K9 Thunder

K9 Thunder

1 About K9 Thunder

The K9 Thunder is a South Korean self-propelled 155 mm howitzer designed and developed by the Agency for Defense Development and Samsung Aerospace Industries for the Republic of Korea Armed Forces, and is now manufactured by Hanwha Defense. K9 howitzers operate in groups with the K10 automatic ammunition resupply vehicle variant. The entire K9 fleet operated by the ROK Armed Forces is now undergoing upgrades to K9A1 standard, and a further development of a K9A2 variant is in process.

For more see: https://en.wikipedia.org/wiki/K9_Thunder

https://www.army-technology.com/projects/thunderselfpropelled/

https://eurasiantimes.com/after-india-australia-to-acquire-k9-thunder-self-propelled-howitzers-from-china/

2 Data

All data for long range guns (LRG) is here: https://en.wikipedia.org/wiki/List_of_railway_artillery

Some interesting statistics is here:http://rpubs.com/alex-lev/357849

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(DT)
lrg <- read.delim("lrg.txt")
datatable(lrg)
#lrg

3 Linear Regression Model

Here we fit linear regression model for data with Range_M < 70000. For more about linear model see: https://rpubs.com/alex-lev/360385

lrg2 <- lrg%>%filter(Range_M<70000)
fit.lrg.2 <- lm(Range_M~Velocity_MPS+Barrel_M+Elevation,data = na.omit(lrg2))
summary(fit.lrg.2)
## 
## Call:
## lm(formula = Range_M ~ Velocity_MPS + Barrel_M + Elevation, data = na.omit(lrg2))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14153.6  -1985.0    -60.7   2916.1   7010.3 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -44325.420   6863.817  -6.458 2.69e-06 ***
## Velocity_MPS     59.200      7.428   7.970 1.24e-07 ***
## Barrel_M        736.305    182.142   4.042 0.000637 ***
## Elevation       411.624    116.034   3.547 0.002020 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5075 on 20 degrees of freedom
## Multiple R-squared:  0.8804, Adjusted R-squared:  0.8624 
## F-statistic: 49.06 on 3 and 20 DF,  p-value: 2.096e-09

4 Predicting K9 Thunder Range

Here we apply open source data on K9 Thunder.

predict(fit.lrg.2,newdata = data.frame(Velocity_MPS=928,Barrel_M=155*52/1000,Elevation=60),
        interval = "conf",level = 0.99)
##        fit      lwr      upr
## 1 41244.47 34080.12 48408.83

\[P(34080.12<Range<48408.8)=0.99\]

5 Conclusion

  1. Linear regression model Range_M~Velocity_MPS+Barrel_M+Elevation is the best for fitting historical long range guns data (\(Range < 70 km\)).
  2. Applying model on real heavy artillery gun K9 Thunder we have got nice result for the range of fire.