resource recovery

Dynamic characteristics analysis on shearer drum in condition of cutting coal with different distributed rocks

Dynamic characteristics analysis on shearer drum is an important aspect in shearer design and a well-structured shearer drum provides better performance in vibration reduction and service life extension. To make reference for structure improvement, many researchers have focused on the dynamic analysis on cutting pick. However, the simplified model such as single pick, linear motion and continuous coal in the previous study made it divorced from actual situations underground. On basis of this, the model of shearer drum with several arranged picks and the coal model with some distributed rock have been set up in order to consist with the real working conditions. Through simulation, it can be found that the mean force in X-direction is little influenced by rock distribution, but the peak force in X-direction fluctuates greatly under different rock distribution. During the analysis on the peak force, it can be found that this kind of force could be cut down effectively when the rock was distributed on the top of the shearer cutting range. Based on these conclusions, we can program the cutting strategy to lower the influence of cutting forces on the shearer and its drum in the future

Learning to Manipulate a Financial Benchmark

Financial benchmarks estimate market values or reference rates used in a wide variety of contexts, but are often calculated from data generated by parties who have incentives to manipulate these benchmarks. Since the London Interbank Offered Rate (LIBOR) scandal in 2011, market participants, scholars, and regulators have scrutinized financial benchmarks and the ability of traders to manipulate them. We study the impact on market welfare of manipulating transaction-based benchmarks in a simulated market environment. Our market consists of a single benchmark manipulator with external holdings dependent on the benchmark, and numerous background traders unaffected by the benchmark. We explore two types of manipulative trading strategies: zero-intelligence strategies and strategies generated by deep reinforcement learning. Background traders use zero-intelligence trading strategies. We find that the total surplus of all market participants who are trading increases with manipulation. However, the aggregated market surplus decreases for all trading agents, and the market surplus of the manipulator decreases, so the manipulator’s surplus from the benchmark significantly increases. This entails under natural assumptions that the market and any third parties invested in the opposite side of the benchmark from the manipulator are negatively impacted by this manipulation