The Development of Computer Models for the Prediction of Factory Noise

1995 ◽  
Vol 2 (2) ◽  
pp. 437-454 ◽  
Author(s):  
S.M. Dance
Keyword(s):  
Mathematical Models ◽  
Ray Tracing ◽  
Computer Technology ◽  
Prediction Models ◽  
Noise Prediction ◽  
Image Source ◽  
The Past ◽  
Source Models ◽  
Insight Into ◽  
Current Potential

The development of factory noise prediction models over the past thirty years has parallelled the development of computer technology. There are currently two main approaches to this problem both of which have been extensively investigated, they are the image-source method and the ray-tracing technique. Presented are brief details of the background to the specific problems of factory noise prediction together with the various approaches used to solve the problem. An outline of how the two types of mathematical models work, together with details of their representational development is presented. A comparison of the current potential of each type of model, enabling an insight into when and where each type of model may be effectively used, is given. Three independent studies comparing various prediction models were considered. All three reviews drew the conclusion that the Ondet and Barbry, and the Lindqvist models were the most accurate ray-tracing and image-source models, respectively. Finally, a review of barrier prediction models is presented.

scholarly journals On numerical solver selection and related uncertainty terminology

2009 ◽  
Vol 12 (3) ◽  
pp. 241-250 ◽  
Author(s):  
Petra Claeys ◽  
Ann van Griensven ◽  
Lorenzo Benedetti ◽  
Bernard De Baets ◽  
Peter A. Vanrolleghem
Keyword(s):  
Fluid Dynamics ◽  
Mathematical Models ◽  
Process Design ◽  
Uncertainty Assessment ◽  
Environmental Modelling ◽  
Chemical Systems ◽  
The Past ◽  
Numerical Solver ◽  
Physical And Chemical ◽  
Insight Into

Mathematical models provide insight into numerous biological, physical and chemical systems. They can be used in process design, optimisation, control and decision support, as acknowledged in many different fields of scientific research. Mathematical models do not always yield reliable results and uncertainty should be taken into account. At present, it is possible to identify some factors contributing to uncertainty, and the awareness of the necessity of uncertainty assessment is rising. In the fields of Environmental Modelling and Computational Fluid Dynamics, for instance, terminology related to uncertainty exists and is generally accepted. However, the uncertainty due to the choice of the numerical solver and its settings used to compute the solution of the models did not receive much attention in the past. A motivating example on the existence and effect of numerical uncertainty is provided and clearly shows that we can no longer ignore it. This paper introduces a new terminology to support communication about uncertainty caused by numerical solvers, so that scientists become perceptive to it.

scholarly journals Methods to optimize energy consumption in Conarc furnaces

2021 ◽  
Vol 3 (12) ◽  
Author(s):  
Aashay Wanjari
Keyword(s):  
Energy Consumption ◽  
Mathematical Models ◽  
Raw Materials ◽  
Maximum Efficiency ◽  
Steelmaking Process ◽  
The Past ◽  
Annual Productivity ◽  
Produce Steel ◽  
Insight Into ◽  
Over Time

AbstractOver the past few decades, steelmaking has reached its zenith in terms of annual productivity, and relevant processes have been developed over time to produce steel with maximum efficiency in a shorter time. One of the prominent steelmaking practices used extensively in contemporary industries is the Conarc Steelmaking Practice, which involves the use of electrical and chemical Energy to carry out melting and decarburization in respective shells. This article reviews the factors that affect the energy consumption in Conarc furnaces and provides insight into the technologies developed to alleviate energy consumption and make the steelmaking process optimal in terms of energy consumption and requirement. This article also accentuates relevant systems and melting practices for the raw materials, which can be utilized in the Conarc Steelmaking practice to make the entire process less energy-intensive. Oxygen-enhanced combustion and thermophotovoltaic systems can alleviate energy consumption substantially while maintaining steel quality at the same time, as discussed in the paper. Additionally, some mathematical models have been discussed that facilitate in formulating an energy optimal and financial steelmaking process.

Recovery boiler sootblowers: History and technological advances

TAPPI Journal ◽  
2015 ◽  
Vol 14 (1) ◽  
pp. 51-60
Author(s):  
HONGHI TRAN ◽  
DANNY TANDRA
Keyword(s):  
Cfd Modeling ◽  
Computing Systems ◽  
The Past ◽  
Technological Advances ◽  
Recovery Boilers ◽  
Computational Fluid Dynamics Cfd ◽  
Recovery Boiler ◽  
Steam Parameters ◽  
Insight Into ◽  
Deposit Removal

Sootblowing technology used in recovery boilers originated from that used in coal-fired boilers. It started with manual cleaning with hand lancing and hand blowing, and evolved slowly into online sootblowing using retractable sootblowers. Since 1991, intensive research and development has focused on sootblowing jet fundamentals and deposit removal in recovery boilers. The results have provided much insight into sootblower jet hydrodynamics, how a sootblower jet interacts with tubes and deposits, and factors influencing its deposit removal efficiency, and have led to two important innovations: fully-expanded sootblower nozzles that are used in virtually all recovery boilers today, and the low pressure sootblowing technology that has been implemented in several new recovery boilers. The availability of powerful computing systems, superfast microprocessors and data acquisition systems, and versatile computational fluid dynamics (CFD) modeling capability in the past two decades has also contributed greatly to the advancement of sootblowing technology. High quality infrared inspection cameras have enabled mills to inspect the deposit buildup conditions in the boiler during operation, and helped identify problems with sootblower lance swinging and superheater platens and boiler bank tube vibrations. As the recovery boiler firing capacity and steam parameters have increased markedly in recent years, sootblowers have become larger and longer, and this can present a challenge in terms of both sootblower design and operation.

Upcoming Drifts In Bio-Similars

2020 ◽  
Vol 15 ◽  
Author(s):  
Geeta Aggarwal ◽  
Manju Nagpal ◽  
Ameya Sharma ◽  
Vivek Puri ◽  
Gitika Arora Dhingra
Keyword(s):  
Metabolic Diseases ◽  
Market Competition ◽  
Current Status ◽  
Medicinal Products ◽  
The Past ◽  
Biologic Activity ◽  
Emerging Trends ◽  
Regulatory Guidelines ◽  
Food Drug Administration ◽  
Insight Into

Background: Biopharmaceuticals such as Biologic medicinal products have been in clinical use over the past three decades and have benefited towards the therapy of degenerative and critical metabolic diseases. It is forecasted that market of biologics will be going to increase at a rate of 20% per year, and by 2025, more than ˃ 50% of new drug approvals may be biological products. The increasing utilization of the biologics necessitates for cost control, especially for innovators products that have enjoyed a lengthy period of exclusive use. As the first wave of biopharmaceuticals is expired or set to expire, it has led to various opportunities for the expansion of bio-similars i.e. copied versions of original biologics with same biologic activity. Development of biosimilars is expected to promote market competition, meet worldwide demand, sustain the healthcare systems and maintain the incentives for innovation. Methods: Appraisal of published articles from peer reviewed journals, PubMed literature, latest news and guidelines from European Medicine Agency, US Food Drug Administration (FDA) and India are used to identify data for review. Results: Main insight into the quality requirements concerning biologics, current status of regulation of biosimilars and upcoming challenges lying ahead for the upgrading of marketing authorization of bio-similars has been incorporated. Compiled literature on therapeutic status, regulatory guidelines and the emerging trends and opportunities of biosimilars has been thoroughly stated. Conclusion: Updates on biosimilars will support to investigate the possible impact of bio-similars on healthcare market.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

Cybersecurity Teamwork: A Review of Current Practices and Suggested Improvements

2020 ◽  
Vol 64 (1) ◽  
pp. 451-455
Author(s):  
Richard J. Simonson ◽  
Joseph R. Keebler ◽  
Mathew Lessmiller ◽  
Tyson Richards ◽  
John C. Lee
Keyword(s):  
Cyber Security ◽  
Cyber Attacks ◽  
Team Science ◽  
The Past ◽  
Insight Into ◽  
Current Practices

As cyber-attacks and their subsequent responses have become more frequent and complex over the past decade, research into the performance and effectiveness of cybersecurity teams has gained an immense amount of traction. However, investigation of teamwork in this domain is lacking due to the exclusion of known team competencies and a lack of reliance on team science. This paper serves to provide insight into the benefit that can be gained from utilizing the extant teamwork literature to improve teams’ research and applications in the domain of cyber-security.

CPT-based liquefaction case histories compiled from three earthquakes in Canterbury, New Zealand

2021 ◽  
pp. 875529302199636
Author(s):  
Mertcan Geyin ◽  
Brett W Maurer ◽  
Brendon A Bradley ◽  
Russell A Green ◽  
Sjoerd van Ballegooy
Keyword(s):  
New Zealand ◽  
Case History ◽  
Prediction Models ◽  
Free Field ◽  
Case Histories ◽  
Cone Penetration ◽  
History Data ◽  
The Past ◽  
Surface Manifestation ◽  
The Many

Earthquakes occurring over the past decade in the Canterbury region of New Zealand have resulted in liquefaction case-history data of unprecedented quantity. This provides the profession with a unique opportunity to advance the prediction of liquefaction occurrence and consequences. Toward that end, this article presents a curated dataset containing ∼15,000 cone-penetration-test-based liquefaction case histories compiled from three earthquakes in Canterbury. The compiled, post-processed data are presented in a dense array structure, allowing researchers to easily access and analyze a wealth of information pertinent to free-field liquefaction response (i.e. triggering and surface manifestation). Research opportunities using these data include, but are not limited to, the training or testing of new and existing liquefaction-prediction models. The many methods used to obtain and process the case-history data are detailed herein, as is the structure of the compiled digital file. Finally, recommendations for analyzing the data are outlined, including nuances and limitations that users should carefully consider.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

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