Experimental aeroacoustics study on jet noise reduction using tangential air injection

PhDAircraft jet exhausts are a source of undesirable noise and continue to be an area of investigation driven by increasingly stringent regulation. The noise is produced by the unsteady mixing of the jet with the surrounding air and is dominated by the effects of the shear layer. In this study, the mechanisms of noise suppression are investigated on an unheated Mach 1.3 jet through three distinct control techniques. The first consists of tangential steady flow injectors located upstream of the nozzle exit whereas the second involves an equal number of control jets spaced further downstream around the nozzle exit. The third technique pulses air tangential into the shear layer at a frequency coinciding with the preferred modes of the jet (St~0.17, f~2kHz). Near and far-field acoustic measurements were made in anechoic chamber with an array of 10 free-field microphones. All three forms of tangential air injection induced reductions in overall sound pressure levels (OASPL) across a range of observer angles. External tangential injection was found to be the most efficient technique, as it produced comparably similar noise reductions at only a fraction of the injection mass flow ratio. The most significant acoustic benefit was an 8dB SPL reduction at the sideline observer angle, subsequently eliminating both screech and broadband shock noise at Strouhal numbers of St~0.74 (f~9.6kHz) and St~1(f~13kHz) respectively. An OASPL reduction of up to 5dB was also recorded at a downstream angle of 15º. However, the low-frequency noise benefits from these control jets came at the expense of increased high frequency noise beyond St>2 (f~26kHz). The flow-fields of the jet were observed using stereoscopic Particle Image Velocimetry (PIV). The introduction of a swirling component of velocity downstream of the nozzle exit was found to have a stabilizing effect on the jet shear layer. Reductions in turbulence intensity and Reynolds stress were recorded towards the end of the potential core by up to 18% and 25% respectively. The ultimate objective of this study was to develop an injection configuration that is effective at reducing jet noise whilst minimising penalties in weight and thrust

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Neuroimmunology: An expanding frontier in 21st century neurology

The recent discovery of functional lymphatic vessels lining the dural sinuses shattered the long held view of the absence of CNS lymphatic vas culature and provided solid neuroanatomical ground for Neuroimmunology1. Moreover, it has been shown that there is the presence of what is known as the inflammatory reflex

“More than meets the eye” non secretory myeloma presenting as cidp in a patient with longstanding diabetic polyneuropathy. a diagnostic and therapeutic challenge.

Diagnosing chronic inflammatory polyradiculopathy in patients with pre-existing diabetic sensorimotor polyneuropathy is a diagnostic challenge. We present a case of a 69 years old who presented with weakness of legs for two months,he was diagnosed as having CIDP on the background of diabetic sensorimotor polyneuropathy, further and extensive workup revealed the final diagnosis of nonsecretory myeloma. Diagnosing non secretory myeloma is itself a diagnostic challenge and usually first line investigations for the workup of myeloma are negative as was the case in our patient. Our patient with CIDP had raised free light chains of kappa which made the final diagnosis of kappa associated plasma cell dyscrasia

Two cases of hyperkalemia presenting as acute demyelinating polyneuropathy: clinical and electrophysiological reversibility with in 72 hours with potassium correction.

GullianBarre syndrome (GBS) is the most common cause of acute flaccid paralysis.Hypokalemia can present with flaccid paralysis and nerve conduction studies show axonal neuropathy.Here we present two cases who were initially admitted with suspicion of GBS but later on baseline investigations showed very high serumpotassium and in both cases nerve conduction showed acute demyelinating polyneuropathy.They were admitted in high dependency units and urgent dialysis was done.In first case NCS were repeated 72 hours of correction of hyperkalemia and they showed significant improvement. In second patient NCSwere repeated after 24 hours and they showed mild improvement in all parameters

Determining the Socio-Economic Importance of Saffron as an Alternative Product to Opium Production in Afghanistan

The objective of this work is to determine the socio-economic importance of saffron production as an alternative to opium production in Afghanistan, and to determine if saffron production could influence farmers’ incomes. The primary data for the survey was obtained via direct interviews with farmers of 4 saffron leader districts in Herat, where 95% Saffron production was noted during 2016-2017. Factor analysis was used to determine the factors that influence saffron producers. Cluster analysis was used further, to separate farmer income groups. According to the first cluster, the most important factors affecting agricultural production were: negative climatic conditions while market instability was the second factor. Saffron producers’ annual average yield is 6.6 kg/ha in results that showed that if opium production is permitted, saffron farmers would produce opium due to the high revenue associated with opium production in Afghanistan

Association of vitamin D with statin induced myalgia

Objective: To determine the association of serum 25-hydroxy vitamin D (25(OH) Vitamin D) deficiency with the occurrence of myalgia in patients on statin therapy. Methods: The pathology laboratory database was reviewed to identify patients tested for serum 25(OH) Vitamin D and creatine kinase. A retrospective chart review was then conducted to ascertain statin use and reporting of myalgia for patients tested concurrently for serum 25(OH) vitamin D and creatinekinase levels between January 1, 2013 and December 31, 2013. Results: Of the 825 patients tested for creatine kinase and 25 (OH) Vitamin D in 2013, 49 met the study criteria.The mean serum 25 (OH) Vitamin D level in the 24 statin induced myalgiapatients was 17.93 ± 12.07 compared to 18.99 ± 15.2 in the 25 no SIM group (p = 0.81). Conclusion: Our study reports no association between statin induced myalgia and low 25 (OH) vitamin D levels

FAKTOR-FAKTOR YANG MEMPENGARUHI PENDAPATAN PETANI HANJELI (Coix lacym-jobi L.) DI DESA WALURAN MANDIRI KECAMATAN WALURAN KABUPATEN SUKABUMI

ARSALAN AHMAD. 2019. Factors Affecting the Income of Hanjeli Farmers in Waluran District, Sukabumi Regency. Supervised by Ashrul Tsani and Neneng Kartika Rini. Hanjeli or (Coixlacryma-jobi L.) is a cereal plant from the family poaceae that can be used as food and feed, to find out the real income, then an income analysis in this village is feasible. The purpose of the study was to analyze the factors that influence the income of hanjeli farmers in Waluran District using the quantitative description method. The results showed that the variables studied did not have a significant effect on farmer's income

Traffic grooming and energy-efficiency in flexible-grid networks

Energy-efficient design of flexible-grid networks is investigated. We focus on the design of the logical layer, usually disregarded when dealing with flexible-grid networks. More precisely, we evaluate the impact of introducing an energy-aware electronic traffic grooming in flexible-grid networks design. We propose two greedy heuristics for the network design, one exploiting traffic grooming, and we compare their energy efficiency. Results have been retrieved for several randomly generated networks of different size, with different connectivity, average physical link length and traffic scenarios. Significant energy savings can be achieved for low traffic loads and large network size when performing traffic grooming

Power-Aware Logical Topology Design Heuristics in Wavelength-Routing Networks

Abstract—Wavelength-Routing (WR) networks are the most common solution for core networks. With the access segment moving from copper to Passive Optical Networks (PON), core networks will become one of the major culprits of Internet power consumption. However, WR networks offer some design flexibility which can be exploited to mitigate their energy requirements. One of the main steps which has to be faced in designing WR networks is the planning of the Logical Topology (LT) starting from the matrix of traffic requests. In this paper, we propose a Mixed Integer Linear Programming (MILP) formulation to find power-wise optimal LTs. In addition, due to the complexity of the MILP approach we propose a greedy heuristic and a genetic algorithm (GA) ensuring performance close to the one achieved by the MILP formulation. I