Experimental Set Up & Data
Tina Lasisi
January 19, 2022
Last updated: 2022-01-19
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Knit directory: HairManikin_manuscript/
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0.1 Introduction
In brief, the experiments were carried out in a climate controlled chamber at the University of Loughborough (UK) using a full-body thermal manikin with 20 independently controllable zones. Human hair wigs ranging from straight to tightly curled were used to test the effect of hair on heat gain/loss in a number of conditions.
The experimental variables were: - head covering (no wig, straight wig, moderately curled wig, tightly curled wig) - radiation (on vs. off) - wind speed (0.3m/s, 1m/s, 2.5m/s) - evaporation (manikin with dry scalp vs. manikin with wet scalp)
0.2 Set-up
Dry heat resistance experiments were carried out from October to November 2018 in a climate controlled chamber with a custom-built wind tunnel (see below).
knitr::include_graphics("assets/manikin-chamber-setup.jpeg", error = FALSE)
| Version | Author | Date |
|---|---|---|
| afab42a | Tina Lasisi | 2021-10-24 |
knitr::include_graphics("assets/manikin-chamber-setup-diagram.png", error = FALSE)
| Version | Author | Date |
|---|---|---|
| c5d6198 | Tina Lasisi | 2021-10-24 |
0.3 Data
The raw data from the manikin is structured as follows
raw_manikin <- read_csv(F("data/raw_manikin_output/round_1/HighCurv_RadOff_wind0_3 (run with new conditions).csv"), skip = 16) %>%
clean_names()
head(raw_manikin)# A tibble: 6 × 79
x1 x2 face_3 head_4 r_up_arm_fr_5 r_up_arm_bk_6 l_up_arm_fr_7
<chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Time Clock Averag… Averag… Average Surf… Average Surf… Average Surf…
2 Seconds <NA> degC degC degC degC degC
3 30.0117166 15:27:08 37.99 37.96 37.94 37.97 37.95
4 60.0264333 15:27:38 37.98 37.95 37.96 37.97 37.96
5 90.04015 15:28:08 37.96 37.95 37.96 37.97 37.97
6 120.0548667 15:28:38 37.94 37.94 37.97 37.97 37.98
# … with 72 more variables: l_up_arm_bk_8 <chr>, r_forearm_fr_9 <chr>,
# r_forearm_bk_10 <chr>, l_forearm_fr_11 <chr>, l_forearm_bk_12 <chr>,
# r_hand_13 <chr>, l_hand_14 <chr>, upper_chest_15 <chr>, shoulders_16 <chr>,
# stomach_17 <chr>, mid_back_18 <chr>, waist_19 <chr>, lower_back_20 <chr>,
# r_up_thigh_fr_21 <chr>, r_up_thigh_grd_22 <chr>, r_up_thigh_bk_23 <chr>,
# l_up_thigh_fr_24 <chr>, l_up_thigh_grd_25 <chr>, l_up_thigh_bk_26 <chr>,
# r_lwr_thigh_fr_27 <chr>, r_lwr_thigh_bk_28 <chr>, …The data can be cleaned using the following pipeline.
library(lubridate)
library(fs)
data_dir1 <- F("data/raw_manikin_output/round_1")
data_dir2 <- F("data/raw_manikin_output/round_2")
data_dir3 <- F("data/raw_manikin_output/round_3")
round1 <- fs::dir_ls(data_dir1, regexp = "\\.csv$")
round2 <- fs::dir_ls(data_dir2, regexp = "\\.csv$")
round3 <- fs::dir_ls(data_dir3, regexp = "\\.csv$")
round<-c(round1,round2,round3)
raw_manikin<- round %>%
map_dfr(read_csv,.id="source",skip=16)%>%
clean_names() %>%
select(c(source, x1, x2, contains("head"), ambient_71:ambient_74)) %>%
rename(filepath = source,
time_elapsed = x1,
time_hms = x2,
surface_temp = head_4,
heatflux = head_38,
avg_amb= ambient_71,
amb_1 = ambient_72,
amb_2 = ambient_73,
rh = ambient_74)%>%
slice(-(1:2)) %>%
as_tibble() %>%
mutate(time_hms = hms(time_hms)) %>%
mutate(time_elapsed = dseconds(time_elapsed)) %>%
select(time_elapsed, time_hms, everything())
raw_manikin$round=substr(raw_manikin$filepath,regexpr("round",raw_manikin$filepath),regexpr("round",raw_manikin$filepath)+6)
raw_manikin$filepath <- sub( ".*output/","", raw_manikin$filepath)
head(raw_manikin)# A tibble: 6 × 10
time_elapsed time_hms filepath surface_temp heatflux avg_amb
<Duration> <Period> <chr> <chr> <chr> <chr>
1 30.0117166s 15H 27M 8S round_1… 37.96 87.0 1.76
2 60.0264333s (~1 minutes) 15H 27M 38S round_1… 37.95 90.4 1.72
3 90.04015s (~1.5 minutes) 15H 28M 8S round_1… 37.95 92.0 1.67
4 120.0548667s (~2 minutes) 15H 28M 38S round_1… 37.94 93.7 1.62
5 150.0695835s (~2.5 minutes) 15H 29M 8S round_1… 37.94 95.0 1.57
6 180.0843002s (~3 minutes) 15H 29M 38S round_1… 37.94 96.7 1.53
# … with 4 more variables: amb_1 <chr>, amb_2 <chr>, rh <chr>, round <chr>0.3.1 Variables extracted
Since we are only interested in the head, all other zones have been dropped. The variables are as follows:
time_elapsed(in seconds) describes how long the experiment has been runningsurface_temp(deg C) describes surface temperature for head zoneheatflux(W/m2) describes heat loss in watts per meter square.avg_amb(deg C) describes average ambient temperature based on two measurementsamb_1(deg C) first ambient temperature sensoramb_2(deg C) second ambient temperature sensorrh(percentage) describes relative humidity in the chamber.
raw_manikin_long <- raw_manikin %>%
pivot_longer(cols = surface_temp:rh, names_to = "var")raw_manikin_long %>%
ggplot(aes(x = time_elapsed,
y = value,
col = var)) +
geom_line() +
facet_grid(var ~ ., scales = "free")
0.3.2 Variables calculated
A stable period is then chosen to average.
Some information required for the calculations includes the Area of skin surface on the manikin (Ask), which is 0.0978 in meters squared.
start_time_mins = 15
end_time_mins = 40
area = 0.0978
start_time = start_time_mins*60
end_time = end_time_mins*60
# raw_manikin %>%
# filter(time_elapsed > start_time & time_elapsed < end_time) %>%
# mutate(heat_resistance = (surface_temp-avg_amb)/ max(0.01, heatflux)) %>%
# mutate(clo = heat_resistance/0.155)
# Two additional variables are calculated: heat_resistance and clo. Both are measures of insulation.
Heat resistance is calculated from the difference between the surface temperature and the ambient temperature
Other calculations:
Net solar influx (W/m2) is calculated as:
\[heatflux_{(radiation)}- heatflux_{(no \ radiation)}\]
temperature correction for 30C (no radiation) is calculated as:
\[5/heat\ resistance_{(no \ radiation)} \] temperature correction for 30C (radiation) is calculated as:
\[temp\ corrected \ heat loss_{(30C)} - solar\ influx \]
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] fs_1.5.0 janitor_2.1.0 lubridate_1.8.0 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4 readr_2.0.2
[9] tidyr_1.1.4 tibble_3.1.5 ggplot2_3.3.5 tidyverse_1.3.1
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.7 assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.28
[5] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0 backports_1.2.1
[9] reprex_2.0.1 evaluate_0.14 highr_0.9 httr_1.4.2
[13] pillar_1.6.4 rlang_0.4.12 readxl_1.3.1 rstudioapi_0.13
[17] whisker_0.4 jquerylib_0.1.4 rmarkdown_2.11 labeling_0.4.2
[21] bit_4.0.4 munsell_0.5.0 broom_0.7.9 compiler_4.1.2
[25] httpuv_1.6.3 modelr_0.1.8 xfun_0.27 pkgconfig_2.0.3
[29] htmltools_0.5.2 tidyselect_1.1.1 fansi_0.5.0 crayon_1.4.1
[33] tzdb_0.1.2 dbplyr_2.1.1 withr_2.4.2 later_1.3.0
[37] grid_4.1.2 jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.1
[41] DBI_1.1.1 git2r_0.28.0 magrittr_2.0.1 scales_1.1.1
[45] vroom_1.5.5 cli_3.1.0 stringi_1.7.5 farver_2.1.0
[49] promises_1.2.0.1 snakecase_0.11.0 xml2_1.3.2 ellipsis_0.3.2
[53] generics_0.1.0 vctrs_0.3.8 tools_4.1.2 bit64_4.0.5
[57] glue_1.4.2 hms_1.1.1 parallel_4.1.2 fastmap_1.1.0
[61] yaml_2.2.1 colorspace_2.0-2 rvest_1.0.2 knitr_1.36
[65] haven_2.4.3