Climatology of snowfall and related meteorological variables with application to roof snow load specifications

1977 ◽  
Vol 4 (2) ◽  
pp. 240-256 ◽  
Author(s):  
N. Isyumov ◽  
M. Mikitiuk
Keyword(s):  
Statistical Models ◽  
Meteorological Data ◽  
Aerodynamic Characteristics ◽  
Extreme Value Statistics ◽  
Meteorological Variables ◽  
Snow Hydrology ◽  
Design Problems ◽  
Snow Load ◽  
Roof Shape ◽  
Engineering Design Problems

Representative statistical models of snowfall and related meteorological variables, important in snow hydrology research, are also required for many engineering design problems. Design roof snow loads, if based on the balance between deposition of snow by individual snowfalls and subsequent depletion by wind action and various thermodynamic mechanisms, in addition to aerodynamic characteristics of particular roof shapes, require the development of statistical models of various meteorological variables which influence the process.Mathematical models suitably describing the statistics of individual snowfall amounts, wind speed and directions, and air temperature are presented in this paper. These results are based on the analysis of daily meteorological data obtained at some 28 Canadian stations with typical record lengths of about 30 years. The suitability of the developed probability distribution of daily snowfall magnitudes is examined through comparisons of both derived parent and extreme value statistics with available full scale observations. The joint statistics of these meteorological variables are also examined and a methodology for approximately accounting for their interdependence is presented.The relevance of the overall winter climatology of an area to roof snow load formation is discussed. Results of a Monte Carlo simulation, which show the dependence of the snow load for a particular roof shape on the climatology of snowfall and related meteorological variables, are presented.

A Probabilistic Approach to the Prediction of Snow Loads

1974 ◽  
Vol 1 (1) ◽  
pp. 28-49 ◽  
Author(s):  
N. Isyumov ◽  
A. G. Davenport
Keyword(s):  
Formation Process ◽  
Extreme Values ◽  
Meteorological Data ◽  
Digital Simulation ◽  
Probabilistic Approach ◽  
Snow Accumulation ◽  
Meteorological Variables ◽  
Snow Load ◽  
Design Values ◽  
Snow Loads

The magnitudes of loads imposed by snow depend upon a number of climatological and meteorological variables and as a result exhibit marked variations geographically, due to local effects within a particular region, and with time. The snowload formation process, which depends both on the macro- and microclimates of such meteorological variables as the depth of the snowfall, the snowfall density, wind speed, air temperature etc., as well as, the size and geometry of particular roofs and the influence of their immediate environment, is discussed.A model of the snow load formation process based on a mass balance approach, which takes into account the deposition of snow by individual snowfalls and the depletion of the snow load by wind action and thermal effects, is introduced. The use of this approach requires the establishment of statistical descriptions of the various meteorological variables, as well as a knowledge of the physical process of snow accumulation and depletion for a particular roof. The statistical properties of some of the more important meteorological variables are discussed. Also presented are some model derived data of snow accumulation and depletion for particular roofs located in different terrain.It is shown that even relatively simple statistical descriptions of the relevant meteorological data and snow accumulation and depletion mechanisms can lead to realistic predictions of roof snow loads. Snow loads on a flat roof are generated by a digital simulation technique and compared with full scale observations. Annual extreme values of the simulated snow load process are presented and compared with currently specified design values. Comments are made regarding the practicability of this approach.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

scholarly journals A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4613
Author(s):  
Shah Fahad ◽  
Shiyou Yang ◽  
Rehan Ali Khan ◽  
Shafiullah Khan ◽  
Shoaib Ahmed Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Design Problems ◽  
Swarm Optimization ◽  
Electromagnetic Design ◽  
Quantum Particle Swarm Optimization ◽  
Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

scholarly journals Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiming Li ◽  
Yongquan Zhou ◽  
Sen Zhang ◽  
Junmin Song
Keyword(s):  
Engineering Design ◽  
Function Optimization ◽  
Superior Performance ◽  
Local Optimum ◽  
Lévy Flight ◽  
Design Problems ◽  
Levy Flight ◽  
Spiral Path ◽  
Straight Line ◽  
Engineering Design Problems

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

scholarly journals Impact of meteorological factors on number of new COVID-19 cases in Pune

2021 ◽  
Vol 8 (9) ◽  
pp. 4307
Author(s):  
Saurabh Mahajan ◽  
Ravi Devarakonda ◽  
Gautam Mukherjee ◽  
Nisha Verma ◽  
Kumar Pushkar
Keyword(s):  
Meteorological Data ◽  
Secondary Data ◽  
Significant Negative Correlation ◽  
Meteorological Variables ◽  
Strong Positive Correlation ◽  
Daily Number ◽  
Average Temperature ◽  
Time Period ◽  
Indian Meteorological Department ◽  
The Impact

Background: Coronaviruses are a family of viruses that can result in different types of illnesses, most commonly, as Severe acute respiratory syndrome (SARS). Researches have shown that the atmospheric variables and the density of population have affected the transmission of the disease. Meteorological variables like temperature, humidity among others have found to affect the rise of pandemic in positive or negative ways.  Respiratory virus illnesses have shown seasonal variability since the time they have been discovered and managed. This study investigated the relationship between the meteorological variables of temperature, humidity and precipitation in the spread of COVID-19 disease in the city of Pune.Methods: This record based descriptive study is conducted after secondary data analysis of number of new cases of COVID-19 per day from the period 01 May to 24 December 2020 in Pune. Meteorological data of maximum (Tmax), minimum (Tmin) and daily average temperature (Tavg), humidity and precipitation were daily noted from Indian meteorological department website. Trend was identified plotting the daily number of clinically diagnosed cases over time period. Pearson’s correlation was used to estimate association between meteorological variables and daily detected fresh cases of COVID-19 disease.  Results: Analysis revealed significant negative correlation (r=-0.3563, p<0.005) between daily detected number of cases and maximum daily temperature. A strong positive correlation was seen between humidity and daily number of cases (r=0.5541, p<0.005).Conclusions: The findings of this study will aid in forecasting epidemics and in preparing for the impact of climate change on the COVID epidemiology through the implementation of public health preventive measures.

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