KR20140000857A - Surface roughness model - Google Patents

Surface roughness model Download PDF

Info

Publication number
KR20140000857A
KR20140000857A KR1020120068383A KR20120068383A KR20140000857A KR 20140000857 A KR20140000857 A KR 20140000857A KR 1020120068383 A KR1020120068383 A KR 1020120068383A KR 20120068383 A KR20120068383 A KR 20120068383A KR 20140000857 A KR20140000857 A KR 20140000857A
Authority
KR
South Korea
Prior art keywords
surface roughness
wind
area
roughness
road
Prior art date
Application number
KR1020120068383A
Other languages
Korean (ko)
Inventor
이영규
Original Assignee
(주)큐버솔루션
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by (주)큐버솔루션 filed Critical (주)큐버솔루션
Priority to KR1020120068383A priority Critical patent/KR20140000857A/en
Publication of KR20140000857A publication Critical patent/KR20140000857A/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/08—Construction

Landscapes

  • Business, Economics & Management (AREA)
  • Environmental & Geological Engineering (AREA)
  • Engineering & Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Ecology (AREA)
  • Environmental Sciences (AREA)
  • Processing Or Creating Images (AREA)

Abstract

The present invention relates to a surface roughness model. The purpose of the present invention is to create a surface roughness database in consideration to the type of wind in each direction and evaluating the surface roughness according to the classification of a land surface map. According to the embodiment of the present invention, the standard for a domestic surface roughness and the foundation of the DB management are prepared using the surface roughness model.

Description

Surface Roughness Model

Development of surface roughness model to describe strong wind risk assessment

In the structural standard for buildings, the effect of surface roughness is concisely processed by using Exposure Categories. For example, the Architectural Structural Design Standard (Korean Institute of Architecture 2005) classifies open areas as road classification C and residential areas as road classification B. Most national and international design standards, as well as domestic structural design standards, have adopted this approach to define the basic wind speed that represents the wind speed 10 m above the open area. The influence of the actual surface roughness by region is considered by modifying the wind speed by the index according to the wind roughness corresponding to the surface roughness.

Surface roughness is defined using roughness length expressed as z 0 . The roughness length depends on the height and spatial arrangement of buildings, trees and other obstacles on the ground. Given wind speed at open area, surface roughness length, and wind-side area, the actual wind speed at the surface can be estimated.

Over the past decades, numerous studies have been conducted to distinguish z 0 using surface features. However, until now, there has been no consistent result among researchers. Wieringa (1992, 1993) has presented in the past 30 years field experiments, numerical studies, and wind tunnel surveys to make the use of noteworthy studies in terms of roughness length as a reasonable measure. Simiu and Scanlan (1996) also combine the roughness lengths of various researchers and suggest the roughness lengths in various surface situations. Wieringa (1992, 1993) suggests the roughness length for urbanized areas consisting of buildings. Lettau (1969) proposes an equation for estimating the effective roughness length of a region.

There is no domestic indicator roughness standard and indicator roughness model and DB for strong wind risk assessment.

The land roughness map is used to evaluate the surface roughness according to the classification of the land cover map and to make the surface roughness DB considering wind direction by wind direction.

Lay the foundation for standardization of domestic surface roughness and DB management.

Open road classification Surrounding surface state A A large area of high-rise buildings with more than 10 stories in the center of a large city B Areas where buildings such as houses of 3.5m height are concentrated (areas interspersed with mid-rise buildings) C Areas scattered with buildings 1.5m to 10m in height (areas interspersed with low-rise buildings) D Almost no obstacles and the average height of surrounding obstacles is 1.5m or less (shore, grassland airfield)

The Architectural Structural Design Standard (Korean Institute of Architects 2005) defines the four levels of open roads, and [Table 1] describes the surface roughness conditions according to the open roads. Open-air road category A, which has the highest surface roughness, is an area where large-scale buildings with more than 10 floors are concentrated, such as the center of a large city. On the land cover map used in this study, all of them have limitations, which are represented by urbanized areas regardless of the height of buildings. Based on the judgment that it is impossible to select classification A of the road map using land cover map, this study prepared criteria to classify B, C, and D except A through land cover map.

Even when surface roughness is calculated at the same point, surface roughness varies according to the wind direction. In general, when the surface roughness conditions in the wind-side region vary, the surface roughness is evaluated by determining the average surface roughness situation in the wind-side region. The wind up side area depends on the height under consideration. Building load criteria and commentary (Korean Institute of Architects' 2000) define about 30 times the height of the building as the wind-side area. According to the Road Bridge Design Standard (Korea Road Traffic Association 2005), the area is at least 500m and the upper side of the bridge is about 100 times the height of the bridge. Table 2 shows the upwind regions defined by the Australian / New Zealand Standards (AS / NZS 2002). If the height is less than 50m, the wind side area is considered to be 1,000m. In this study, we are interested in wind speed at 10m above the ground and defined each wind direction area as 1km by combining each reference.

Structure height Windward side area

Figure pat00001
1,000 m
Figure pat00002
 2,000 m
Figure pat00003
 3,000 m
Figure pat00004
 4,000m

Surface roughness was calculated for the 8-direction wind. The surface roughness was calculated in consideration of the average surface roughness of the fan-shaped wind-like region with a 45-degree interior angle and a radius of 1 km. In order to determine the average surface roughness, weights of land cover maps were assigned as shown in [Table 3] through the study of roughness length according to land cover map classification (Wieringa 1993, Simiu and Scanlan 1996) and engineering intuition.

Land cover map classification Division weight

Figure pat00005
Wieringa (1993)

z 0 (m) Simiu and Scanlan (1996)
z 0 (m) Body of water 0 0.0002 - Urbanization zone 12 0.4-1.5 0.20-3.00 Naji 0.5 0.001-0.004 0.02-0.03 marsh One - - Grassland One 0.008-0.03 0.01-0.04 Forest 15 0.35-1.6 0.10-1.00 Rice field One 0.04-0.18 0.04-0.10 field One 0.04-0.18 0.04-0.10

The land cover map situation and the condition of the road map were linked as shown in [Table 4]. In the case where the ratio of forests and urbanized areas to the upper side of the wind-up area exceeds 50%, the criteria for evaluating the road breeze category B, an area where buildings are concentrated, is set. If the wind-side area consists only of water bodies or only grassland, it can be said that it is clearly classified as road category D. In addition, the upwind region, which consists of land cover map divisions with small roughness components of cropland, wetland, grassland, bare land, and body of water, can be considered as an area with few obstacles. Open road classification C includes cases that do not correspond to B and D.

Open road classification Land cover map situation B More than half of urbanized forests and forests in the upwind region C If the upstream area consists only of water bodies
If the upwind area consists only of grassland
If the upstream area contains water bodies, and other areas consist of grassland, agricultural land (fields or rice fields), wetlands, and bare land
In the upper side, bare land is included, and other areas consist of grassland, agricultural land (fields or rice fields), wetlands, and water bodies.
If the upstream area consists of water bodies and bare land D Does not satisfy B and D

In order to quantitatively evaluate the classification of road breeze based on the qualitative criteria of the road breeze classification according to the land cover map situation presented in [Table 4], the quantitative evaluation criteria are prepared as shown in [Table 5]. In the case of the urbanized area, the road class B was evaluated through the fact that not only the building structure but also the small roughness elements such as roads and sidewalks were included and the value of the wind side except for the urbanized area and the forest. In order to satisfy the quantitative criterion of the draft degree D, the average of the division weights is less than one. In the case of the road-class classification C, it was set as an area which does not satisfy the conditions of B and D.

Land cover map segment weighted average

Figure pat00006
Open road classification
Figure pat00007
 B (z 0 = 0.0053)
Figure pat00008
 C (z 0 = 0.07)
Figure pat00009
 D (z 0 = 0.005)

Figure pat00010

The figure above shows a surface roughness model for the 2000-land cover map: north wind.

Claims (3)

Surface roughness evaluation using large-scale land cover map for high wind risk assessment Development of surface roughness standard in claim 1 DB of domestic surface roughness according to claim 2
KR1020120068383A 2012-06-26 2012-06-26 Surface roughness model KR20140000857A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020120068383A KR20140000857A (en) 2012-06-26 2012-06-26 Surface roughness model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
KR1020120068383A KR20140000857A (en) 2012-06-26 2012-06-26 Surface roughness model

Publications (1)

Publication Number Publication Date
KR20140000857A true KR20140000857A (en) 2014-01-06

Family

ID=50138583

Family Applications (1)

Application Number Title Priority Date Filing Date
KR1020120068383A KR20140000857A (en) 2012-06-26 2012-06-26 Surface roughness model

Country Status (1)

Country Link
KR (1) KR20140000857A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20210114226A (en) * 2020-03-10 2021-09-23 (주)큐버솔루션 Real-time wind direction forecast service apparatus using artificial neural network and its method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20210114226A (en) * 2020-03-10 2021-09-23 (주)큐버솔루션 Real-time wind direction forecast service apparatus using artificial neural network and its method

Similar Documents

Publication Publication Date Title
Jiao et al. Geographic field model based hedonic valuation of urban open spaces in Wuhan, China
Shi et al. Urban ventilation corridors and spatiotemporal divergence patterns of urban heat island intensity: a local climate zone perspective
CN107886216A (en) A kind of land carrying capacity analysis method based on Evaluation of Land Use Suitability
Tandel et al. Urban corridor noise pollution: A case study of Surat city, India
Leń et al. Determination of the urgency of undertaking land consolidation works in the villages of the Sławno municipality
Chang et al. Measuring the influence of the greening design of the building environment on the urban real estate market in Taiwan
CN108875290A (en) Resosurces environment loading capacity method for early warning
Leśniok et al. Classification of circulation types for Southern Poland with an application to air pollution concentration in Upper Silesia
Dunichkin et al. Studies and evaluation of bioclimatic comfort of residential areas for improving the quality of environment
Shach-Pinsly et al. Multiparametric analysis of urban environmental quality for estimating neighborhood renewal alternatives
Guttikunda Impact analysis of brick kilns on the air quality in Dhaka, Bangladesh
Su et al. Coupling relationships between urban form and performance of outdoor environment at the pedestrian level
Khwedim et al. Trace metals in topsoils near the Babylon Cement Factory (Euphrates River) and human health risk assessment
Burian et al. Morphological analyses using 3D building databases: Los Angeles, California
Ayutthaya et al. The influence of bioretention assets on outdoor thermal comfort in the urban area
KR20140000857A (en) Surface roughness model
Boinas et al. Rising damp in Portuguese cultural heritage–a flood risk map
Feng et al. Scaling from field to region for wind erosion prediction using the Wind Erosion Prediction System and geographical information systems
Santamouris The Athens urban climate experiment
Hyde Assessing urban heat island mitigation capacities of green infrastructure to address heat vulnerability inequities in San Francisco, California
Lau et al. Applying ‘Local Climate Zone (LCZ)’into a High-density Highrise Cities-A Pilot Study in Hong Kong
Urias et al. Identification and classification of local climate zones in a semi-arid city of northwestern Mexico
Hedayati-Dezfuli et al. Evaluation of seasonal changes in temperature and precipitation for Iran five provincial centres during 1960-2017
Fam et al. Spatial analysis of socioeconomic deprivation in Peninsular Malaysia
Costa et al. The green roof thermal performance evaluation in comparison to asbestos cement tiles applied to light steel frame Brazilian buildings

Legal Events

Date Code Title Description
WITN Withdrawal due to no request for examination