scholarly journals Predicting specific impulse distributions for spherical explosives in the extreme near-field using a Gaussian function

2021 ◽  
pp. 204141962199349
Author(s):  
Jordan J Pannell ◽  
George Panoutsos ◽  
Sam B Cooke ◽  
Dan J Pope ◽  
Sam E Rigby
Keyword(s):  
Scaling Laws ◽  
Near Field ◽  
Specific Impulse ◽  
Gaussian Function ◽  
Data Driven ◽  
Blast Load ◽  
Structural Deformation ◽  
Load Prediction ◽  
Validation Data ◽  
Wide Range

Accurate quantification of the blast load arising from detonation of a high explosive has applications in transport security, infrastructure assessment and defence. In order to design efficient and safe protective systems in such aggressive environments, it is of critical importance to understand the magnitude and distribution of loading on a structural component located close to an explosive charge. In particular, peak specific impulse is the primary parameter that governs structural deformation under short-duration loading. Within this so-called extreme near-field region, existing semi-empirical methods are known to be inaccurate, and high-fidelity numerical schemes are generally hampered by a lack of available experimental validation data. As such, the blast protection community is not currently equipped with a satisfactory fast-running tool for load prediction in the near-field. In this article, a validated computational model is used to develop a suite of numerical near-field blast load distributions, which are shown to follow a similar normalised shape. This forms the basis of the data-driven predictive model developed herein: a Gaussian function is fit to the normalised loading distributions, and a power law is used to calculate the magnitude of the curve according to established scaling laws. The predictive method is rigorously assessed against the existing numerical dataset, and is validated against new test models and available experimental data. High levels of agreement are demonstrated throughout, with typical variations of <5% between experiment/model and prediction. The new approach presented in this article allows the analyst to rapidly compute the distribution of specific impulse across the loaded face of a wide range of target sizes and near-field scaled distances and provides a benchmark for data-driven modelling approaches to capture blast loading phenomena in more complex scenarios.

scholarly journals Data-driven recursive input–output multivariate statistical forecasting model: case of DO concentration prediction in Advancetown Lake, Australia

2015 ◽  
Vol 17 (5) ◽  
pp. 817-833 ◽  
Author(s):  
Edoardo Bertone ◽  
Rodney A. Stewart ◽  
Hong Zhang ◽  
Cameron Veal
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Input Output ◽  
Model Case ◽  
Validation Data ◽  
Data Set ◽  
Wide Range ◽  
Do Concentration

A regression model integrating data pre-processing and transformation, input selection techniques and a data-driven statistical model, facilitated accurate 7 day ahead time series forecasting of selected water quality parameters. A core feature of the modelling approach is a novel recursive input–output algorithm. The herein described model development procedure was applied to the case of a 7 day ahead dissolved oxygen (DO) concentration forecast for the upper hypolimnion of Advancetown Lake, Queensland, Australia. The DO was predicted with an R2 &gt; 0.8 and a normalised root mean squared error of 14.9% on a validation data set by using 10 inputs related to water temperature or pH. A key feature of the model is that it can handle nonlinear correlations, which was essential for this environmental forecasting problem. The pre-processing of the data revealed some relevant inputs that had only 6 days' lag, and as a consequence, those predictors were in-turn forecasted 1 day ahead using the same procedure. In this way, the targeted prediction horizon (i.e. 7 days) was preserved. The implemented approach can be applied to a wide range of time-series forecasting problems in the complex hydro-environment research area. The reliable DO forecasting tool can be used by reservoir operators to achieve more proactive and reliable water treatment management.

scholarly journals Double moiré localized plasmon structured illumination microscopy

Nanophotonics ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ruslan Röhrich ◽  
A. Femius Koenderink
Keyword(s):  
Near Field ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Experimental Realization ◽  
Spatial Frequencies ◽  
Wide Range ◽  
Localized Plasmon ◽  
Plasmonic Grating

AbstractStructured illumination microscopy (SIM) is a well-established fluorescence imaging technique, which can increase spatial resolution by up to a factor of two. This article reports on a new way to extend the capabilities of structured illumination microscopy, by combining ideas from the fields of illumination engineering and nanophotonics. In this technique, plasmonic arrays of hexagonal symmetry are illuminated by two obliquely incident beams originating from a single laser. The resulting interference between the light grating and plasmonic grating creates a wide range of spatial frequencies above the microscope passband, while still preserving the spatial frequencies of regular SIM. To systematically investigate this technique and to contrast it with regular SIM and localized plasmon SIM, we implement a rigorous simulation procedure, which simulates the near-field illumination of the plasmonic grating and uses it in the subsequent forward imaging model. The inverse problem, of obtaining a super-resolution (SR) image from multiple low-resolution images, is solved using a numerical reconstruction algorithm while the obtained resolution is quantitatively assessed. The results point at the possibility of resolution enhancements beyond regular SIM, which rapidly vanishes with the height above the grating. In an initial experimental realization, the existence of the expected spatial frequencies is shown and the performance of compatible reconstruction approaches is compared. Finally, we discuss the obstacles of experimental implementations that would need to be overcome for artifact-free SR imaging.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Aerodynamic Similarity Principles and Scaling Laws for Windmilling Fans

2018 ◽  
Author(s):  
Dilip Prasad
Keyword(s):  
Rotational Speed ◽  
Pressure Loss ◽  
Scaling Laws ◽  
Dynamic Pressure ◽  
Stagnation Pressure ◽  
Design Parameters ◽  
Similarity Parameter ◽  
Wide Range ◽  
Simulation Results ◽  
Good Agreement

Windmilling requirements for aircraft engines often define propulsion and airframe design parameters. The present study is focused is on two key quantities of interest during windmill operation: fan rotational speed and stage losses. A model for the rotor exit flow is developed, that serves to bring out a similarity parameter for the fan rotational speed. Furthermore, the model shows that the spanwise flow profiles are independent of the throughflow, being determined solely by the configuration geometry. Interrogation of previous numerical simulations verifies the self-similar nature of the flow. The analysis also demonstrates that the vane inlet dynamic pressure is the appropriate scale for the stagnation pressure loss across the rotor and splitter. Examination of the simulation results for the stator reveals that the flow blockage resulting from the severely negative incidence that occurs at windmill remains constant across a wide range of mass flow rates. For a given throughflow rate, the velocity scale is then shown to be that associated with the unblocked vane exit area, leading naturally to the definition of a dynamic pressure scale for the stator stagnation pressure loss. The proposed scaling procedures for the component losses are applied to the flow configuration of Prasad and Lord (2010). Comparison of simulation results for the rotor-splitter and stator losses determined using these procedures indicates very good agreement. Analogous to the loss scaling, a procedure based on the fan speed similarity parameter is developed to determine the windmill rotational speed and is also found to be in good agreement with engine data. Thus, despite their simplicity, the methods developed here possess sufficient fidelity to be employed in design prediction models for aircraft propulsion systems.

scholarly journals The Open Global Glacier Model (OGGM) v1.0

2018 ◽  
Author(s):  
Fabien Maussion ◽  
Anton Butenko ◽  
Julia Eis ◽  
Kévin Fourteau ◽  
Alexander H. Jarosch ◽  
...  
Keyword(s):  
Mass Balance ◽  
Open Source ◽  
Sea Level ◽  
Computational Cost ◽  
Balance Model ◽  
Climate Data ◽  
Estimation Model ◽  
Model Framework ◽  
Validation Data ◽  
Wide Range

Abstract. Despite of their importance for sea-level rise, seasonal water availability, and as source of geohazards, mountain glaciers are one of the few remaining sub-systems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM, http://www.oggm.org), developed to provide a modular and open source numerical model framework for simulating past and future change of any glacier in the world. The modelling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice flow model. The monthly mass-balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model explicitly simulating glacier dynamics: the model relies on the shallow ice approximation to compute the depth-integrated flux of ice along multiple connected flowlines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows a very realistic behaviour. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozens of glaciers can be simulated on a personal computer, while global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the codebase, allowing to run new kinds of model intercomparisons in a controlled environment. Future developments will add new physical processes to the model as well as tools to calibrate the model in a more comprehensive way. OGGM spans a wide range of applications, from ice-climate interaction studies at millenial time scales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained, community driven model for global and regional glacier evolution.

Aerodynamic Similarity Principles and Scaling Laws for Windmilling Fans

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Dilip Prasad
Keyword(s):  
Rotational Speed ◽  
Pressure Loss ◽  
Scaling Laws ◽  
Dynamic Pressure ◽  
Stagnation Pressure ◽  
Design Parameters ◽  
Similarity Parameter ◽  
Wide Range ◽  
Simulation Results ◽  
Good Agreement

Windmilling requirements for aircraft engines often define propulsion and airframe design parameters. The present study is focused on two key quantities of interest during windmill operation: fan rotational speed and stage losses. A model for the rotor exit flow is developed that serves to bring out a similarity parameter for the fan rotational speed. Furthermore, the model shows that the spanwise flow profiles are independent of the throughflow, being determined solely by the configuration geometry. Interrogation of previous numerical simulations verifies the self-similar nature of the flow. The analysis also demonstrates that the vane inlet dynamic pressure is the appropriate scale for the stagnation pressure loss across the rotor and splitter. Examination of the simulation results for the stator reveals that the flow blockage resulting from the severely negative incidence that occurs at windmill remains constant across a wide range of mass flow rates. For a given throughflow rate, the velocity scale is then shown to be that associated with the unblocked vane exit area, leading naturally to the definition of a dynamic pressure scale for the stator stagnation pressure loss. The proposed scaling procedures for the component losses are applied to the flow configuration of Prasad and Lord (2010). Comparison of simulation results for the rotor-splitter and stator losses determined using these procedures indicates very good agreement. Analogous to the loss scaling, a procedure based on the fan speed similarity parameter is developed to determine the windmill rotational speed and is also found to be in good agreement with engine data. Thus, despite their simplicity, the methods developed here possess sufficient fidelity to be employed in design prediction models for aircraft propulsion systems.

scholarly journals A Survey of NFC Sensors Based on Energy Harvesting for IoT Applications

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3746 ◽  
Author(s):  
Antonio Lazaro ◽  
Ramon Villarino ◽  
David Girbau
Keyword(s):  
Energy Harvesting ◽  
Integrated Circuits ◽  
Radio Frequency Identification ◽  
Low Cost ◽  
Near Field ◽  
Wearable Sensors ◽  
Cold Chain ◽  
Iot Applications ◽  
Wide Range ◽  
Frequency Identification

In this article, an overview of recent advances in the field of battery-less near-field communication (NFC) sensors is provided, along with a brief comparison of other short-range radio-frequency identification (RFID) technologies. After reviewing power transfer using NFC, recommendations are made for the practical design of NFC-based tags and NFC readers. A list of commercial NFC integrated circuits with energy-harvesting capabilities is also provided. Finally, a survey of the state of the art in NFC-based sensors is presented, which demonstrates that a wide range of sensors (both chemical and physical) can be used with this technology. Particular interest arose in wearable sensors and cold-chain traceability applications. The availability of low-cost devices and the incorporation of NFC readers into most current mobile phones make NFC technology key to the development of green Internet of Things (IoT) applications.

scholarly journals Metals and Alloys Additives as Enhancer for Rocket Propulsion: A Review

2021 ◽  
Vol 90 (1) ◽  
pp. 1-9
Author(s):  
Izham Izzat Ismail ◽  
Norhuda Hidayah Nordin ◽  
Muhammad Hanafi Azami ◽  
Nur Azam Abdullah
Keyword(s):  
Specific Impulse ◽  
Flame Temperature ◽  
High Energy ◽  
Combustion Efficiency ◽  
High Energy Density ◽  
Regression Rate ◽  
High Entropy ◽  
Rocket Propulsion ◽  
Wide Range ◽  
Metal Additives

A rocket's engine usually uses fuel and oxygen as propellants to increase the rocket's projection during launch. Nowadays, metallic ingredients are commonly used in the rocket’s operation to increase its performance. Metallic ingredients have a high energy density, flame temperature, and regression rate that are important factors in the propulsion process. There is a wide range of additives have been reported so far as catalysts for rocket propulsion. The studies show that the presence of metal additives improves the regression rate, specific impulse and combustion efficiency. Herein, the common energetic additives for rocket propulsion such as metal and light metals are reviewed. Besides the effect of these energetic particles on the regression behaviors of base (hybrid) fuel has been exclusively discussed. This paper also proposed a new alloy namely high entropy alloys (HEAs) as a new energetic additive that can potentially increase the performance of the rocket propellant system.

scholarly journals A Recommender for Choosing Data Systems based on Application Profiling and Benchmarking

2021 ◽  
Author(s):  
Elton Figueiredo de Souza Soares ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Marcelo de Oliveira Costa Machado ◽  
Leonardo Guerreiro Azevedo
Keyword(s):  
Empirical Evidence ◽  
Informed Decision ◽  
Data System ◽  
Data Driven ◽  
Data Systems ◽  
Ongoing Work ◽  
Wide Range

In our data-driven society, there are hundreds of possible data systems in the market with a wide range of configuration parameters, making it very hard for enterprises and users to choose the most suitable data systems. There is a lack of representative empirical evidence to help users make an informed decision. Using benchmark results is a widely adopted practice, but like there are several data systems, there are various benchmarks. This ongoing work presents an architecture and methods of a system that supports the recommendation of the most suitable data system for an application. We also illustrates how the recommendation would work in a fictitious scenario.

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