The U.S. Department of Energy has started making a new, low-yield nuclear weapon designed to counter Russia and China as well as North Korea. The National Nuclear Security Administration says production of the weapon, known as the W76-2, has begun at its Pantex Plant in the Texas Panhandle.

Is there any relevant logic except key phrase “low-yield”? Let’s see.

Current Reliability of Trident II D5

Now we want to estimate Trident II D5 SLBM reliability, assuming 167 successful and 6 failed historical DASO trials.

library(ggplot2)
seed=12345
PD5<-rbeta(10000, shape1 = 167, shape2 = 6)
bnt <- binom.test(167,173,0.95)
print(bnt)

## 
##  Exact binomial test
## 
## data:  167 and 173
## number of successes = 167, number of trials = 173, p-value = 0.483
## alternative hypothesis: true probability of success is not equal to 0.95
## 95 percent confidence interval:
##  0.9260432 0.9871679
## sample estimates:
## probability of success 
##              0.9653179

bnt.low <- bnt$conf.int[1]
bnt.up <- bnt$conf.int[2]
quantile(PD5,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.9332258 0.9669493 0.9867906

ggplot(data=as.data.frame(PD5), aes(PD5)) + 
  geom_histogram(bins = 30,col="black",fill="green") + 
  geom_vline(xintercept = mean(PD5), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(PD5,0.025), xmax=quantile(PD5,0.975)), 
                 data=as.data.frame(PD5), col="#0094EA", size=3) +
  ggtitle(label="Probability density of Trident II D5 successful trial after DASO 28")

As we see the current Trident II D5 reliability can be estimated as 95% credible interval \(0.933\le P_{D5}\le0.987\) with mean value \(M(P_{D5})=0.967\).

Terminal kill probability

Here we apply formula from Bruice G.Blair “Strategic Command and Control. Redefining the Nuclear Threat”, page 305.

\(P_{kill}=OAR(1-0.5^{8.41Y^{2/3}/H^{2/3}CEP^2})\)

tkprob <- function(OAR,Y,H,CEP){
  #OAR - Overall reliability of attacking ICBM
  #Y - RV Yield, MT
  #CEP -  Circular error probable of attacking RV, km
  #H - Hardness of target (silo), psi (pound per sq.inch)
  return(OAR*(1-0.5^(8.41*Y^(2/3)/((H^(2/3))*((CEP*1.86)^2)))))
}
OARD5 <- 0.967
H1  <-  seq(10,by=10,length.out = 100)
test_data <-
  data.frame(
    H1,
    TK1 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.09),
    TK2 = tkprob(OAR=OARD5,Y=0.01,H1,CEP=0.09),
    TK3 = tkprob(OAR=OARD5,Y=0.1,H1,CEP=0.09),
    TK4 = tkprob(OAR=OARD5,Y=0.45,H1,CEP=0.09)
    )


ggplot(test_data, aes(x=H1)) + 
  geom_line(aes(y = TK1, colour = "0.005"))+
  geom_line(aes(y = TK2, colour = "0.01"))+
  geom_line(aes(y = TK3, colour = "0.1"))+
  geom_line(aes(y = TK4, colour = "0.45"))+
  ggtitle(label="Kill probability for CEP=0.09 km")+
  labs(x="Hardness,psi",y="Probability", colour="Yield,MT",
       caption = "based on “Strategic Command and Control. 
Redefining the Nuclear Threat” by Bruice G.Blair")

H2 = seq(1,by=5,length.out = 10)
test_data <-
  data.frame(
    H2,
    TK1 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.09),
    TK2 = tkprob(OAR=OARD5,Y=0.01,H2,CEP=0.09),
    TK3 = tkprob(OAR=OARD5,Y=0.1,H2,CEP=0.09),
    TK4 = tkprob(OAR=OARD5,Y=0.45,H2,CEP=0.09)
    )


ggplot(test_data, aes(x=H2)) + 
  geom_line(aes(y = TK1, colour = "0.005"))+
  geom_line(aes(y = TK2, colour = "0.01"))+
  geom_line(aes(y = TK3, colour = "0.1"))+
  geom_line(aes(y = TK4, colour = "0.45"))+
  ggtitle(label="Kill probability for CEP=0.09 km")+
  labs(x="Hardness,psi",y="Probability", colour="Yield,MT",
       caption = "based on “Strategic Command and Control. 
Redefining the Nuclear Threat” by Bruice G.Blair")

test_data_2 <-
  data.frame(
    H1,
    TK1 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.03),
    TK2 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.045),
    TK3 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.09),
    TK4 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.18),
    TK5 = tkprob(OAR=OARD5,Y=0.005,H1,CEP=0.3)
    
  )


ggplot(test_data_2, aes(x=H1)) + 
  geom_line(aes(y = TK1, colour = "0.03"))+
  geom_line(aes(y = TK2, colour = "0.045"))+
  geom_line(aes(y = TK3, colour = "0.09"))+
  geom_line(aes(y = TK4, colour = "0.18"))+
  geom_line(aes(y = TK5, colour = "0.35"))+
  ggtitle(label="Kill probability for Yield=5 KT")+
  labs(x="Hardness,psi",y="Probability", colour="CEP,km",
       caption = "based on “Strategic Command and Control. 
Redefining the Nuclear Threat” by Bruice G.Blair")

test_data_2 <-
  data.frame(
    H2,
    TK1 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.03),
    TK2 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.045),
    TK3 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.09),
    TK4 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.18),
    TK5 = tkprob(OAR=OARD5,Y=0.005,H2,CEP=0.3)
    
  )


ggplot(test_data_2, aes(x=H2)) + 
  geom_line(aes(y = TK1, colour = "0.03"))+
  geom_line(aes(y = TK2, colour = "0.045"))+
  geom_line(aes(y = TK3, colour = "0.09"))+
  geom_line(aes(y = TK4, colour = "0.18"))+
  geom_line(aes(y = TK5, colour = "0.35"))+
  ggtitle(label="Kill probability for Yield=5 KT")+
  labs(x="Hardness,psi",y="Probability", colour="CEP,km",
       caption = "based on “Strategic Command and Control. 
Redefining the Nuclear Threat” by Bruice G.Blair")

As we see there is only one strong reason to mount low-yield 5 KT W67-2 on board Trident II D5 - decreased Circular Error Probable (CEP) that makes it’s reentry vehicle (RV) more powerful for destroying potential targets. The official CEP for Trident II D5 is 90 meters but we should keep in mind some interesting facts about evolution of this SLBM in the near past. Precise guidance of SLBM as well as RV is the sum of advanced navigation technics including astra-inertial and GPS correction, advanced detector of height for nuclear warhead detonation (burst-height compensating super-fuze), atom clock like time generator.

Atomic clock test production

The last one (time generator) is the product of R&D of Sandia National Laboratories. The new Chip Scale Atomic Clock, which is 100 times smaller than its commercial predecessors and requires a hundred times less power: instead of 10 watts, it uses only 100 millliwatts. This chip counts the frequency of electromagnetic waves emitted by cesium atoms struck by a tiny laser beam to determine the passage of time i.e. makes navigation robust even during GPS outages. The clock’s many uses, both military and commercial, are why the work was funded by the Defense Advanced Research Projects Agency (DARPA) from 2001 until its market arrival in January 2011.

All in all Trident II D5 should be qualified as SLBM with CEP less than 90 meters (maybe 45 or even 30 m as Pershing II). That makes 5 KT reasonable for one more warhead as an option inline with 100 KT and 450 KT warheads.

For more see:

Hardened silo based DF41

DF41 test silo

#Terminal kill probability of target (silo) by two W76-2:
#M[Y}=0.005MT & SD[Y]=0.0005MT,M[CEP]=0.09km & SD[CEP]=0.005km,H=100psi,OAR=PD5
seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),1000,rnorm(5000,0.09,0.005)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target (silo) by two RV blocks

quantile(TKP2,probs = c(0.025,0.5,0.975))

##       2.5%        50%      97.5% 
## 0.08713116 0.11110724 0.14131599

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of silo (1000 psi) kill by two W76-2 CEP=90 m")

#Terminal kill probability of target (silo) by two W76-2:
#M[Y}=0.005MT & SD[Y]=0.0005MT,M[CEP]=0.045km & SD[CEP]=0.001km,H=1000psi,OAR=PD5
seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),1000,rnorm(5000,0.045,0.005)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target (silo) by two RV blocks

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.2626067 0.3737788 0.5358200

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of silo (1000 psi) kill by two W76-2 CEP=45 m")

#Terminal kill probability of target (silo) by two W76-2:
#M[Y}=0.005MT & SD[Y]=0.0005MT,M[CEP]=0.03km & SD[CEP]=0.001km,H=1000psi,OAR=PD5
seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),1000,rnorm(5000,0.03,0.001)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target (silo) by two RV blocks

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.5774798 0.6479421 0.7160133

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of silo (1000 psi) kill by two W76-2 CEP=30 m")

Mobile fixed DF41 without tactical warning

DF41 mobile ICBM https://www.princeton.edu/sgs/faculty-staff/bruce-blair/One-Hundred-Nuclear-Wars-B-Blair.pdf

“One Hundred Nuclear Wars: Stable Deterrence between the United States and Russia at Reduced Nuclear Force Levels Off Alert in the Presence of Limited Missile Defenses, Victor Esin, Matthew Mckinzie, Valery Yarynich, Pavel Zolotarev,”Science & Global Security 19, no. 3 (2011): 167-194.

The Lethal Radius (LR) is defined as the distance from the point of the nuclear explosion that the warhead will be able to destroy its target. The formula for LR (in meters) as a function of Yield (Y–in Megatons) and silo hardness (H –in overpressure pounds per square inch or psi) is given by:

Lethal Radius im meters

Nuclear blast overpressure

The Effects of Nuclear Weapons Samuel Glasstone and Philip J. Dolan, Third edition, 1977

The height of burst and energy yield of the nuclear explosion are important factors in determining the extent of damage at the surface.

#Terminal kill probability of fixed land soft target by one W67-2:
#M[Y}=0.005MT & SD[Y]=0.0005MT,M[CEP]=0.09km & SD[CEP]=0.005km,H=30psi,OAR=PD5
#L=21m,R=2.25m S=2*PI*R*L=6.28*2.25*21=297sq.m=2970000sq.sm F=H*0.43/(0.25*0.25)*S/2=30*0.07*148.5*10000=3118500kg 
LRM <- function(Y,H){
  LRM1 = 4540*(Y^(1/3))/(H^(1/3))
  LRM2 = (sqrt(1+2.79/H)+1.67/sqrt(H))^(2/3)
  return(LRM1*LRM2)
}

SSKP <- function(Y,H,CEP) {
  LRM1 = 4540*(Y^(1/3))/(H^(1/3))
  LRM2 = (sqrt(1+2.79/H)+1.67/sqrt(H))^(2/3)
  return(1-0.5^((LRM1*LRM2)^2/CEP^2))
}



seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),30,rnorm(5000,0.09,0.005)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target by two W76-2 

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.6085505 0.6985283 0.7868060

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of DF41 (30 psi, garage) kill by two W76-2 CEP=90 m")+labs(x="Pkill")

seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),30,rnorm(5000,0.045,0.001)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target by two W76-2 

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.9749194 0.9873999 0.9939345

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of DF41 (30 psi, garage) kill by two W76-2 CEP=45 m")+labs(x="Pkill")

seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),15,rnorm(5000,0.09,0.005)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target by two W76-2 

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.7693175 0.8475295 0.9120680

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of DF41 (15 psi, open air) kill by two W76-2 CEP=90 m")+labs(x="Pkill")

seed=12345
TKP <-tkprob(PD5[1:5000],rnorm(5000,0.005,0.0005),15,rnorm(5000,0.045,0.001)) 
TKP2 <- 1-(1-TKP)^2 #Terminal kill probability of target by two W76-2 

quantile(TKP2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.9924756 0.9973196 0.9991741

ggplot(data=as.data.frame(TKP2), aes(TKP2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKP2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKP2,0.025), xmax=quantile(TKP2,0.975)), 
                 data=as.data.frame(TKP2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of DF41 (15 psi, open air) kill by two W76-2 CEP=45 m")+labs(x="Pkill")

Moving mobile DF41 with tactical warning

The motion of an object moving near the surface of the earth can be described using the equations: \(x = x_o + vx_ot\) (1)

\(y = y_o + vy_ot - 0.5gt^2\) (2)

We should solve these two simultaneous equations to obtain a description of the ballistic trajectory. The horizontal \(x\) and vertical \(y\) components of initial velocity are determined from:

\(v_{xo} = v_ocos(\theta)\)

\(v_{yo} = v_osin(\theta)\)

Then we compute the time to reach the maximum height \(h\) by finding the time \(t_{rise}\) at which vertical velocity \(v_{y}\) becomes zero:

\(v_y = v_{yo} - gt\)

\(t_{rise} = v_{yo}/g\)

Maximum height is obtained by substitution of this time into equation (2).

\(h_{max} = y_o + v_{yo}t - 0.5gt^2\)

Next, the time to fall from the maximum height is computed by solving equation (2) for an object dropped from the maximum height with zero initial velocity.

\(h - 0.5gt^2=0\)

\(t_{fall} = \sqrt{2h/g}\)

The total flight time of the projectile is then:

\(t_{flight} = t_{rise} + t_{fall}\)

From this, equation (1) gives the maximum range:

\(R = v_{xo}t_{flight}\)

The projectile speed at impact \(v_f\) is determined by applying the Pythagorean Theorem:

\(v_f = \sqrt{(v_{xf}^2 + v_{yf}^2)}\)

In which:

\(v_{xf} = v_{xo}\)

\(v_{yf} = -gt_{fall}\)

The question remains what is the surface distance L between the launch and impact point in this situation. We know from the conservation of angular momentum that \(v_{\theta}=RV_0cos(\beta)/r\) and for \(H/R<<1\) that \(r\) is well approximated by \(R\). Thus we have the range \(L=tV_0 cos(\beta)\) and \(t=L/(V_0cos(\beta))\). For \(V_0=5.5 km/sec\), \(\beta=30\) and \(L=2500 km\) we got \(t=2500/(5.5*\sqrt3/2)=525 sec = 525/60=8.75 min=9min\)

#Terminal kill probability of fixed land soft target by one RV block:
#M[Y}=0.1MT & SD[Y]=0.01MT,M[CEP]=0.09km & SD[CEP]=0.01km,H=30psi,OAR=PD5
#VDF41=60km/h, TW=2 min, LD5=2500 km, VD5=5 km/s, TD5=LD5/VD5=500 sec (8.3 min),
#LDF41=VDF41*(TD5-TW)=60*(9-2)/60=7 km, SDF41=3.14*LDF41*LDF41=113 sq.km
# D=D1*Y^1/3 D=600[1kt,30psi]*0.3*100^1/3=6000m=6km[100kt,30psi]
#(D/LDF41)^2=(6/7)^2=0.735

seed=12345

PKSQ <- function(Y,H,V,TT){
  #Lethal area versus moving target area  
  (LRM(Y,H)*0.001)^2/(V*TT/60)^2
}


#With tactical warning DF41 starts moving with V=10 km/h after 2 minutes having got 9-2=7 minutes before D5 RV impact. 

TKPA <-PD5[1:3000]*PKSQ(0.005,15,rnorm(3000,10,1),rnorm(3000,7,0.1)) 
TKPA2 <- 1-(1-TKPA)^2 #Terminal kill probability of target (silo) by two RV blocks

quantile(TKPA2,probs = c(0.025,0.5,0.975))

##      2.5%       50%     97.5% 
## 0.1622721 0.2299673 0.3447092

ggplot(data=as.data.frame(TKPA2), aes(TKPA2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKPA2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKPA2,0.025), xmax=quantile(TKPA2,0.975)), 
                 data=as.data.frame(TKPA2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of moving mobile DF41 kill by two W76-2 ")+labs(x="Pkill",  caption = "Hardness 15 psi, V=10 km/h, Distance=2550 km")

#With tactical warning DF41 starts moving with V=30 km/h after 2 minutes having got 9-2=7 minutes before D5 RV impact. 

TKPA <-PD5[1:3000]*PKSQ(0.005,15,rnorm(3000,30,1),rnorm(3000,7,0.1)) 
TKPA2 <- 1-(1-TKPA)^2 #Terminal kill probability of target (silo) by two RV blocks

quantile(TKPA2,probs = c(0.025,0.5,0.975))

##       2.5%        50%      97.5% 
## 0.02358348 0.02707616 0.03140569

ggplot(data=as.data.frame(TKPA2), aes(TKPA2)) + 
  geom_histogram(bins = 20,col="black",fill="green") + 
  geom_vline(xintercept = mean(TKPA2), color = "red") + 
  geom_errorbarh(aes(y=-5, xmin=quantile(TKPA2,0.025), xmax=quantile(TKPA2,0.975)), 
                 data=as.data.frame(TKPA2), col="#0094EA", size=3) +
  ggtitle(label="Probability density of moving mobile DF41 kill by two W76-2 ")+labs(x="Pkill",  caption = "Hardness 15 psi, V=30 km/h, Distance=2550 km")

Conclusions

1.DF41 ICBM based in fixed silo (H=1000 psi) would be destroyed by two Trident D5 W76-2 5 KT and CEP=90 m each with the 95% credible interval \(P(0.087\leq P_{kill} \leq0.087)=0.95\) with the mean value \(M(P_{kill})=0.111\).

2.DF41 ICBM based in fixed silo (H=1000 psi) would be destroyed by two Trident D5 W76-2 5 KT and CEP=45 m each with the 95% credible interval \(P(0.263\leq P_{kill} \leq0.536)=0.95\) with the mean value \(M(P_{kill})=0.374\).

3.DF41 ICBM based in fixed silo (H=1000 psi) would be destroyed by two Trident D5 W76-2 5 KT and CEP=30 m each with the 95% credible interval \(P(0.577\leq P_{kill} \leq0.716)=0.95\) with the mean value \(M(P_{kill})=0.648\).

4.Ground mobile DF41 in fixed position (H=30 psi, garage) without tactical warning would be destroyed by two Trident D5 W76-2 5 KT and CEP=90 m each with the 95% credible interval \(P(0.769\leq P_{kill} \leq0.913)=0.95\) with the mean value \(M(P_{kill})=0.847\).

5.Ground mobile DF41 in fixed position (H=30 psi, garage) without tactical warning would be destroyed by two Trident D5 W76-2 5 KT and CEP=45 m each with the 95% credible interval \(P(0.975\leq P_{kill} \leq0.994)=0.95\) with the mean value \(M(P_{kill})=0.987\).

6.Ground mobile DF41 in fixed position (H=15 psi, open air ) without tactical warning would be destroyed by two Trident D5 W76-2 5 KT and CEP=90 m each with the 95% credible interval \(P(0.609\leq P_{kill} \leq0.787)=0.95\) with the mean value \(M(P_{kill})=0.699\).

7.Ground mobile DF41 in fixed position (H=15 psi, open air ) without tactical warning would be destroyed by two Trident D5 W76-2 5 KT and CEP=45 m each with the 95% credible interval \(P(0.992\leq P_{kill} \leq0.999)=0.95\) with the mean value \(M(P_{kill})=0.997\).

8.Ground mobile DF41 (H=15 psi) with tactical warning (2 minutes) would be destroyed by two Trident D5 W76-2 5 KT and CEP=90 m each from the distance 2500 km after 7 minutes run (V=10 km/h) with the 95% credible interval \(P(0.163\leq P_{kill} \leq0.348)=0.95\) with the mean value \(M(P_{kill})=0.229\).

9.Ground mobile DF41 (H=15 psi) with tactical warning (2 minutes) would be destroyed by two Trident D5 W76-2 5 KT and CEP=90 m each from the distance 2500 km after 7 minutes run (V=30 km/h) with the 95% credible interval \(P(0.024 \leq P_{kill} \leq0.031)=0.95\) with the mean value \(M(P_{kill})=0.027\).

10.The new Trident D5 W76-2 5 KT is an optional warhead for US strategic nuclear forces that can be applied only for destroying mobile ICBM (IRBM) with low (\(H\le15 psi\), open air) and medium (\(15psi\le H\le30 psi\), garage) hardness given no tactical warning. To be affective the new Trident D5 W76-2 warhead must have decreased CEP i.e. \(30m\le CEP\le 90m\) that makes impossible low altitude flight of SLBM using suppressed trajectory.

W76-2 mystery

Alexander Levakov, Senior Research Fellow, Ph.D

February, 2019