Presentation is loading. Please wait.

Presentation is loading. Please wait.

Sarah Chan, Aima Ojehomon, Akshay Adya, Eno Inyang KOWLOON BAY.

Similar presentations


Presentation on theme: "Sarah Chan, Aima Ojehomon, Akshay Adya, Eno Inyang KOWLOON BAY."— Presentation transcript:

1 Sarah Chan, Aima Ojehomon, Akshay Adya, Eno Inyang KOWLOON BAY

2 Introduction

3

4 Team

5 Scope

6 Objectives Define Objectives… …Determine Priorities MACDADI Tool

7 Preferences Define Objectivs… …Determine Priorities MACDADI Tool

8 Alternatives Exit

9 Objectives Information Access Efficiency Layout Passenger Mobility Aesthetically Pleasing Clean Passenger Perception Vision 2020 Energy Cost Optimization HVAC Comfort Visual Comfort Passenger Comfort Congestion Analysis Egress HVAC Comfort Daylighting Energy Use Analysis

10 Passenger Mobility Congestion Baseline model peak 6pm weekday Show model, cite conges 28 minutes into rush hour Entrance B platform escalator Entrance A platform escalator

11 Passenger Mobility Congestion Baseline model peak 6pm weekday Show model, cite conges 28 minutes into rush hour Entrance B platform escalator Entrance A platform escalator

12 Passenger Mobility Congestion Baseline model peak 6pm weekday Show model, cite conges Entrance B platform escalator at 28 min

13 Passenger Mobility Congestion Baseline model peak 6pm weekday Show model, cite conges Entrance A platform escalator at 28 min

14 Passenger Mobility Congestion Traditional Method Use people per area as a determination of Congestion Problem Entity paths pre determined, therefore not valid measurement Solution Analogy  Cars in traffic Similar constraints: single lane, multiple goals Defining Congestion Testing Method Traffic Congestion Analysis Time in system - Peak : Time in system - Target Traffic Congestion Analysis Time in system - Peak : Time in system - Target

15 Passenger Mobility Congestion Traffic Congestion Analysis Time in system - Peak : Time in system - Target Traffic Congestion Analysis Time in system - Peak : Time in system - Target System Peak Weekdays 6pm hour 16,360 people System Target Sundays 6pm hour 7,300 people

16 Passenger Mobility Congestion Traffic Congestion Analysis Time in system - Peak : Time in system - Target Traffic Congestion Analysis Time in system - Peak : Time in system - Target 2.7 min

17 Passenger Mobility Congestion Traffic Congestion Analysis Time in system - Peak : Time in system - Target Traffic Congestion Analysis Time in system - Peak : Time in system - Target Objectives Score Evaluation Metric min min 5.94 min min min min min

18 Passenger Mobility Congestion Alternative 1 – Simple

19 Passenger Mobility Congestion Alternative 2 – Intensive

20 Passenger Mobility Congestion Results 2.7 m

21 Passenger Mobility Congestion Alternative 1 TargetPeakAlt 1Alt 2 Hours Minutes Alt 1Alt 2 % Reduction in time 58%54% Objective Rating 11

22 Passenger Mobility Egress - Data

23 Passenger Mobility Egress- Modelling 1390 people Randomly placed 50 % Male & 50% Female Low Stress, Co-operative Multi Agent System >10min7-10 min5-7 min4-5 min3-4min1-3min<1 min People Obstacles Exit Goal

24 Passenger Mobility Egress Baseline : 3 Exits Time : 5min 38 sec -1

25 Passenger Mobility Egress Baseline : 3 Exits Time : 5min 38 sec -1

26 Passenger Mobility Egress Alternative 1 : 4 Exits Time : 2 min 59 sec 1

27 Passenger Mobility Egress Alternative 1 : 4 Exits Time : 2 min 59 sec 1

28 Passenger Mobility Egress Alternative 2 : 5 Exits Time : 3 min 20 sec 0

29 Passenger Mobility Egress Alternative 2 : 5 Exits Time : 3 min 20 sec 0

30 Baseline (Tool : Hevacomp) General Parameters : 5-12 pm daily Kings Park, HK Glazed windows (Optifloat 6 mm argon) Design Temperature: Modeled as 26 °C Must be < 28 °C (summer) Max Temp outside air = 34 °C Only the Concourse Level is considered in the analysis. Energy Usage Cost Optimization

31 Process Energy Usage

32 Energy Analysis Energy Usage Cost Optimization Escalator Energy pertaining to the Concourse = ½ of total (split between concourse and platform )

33 Baseline Several open door entrances : Two 4 x 3.4 m and one 10.5 x 3 m and one 5 x 3 m 12 escalators Energy Usage Cost Optimization Baseline

34 Alternative 1 Add 1 entrance, 10.5 x 3 m Remove 2 windows Add 2 escalators (14 total) Energy Usage Cost Optimization Alternative 1

35 Alternative 2 Add 2 entrances, 10.5 x 3 m each Remove rooms near each entrance Add 1 escalator (13 total) 1 Energy Usage 0 Cost Optimization Alternative 2

36 Comparison Energy Usage Alternative 1 to the have the highest energy use, with 2 additional escalators Alternative 2 has the lowest energy use, even with 1 additional escalator 0 Cost Optimization Comaprison

37 Passenger Comfort Modelling

38 HVAC (TAS) Inputs | Internal Conditions

39 HVAC (TAS) Inputs | Apertures Alternative 1: ‘Wall Openings – Doors’ + ‘Window Openings (alt 1)’ Alternative 2: ‘Wall Openings – Doors’ + ‘Window Openings (alt 2)’ Baseline: ‘Wall Openings – Doors’

40 HVAC (TAS) Analyses Alternative 1 (& Baseline) Alternative 2

41 Passenger Comfort HVAC Inferences Creating these new openings has little to no effect on HVAC. Internal temp (35⁰C) at peak external temp (36⁰C), 7⁰C over target temp (28⁰C).

42 HVAC Evaluation | Metrics

43 Daylighting Inputs | Revit

44 Daylighting Analyses | Shadow

45 Daylighting Analyses | Shadow

46 Daylighting Analyses | Illuminance

47 Daylighting Analyses | Illuminance (Baseline & Alt 1)

48 Daylighting Analyses | Illuminance (Alt 2)

49 Daylighting Inferences | General Alternative 2, with 2 more openings has a positive effect on daylighting

50 Daylighting Evaluation

51 Impacts

52 Values

53 Thank You By the way….. We made some news !news


Download ppt "Sarah Chan, Aima Ojehomon, Akshay Adya, Eno Inyang KOWLOON BAY."

Similar presentations


Ads by Google