646. Maximum Length of Pair Chain

本文介绍了一个寻找最长数对链的问题,通过排序而非动态规划的方法高效解决。给定一系列递增数对,目标是找到能够形成最长连续递增序列的数对组合。

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You are given n pairs of numbers. In every pair, the first number is always smaller than the second number.

Now, we define a pair (c, d) can follow another pair (a, b) if and only if b < c. Chain of pairs can be formed in this fashion.

Given a set of pairs, find the length longest chain which can be formed. You needn't use up all the given pairs. You can select pairs in any order.

Example 1:
Input: [[1,2], [2,3], [3,4]]
Output: 2
Explanation: The longest chain is [1,2] -> [3,4]
Note:
The number of given pairs will be in the range [1, 1000].
  • 这道题目不用dp,采用排序即可完成。
class Solution {
    public:
        int findLongestChain(vector<vector<int>>& pairs) {
            sort(pairs.begin(), pairs.end(), cmp);
            int cnt = 0;
            vector<int>& pair = pairs[0];
            for (int i = 0; i < pairs.size(); i++) {
                if (i == 0 || pairs[i][0] > pair[1]) {
                    pair = pairs[i];
                    cnt++;
                }
            }
            return cnt;
        }

    private:
        static bool cmp(vector<int>& a, vector<int>&b) {
            return a[1] < b[1] || a[1] == b[1] && a[0] < b[0];
        }/*按照a[0]的大小来排序*/
};
struct SAFTVRMieParam{T} <: ParametricEoSParam{T} Mw::SingleParam{T} segment::SingleParam{T} sigma::PairParam{T} lambda_a::PairParam{T} lambda_r::PairParam{T} epsilon::PairParam{T} epsilon_assoc::AssocParam{T} bondvol::AssocParam{T} end function SAFTVRMieParam(Mw,segment,sigma,lambda_a,lambda_r,epsilon,epsilon_assoc,bondvol) return build_parametric_param(SAFTVRMieParam,Mw,segment,sigma,lambda_a,lambda_r,epsilon,epsilon_assoc,bondvol) end abstract type SAFTVRMieModel <: SAFTModel end @newmodel SAFTVRMie SAFTVRMieModel SAFTVRMieParam{T} default_references(::Type{SAFTVRMie}) = ["10.1063/1.4819786", "10.1080/00268976.2015.1029027"] default_locations(::Type{SAFTVRMie}) = ["SAFT/SAFTVRMie", "properties/molarmass.csv"] function transform_params(::Type{SAFTVRMie},params) sigma = params["sigma"] sigma.values .*= 1E-10 sigma = sigma_LorentzBerthelot(sigma) epsilon = epsilon_HudsenMcCoubreysqrt(params["epsilon"], sigma) lambda_a = lambda_LorentzBerthelot(params["lambda_a"]) lambda_r = lambda_LorentzBerthelot(params["lambda_r"]) params["sigma"] = sigma params["epsilon"] = epsilon params["lambda_a"] = lambda_a params["lambda_r"] = lambda_r return params end """ SAFTVRMieModel <: SAFTModel SAFTVRMie(components; idealmodel = BasicIdeal, userlocations = String[], ideal_userlocations = String[], reference_state = nothing, verbose = false, assoc_options = AssocOptions()) ## Input parameters - `Mw`: Single Parameter (`Float64`) - Molecular Weight `[g/mol]` - `segment`: Single Parameter (`Float64`) - Number of segments (no units) - `sigma`: Single Parameter (`Float64`) - Segment Diameter [`A°`] - `epsilon`: Single Parameter (`Float64`) - Reduced dispersion energy `[K]` - `lambda_a`: Pair Parameter (`Float64`) - Atractive range parameter (no units) - `lambda_r`: Pair Parameter (`Float64`) - Repulsive range parameter (no units) - `k`: Pair Parameter (`Float64`) (optional) - Binary Interaction Paramater (no units) - `epsilon_assoc`: Association Parameter (`Float64`) - Reduced association energy `[K]` - `bondvol`: Association Parameter (`Float64`) - Association Volume ## Model Parameters - `Mw`: Single Parameter (`Float64`) - Molecular Weight `[g/mol]` - `segment`: Single Parameter (`Float64`) - Number of segments (no units) - `sigma`: Pair Parameter (`Float64`) - Mixed segment Diameter `[m]` - `lambda_a`: Pair Parameter (`Float64`) - Atractive range parameter (no units) - `lambda_r`: Pair Parameter (`Float64`) - Repulsive range parameter (no units) - `epsilon`: Pair Parameter (`Float64`) - Mixed reduced dispersion energy`[K]` - `epsilon_assoc`: Association Parameter (`Float64`) - Reduced association energy `[K]` - `bondvol`: Association Parameter (`Float64`) - Association Volume ## Input models - `idealmodel`: Ideal Model ## Description SAFT-VR with Mie potential ## References 1. Lafitte, T., Apostolakou, A., Avendaño, C., Galindo, A., Adjiman, C. S., Müller, E. A., & Jackson, G. (2013). Accurate statistical associating fluid theory for chain molecules formed from Mie segments. The Journal of Chemical Physics, 139(15), 154504. [doi:10.1063/1.4819786](https://doi.org/10.1063/1.4819786) 2. Dufal, S., Lafitte, T., Haslam, A. J., Galindo, A., Clark, G. N. I., Vega, C., & Jackson, G. (2015). The A in SAFT: developing the contribution of association to the Helmholtz free energy within a Wertheim TPT1 treatment of generic Mie fluids. Molecular Physics, 113(9–10), 948–984. [doi:10.1080/00268976.2015.1029027](https://doi.org/10.1080/00268976.2015.1029027) """ SAFTVRMie export SAFTVRMie function recombine_impl!(model::SAFTVRMieModel) assoc_options = model.assoc_options sigma = model.params.sigma epsilon = model.params.epsilon lambda_a = model.params.lambda_a lambda_r = model.params.lambda_r epsilon_assoc = model.params.epsilon_assoc bondvol = model.params.bondvol bondvol,epsilon_assoc = assoc_mix(bondvol,epsilon_assoc,sigma,assoc_options,model.sites) #combining rules for association model.params.epsilon_assoc.values.values[:] = epsilon_assoc.values.values model.params.bondvol.values.values[:] = bondvol.values.values sigma = sigma_LorentzBerthelot!(sigma) epsilon = epsilon_HudsenMcCoubrey!(epsilon,sigma) lambda_a = lambda_LorentzBerthelot!(lambda_a) lambda_r = lambda_LorentzBerthelot!(lambda_r) return model end function x0_volume_liquid(model::SAFTVRMieModel,T,z) v_lb = lb_volume(model,z) return v_lb*1.5 end function data(model::SAFTVRMieModel, V, T, z) m̄ = dot(z,model.params.segment.values) _d = @f(d) ζi = @f(ζ0123,_d) _ζ_X,σ3x = @f(ζ_X_σ3,_d,m̄) _ρ_S = @f(ρ_S,m̄) _ζst = σ3x*_ρ_S*π/6 return (_d,_ρ_S,ζi,_ζ_X,_ζst,σ3x,m̄) end function packing_fraction(model::SAFTVRMieModel,_data::Tuple) _,_,ζi,_,_,_,m̄ = _data _,_,_,η = ζi return η end # function a_res(model::SAFTVRMieModel, V, T, z, _data = @f(data)) # return @f(a_hs,_data)+@f(a_disp,_data) + @f(a_chain,_data) + @f(a_assoc,_data) # end #fused chain and disp calculation function a_res(model::SAFTVRMieModel, V, T, z, _data = @f(data)) return @f(a_hs,_data)+@f(a_dispchain,_data) + @f(a_assoc,_data) end function a_mono(model::SAFTVRMieModel, V, T, z,_data = @f(data)) return @f(a_hs,_data)+@f(a_disp,_data) end function a_hs(model::SAFTVRMieModel, V, T, z,_data = @f(data)) _d,_,ζi,_,_,_,m̄ = _data ζ0,ζ1,ζ2,ζ3 = ζi if !iszero(ζ3) _a_hs = bmcs_hs(ζ0,ζ1,ζ2,ζ3) else _a_hs = @f(bmcs_hs_zero_v,_d) end return m̄*_a_hs/sum(z) end function ρ_S(model::SAFTVRMieModel, V, T, z, m̄ = dot(z,model.params.segment.values)) return N_A/V*m̄ end #= SAFT-VR-Mie diameter: Defined as: ``` C = (λr/(λr-λa))*(λr/λa)^(λa/(λr-λa)) u(r) = C*ϵ*(x^-λr - x^-λa) f(r) = exp(-u(r)/T) d = σ*(1-integral(f(r),0,1)) ``` we use a mixed approach, depending on T⋆ = T/ϵ: if T⋆ < 1: 5-point gauss-laguerre. we do the change of variables `y = r^-λr` else: 10-point modified gauss-legendre with cut. =# function d_vrmie(T,λa,λr,σ,ϵ) C = Cλ_mie(λa, λr) θ = ϵ*C/T ∑fi = vr_mie_d_integral(θ,λa,λr) return σ*(1 - ∑fi) end #this function is a fundamental one. function vr_mie_d_integral(θ,λa,λr) λrinv = 1/λr λaλr = λa/λr if θ > 1 function f_laguerre(x) lnx = log(x) return exp(-λrinv*lnx)*exp(θ*exp(lnx*λaλr))*λrinv/x end return Solvers.laguerre10(f_laguerre,θ,one(θ)) else j = d_vrmie_cut(θ,λa,λr) function f_legendre(x) lnx = log(x) return exp(-θ*(exp(-λr*lnx)-exp(-λa*lnx))) end return Solvers.integral10(f_legendre,j,one(j)) end end #implements the method of aasen for VRQ Mie. (https://github.com/usnistgov/teqp/issues/39) function d_vrmie_cut(θ,λa,λr) #initial point EPS = eps(typeof(θ)) K = log(-log(EPS)/θ) j0 = exp(-K/λr) # exp(-u(r)/T), d[exp(-u(r))/T)]/dr, d2[exp(-u(r))/T)]/dr2 function fdfd2f(r) r⁻¹ = 1/r lnr = log(r) rλr = exp(-lnr*λr) rλa = exp(-lnr*λa) #rλr = r^-λr #rλa = r^-λa u_r = rλr - rλa #u/C*ϵ du_ra = rλa*r⁻¹*(-λa) du_rr = rλr*r⁻¹*(-λr) du_r = (du_rr - du_ra) d2u_rr = du_rr*r⁻¹*(-λr - 1) d2u_ra = du_ra*r⁻¹*(-λa - 1) d2u_r = d2u_rr - d2u_ra f = exp(-u_r*θ) df = -θ*f*du_r d2f = df*df - θ*d2u_r*f return f, f/df, df/d2f end j = j0 for i in 1:5 fi,f1,f2 = fdfd2f(j) dd = (1 - 0.5*f1/f2) dj = f1/(1 - 0.5*f1/f2) j = j - dj fi < eps(eltype(fi)) && break end return j end function d(model::SAFTVRMieModel, V, T, z) ϵ = diagvalues(model.params.epsilon.values) σ = diagvalues(model.params.sigma.values) λa = diagvalues(model.params.lambda_a.values) λr = diagvalues(model.params.lambda_r.values) n = length(z) _d = fill(zero(V+T+first(z)+one(eltype(model))),n) for k ∈ 1:n _d[k] = d_vrmie(T,λa[k],λr[k],σ[k],ϵ[k]) end return _d end function d(model::SAFTVRMieModel, V, T, z, λa,λr,ϵ,σ) d_vrmie(T,λa,λr,σ,ϵ) end function Cλ(model::SAFTVRMieModel, V, T, z, λa, λr) return Cλ_mie(λa, λr) end Cλ_mie(λa, λr) = (λr/(λr-λa))*(λr/λa)^(λa/(λr-λa)) function ζ_X(model::SAFTVRMieModel, V, T, z,_d = @f(d)) _ζ_X,σ3x = @f(ζ_X_σ3,_d) return _ζ_X end function ζ_X_σ3(model::SAFTVRMieModel, V, T, z,_d = @f(d),m̄ = dot(z,model.params.segment.values)) m = model.params.segment.values m̄ = dot(z, m) m̄inv = 1/m̄ σ = model.params.sigma.values ρS = N_A/V*m̄ comps = 1:length(z) _ζ_X = zero(V+T+first(z)+one(eltype(model))) kρS = ρS* π/6/8 σ3_x = _ζ_X for i ∈ comps x_Si = z[i]*m[i]*m̄inv σ3_x += x_Si*x_Si*(σ[i,i]^3) di =_d[i] r1 = kρS*x_Si*x_Si*(2*di)^3 _ζ_X += r1 for j ∈ 1:(i-1) x_Sj = z[j]*m[j]*m̄inv σ3_x += 2*x_Si*x_Sj*(σ[i,j]^3) dij = (di + _d[j]) r1 = kρS*x_Si*x_Sj*dij^3 _ζ_X += 2*r1 end end return _ζ_X,σ3_x end function aS_1(model::SAFTVRMieModel, V, T, z, λ,ζ_X_= @f(ζ_X)) ζeff_ = @f(ζeff,λ,ζ_X_) return -1/(λ-3)*(1-ζeff_/2)/(1-ζeff_)^3 end function ζeff(model::SAFTVRMieModel, V, T, z, λ,ζ_X_= @f(ζ_X)) A = SAFTγMieconsts.A λ⁻¹ = one(λ)/λ Aλ⁻¹ = A * SA[one(λ); λ⁻¹; λ⁻¹*λ⁻¹; λ⁻¹*λ⁻¹*λ⁻¹] return dot(Aλ⁻¹,SA[ζ_X_; ζ_X_^2; ζ_X_^3; ζ_X_^4]) end function B(model::SAFTVRMieModel, V, T, z, λ, x_0,ζ_X_ = @f(ζ_X)) x_0_3λ = x_0^(3-λ) ζ_X_m13 = (1-ζ_X_)^3 I = (1-x_0_3λ)/(λ-3) J = (1-(λ-3)*x_0^(4-λ)+(λ-4)*x_0_3λ)/((λ-3)*(λ-4)) return I*(1-ζ_X_/2)/ζ_X_m13-9*J*ζ_X_*(ζ_X_+1)/(2*ζ_X_m13) end function KHS(model::SAFTVRMieModel, V, T, z,ζ_X_ = @f(ζ_X),ρS=@f(ρ_S)) return (1-ζ_X_)^4/(1+4ζ_X_+4ζ_X_^2-4ζ_X_^3+ζ_X_^4) end function f123456(model::SAFTVRMieModel, V, T, z, α) ϕ = SAFTVRMieconsts.ϕ _0 = zero(α) fa = (_0,_0,_0,_0,_0,_0) fb = (_0,_0,_0,_0,_0,_0) @inbounds for i ∈ 1:4 ϕi = ϕ[i]::NTuple{6,Float64} ii = i-1 αi = α^ii fa = fa .+ ϕi .*αi end @inbounds for i ∈ 5:7 ϕi = ϕ[i]::NTuple{6,Float64} ii = i-4 αi = α^ii fb = fb .+ ϕi .*αi end return fa ./ (one(_0) .+ fb) #return sum(ϕ[i+1][m]*α^i for i ∈ 0:3)/(1+∑(ϕ[i+1][m]*α^(i-3) for i ∈ 4:6)) end function ζst(model::SAFTVRMieModel, V, T, z,_σ = model.params.sigma.values) m = model.params.segment.values m̄ = dot(z, m) m̄inv = 1/m̄ ρS = N_A/V*m̄ comps = @comps _ζst = zero(V+T+first(z)+one(eltype(model))) for i ∈ comps x_Si = z[i]*m[i]*m̄inv _ζst += x_Si*x_Si*(_σ[i,i]^3) for j ∈ 1:i-1 x_Sj = z[j]*m[j]*m̄inv _ζst += 2*x_Si*x_Sj*(_σ[i,j]^3) end end #return π/6*@f(ρ_S)*∑(@f(x_S,i)*@f(x_S,j)*(@f(d,i)+@f(d,j))^3/8 for i ∈ comps for j ∈ comps) return _ζst*ρS* π/6 end function g_HS(model::SAFTVRMieModel, V, T, z, x_0ij,ζ_X_ = @f(ζ_X)) ζX3 = (1-ζ_X_)^3 #evalpoly(ζ_X_,(0,42,-39,9,-2)) = (42ζ_X_-39ζ_X_^2+9ζ_X_^3-2ζ_X_^4) k_0 = -log(1-ζ_X_)+evalpoly(ζ_X_,(0,42,-39,9,-2))/(6*ζX3) #evalpoly(ζ_X_,(0,-12,6,0,1)) = (ζ_X_^4+6*ζ_X_^2-12*ζ_X_) k_1 = evalpoly(ζ_X_,(0,-12,6,0,1))/(2*ζX3) k_2 = -3*ζ_X_^2/(8*(1-ζ_X_)^2) #(-ζ_X_^4+3*ζ_X_^2+3*ζ_X_) = evalpoly(ζ_X_,(0,3,3,0,-1)) k_3 = evalpoly(ζ_X_,(0,3,3,0,-1))/(6*ζX3) return exp(evalpoly(x_0ij,(k_0,k_1,k_2,k_3))) end function ζeff_fdf(model::SAFTVRMieModel, V, T, z, λ,ζ_X_,ρ_S_) A = SAFTγMieconsts.A λ⁻¹ = one(λ)/λ Aλ⁻¹ = A * SA[one(λ); λ⁻¹; λ⁻¹*λ⁻¹; λ⁻¹*λ⁻¹*λ⁻¹] _f = dot(Aλ⁻¹,SA[ζ_X_; ζ_X_^2; ζ_X_^3; ζ_X_^4]) _df = dot(Aλ⁻¹,SA[1; 2ζ_X_; 3ζ_X_^2; 4ζ_X_^3]) * ζ_X_/ρ_S_ return _f,_df end function ζeff_f_ρdf(model::SAFTVRMieModel, V, T, z, λ,ζ_X_) A = SAFTγMieconsts.A λ⁻¹ = one(λ)/λ Aλ⁻¹ = A * SA[one(λ); λ⁻¹; λ⁻¹*λ⁻¹; λ⁻¹*λ⁻¹*λ⁻¹] _f = dot(Aλ⁻¹,SA[ζ_X_; ζ_X_^2; ζ_X_^3; ζ_X_^4]) _ρdf = dot(Aλ⁻¹,SA[1; 2ζ_X_; 3ζ_X_^2; 4ζ_X_^3]) * ζ_X_ return _f,_ρdf end function aS_1_fdf(model::SAFTVRMieModel, V, T, z, λ, ζ_X_= @f(ζ_X),ρ_S_ = 0.0) ζeff_,∂ζeff_ρ_S = @f(ζeff_f_ρdf,λ,ζ_X_) ζeff3 = (1-ζeff_)^3 ζeffm1 = (1-ζeff_*0.5) ζf = ζeffm1/ζeff3 λf = -1/(λ-3) _f = λf * ζf _df = λf * (ζf + ∂ζeff_ρ_S*((3*ζeffm1*(1-ζeff_)^2 - 0.5*ζeff3)/ζeff3^2)) return _f,_df end function B_fdf(model::SAFTVRMieModel, V, T, z, λ, x_0,ζ_X_= @f(ζ_X),ρ_S_ = @f(ρ_S)) x_0_λ = x_0^(3-λ) I = (1-x_0_λ)/(λ-3) J = (1-(λ-3)*x_0^(4-λ)+(λ-4)*x_0_λ)/((λ-3)*(λ-4)) ζX2 = (1-ζ_X_)^2 ζX3 = (1-ζ_X_)^3 ζX6 = ζX3*ζX3 _f = I*(1-ζ_X_/2)/ζX3-9*J*ζ_X_*(ζ_X_+1)/(2*ζX3) _df = (((1-ζ_X_/2)*I/ζX3-9*ζ_X_*(1+ζ_X_)*J/(2*ζX3)) + ζ_X_*( (3*(1-ζ_X_/2)*ζX2 - 0.5*ζX3)*I/ζX6 - 9*J*((1+2*ζ_X_)*ζX3 + ζ_X_*(1+ζ_X_)*3*ζX2)/(2*ζX6))) return _f,_df end function KHS_fdf(model::SAFTVRMieModel, V, T, z,ζ_X_,ρ_S_ = @f(ρ_S)) _f,_ρdf = KHS_f_ρdf(model,V,T,z,ζ_X_) _df = _ρdf/ρ_S_ return _f,_ρdf/ρ_S_ end function KHS_f_ρdf(model::SAFTVRMieModel, V, T, z,ζ_X_) ζX4 = (1-ζ_X_)^4 denom1 = evalpoly(ζ_X_,(1,4,4,-4,1)) ∂denom1 = evalpoly(ζ_X_,(4,8,-12,4)) _f = ζX4/denom1 _df = -ζ_X_*((4*(1-ζ_X_)^3*denom1 + ζX4*∂denom1)/denom1^2) return _f,_df end function ∂a_2╱∂ρ_S(model::SAFTVRMieModel,V, T, z, i) λr = diagvalues(model.params.lambda_r.values) λa = diagvalues(model.params.lambda_a.values) x_0ij = @f(x_0,i,i) ζ_X_ = @f(ζ_X) ρ_S_ = @f(ρ_S) ∂KHS╱∂ρ_S = -ζ_X_/ρ_S_ * ( (4*(1-ζ_X_)^3*(1+4*ζ_X_+4*ζ_X_^2-4*ζ_X_^3+ζ_X_^4) + (1-ζ_X_)^4*(4+8*ζ_X_-12*ζ_X_^2+4*ζ_X_^3))/(1+4*ζ_X_+4*ζ_X_^2-4*ζ_X_^3+ζ_X_^4)^2 ) return 0.5*@f(C,i,i)^2 * (@f(ρ_S)*∂KHS╱∂ρ_S*(x_0ij^(2*λa[i])*(@f(aS_1,2*λa[i])+@f(B,2*λa[i],x_0ij)) - 2*x_0ij^(λa[i]+λr[i])*(@f(aS_1,λa[i]+λr[i])+@f(B,λa[i]+λr[i],x_0ij)) + x_0ij^(2*λr[i])*(@f(aS_1,2*λr[i])+@f(B,2*λr[i],x_0ij))) + @f(KHS)*(x_0ij^(2*λa[i])*(@f(∂aS_1╱∂ρ_S,2*λa[i])+@f(∂B╱∂ρ_S,2*λa[i],x_0ij)) - 2*x_0ij^(λa[i]+λr[i])*(@f(∂aS_1╱∂ρ_S,λa[i]+λr[i])+@f(∂B╱∂ρ_S,λa[i]+λr[i],x_0ij)) + x_0ij^(2*λr[i])*(@f(∂aS_1╱∂ρ_S,2*λr[i])+@f(∂B╱∂ρ_S,2*λr[i],x_0ij)))) end function I(model::SAFTVRMieModel, V, T, z, i, j, _data = @f(data)) ϵ = model.params.epsilon.values[i,j] Tr = T/ϵ _d,ρS,ζi,_ζ_X,_ζst,σ3_x = _data c = SAFTVRMieconsts.c res = zero(_ζst) ρr = ρS*σ3_x ρrn = one(ρr) @inbounds for n ∈ 0:10 res_m = zero(res) Trm = one(Tr) for m ∈ 0:(10-n) res_m += c[n+1,m+1]*Trm Trm = Trm*Tr end res += res_m*ρrn ρrn = ρrn*ρr end return res end function Δ(model::SAFTVRMieModel, V, T, z, i, j, a, b,_data = @f(data)) ϵ = model.params.epsilon.values K = model.params.bondvol.values Kijab = K[i,j][a,b] if iszero(Kijab) return zero(@f(Base.promote_eltype)) end Tr = T/ϵ[i,j] _I = @f(I,i,j,_data) ϵ_assoc = model.params.epsilon_assoc.values F = expm1(ϵ_assoc[i,j][a,b]/T) return F*Kijab*_I end #optimized functions for maximum speed on default SAFTVRMie function a_dispchain(model::SAFTVRMieModel, V, T, z,_data = @f(data)) _d,ρS,ζi,ζₓ,_ζst,_,m̄ = _data comps = @comps ∑z = ∑(z) m = model.params.segment.values _ϵ = model.params.epsilon.values _λr = model.params.lambda_r.values _λa = model.params.lambda_a.values _σ = model.params.sigma.values m̄inv = 1/m̄ a₁ = zero(V+T+first(z)+one(eltype(model))) a₂ = a₁ a₃ = a₁ achain = a₁ _ζst5 = _ζst^5 _ζst8 = _ζst^8 _KHS,ρS_∂KHS = @f(KHS_f_ρdf,ζₓ) for i ∈ comps j = i mi = m[i] x_Si = z[i]*mi*m̄inv x_Sj = x_Si ϵ = _ϵ[i,j] λa = _λa[i,j] λr = _λr[i,j] σ = _σ[i,j] _C = @f(Cλ,λa,λr) dij = _d[i] x_0ij = σ/dij dij3 = dij^3 τ = ϵ/T #precalculate exponentials of x_0ij x_0ij_λa = x_0ij^λa x_0ij_λr = x_0ij^λr x_0ij_2λa = x_0ij^(2*λa) x_0ij_2λr = x_0ij^(2*λr) x_0ij_λaλr = x_0ij^(λa + λr) #calculations for a1 - diagonal aS₁_a,∂aS₁∂ρS_a = @f(aS_1_fdf,λa,ζₓ,ρS) aS₁_r,∂aS₁∂ρS_r = @f(aS_1_fdf,λr,ζₓ,ρS) B_a,∂B∂ρS_a = @f(B_fdf,λa,x_0ij,ζₓ,ρS) B_r,∂B∂ρS_r = @f(B_fdf,λr,x_0ij,ζₓ,ρS) a1_ij = (2*π*ϵ*dij3)*_C*ρS* (x_0ij_λa*(aS₁_a+B_a) - x_0ij_λr*(aS₁_r+B_r)) #calculations for a2 - diagonal aS₁_2a,∂aS₁∂ρS_2a = @f(aS_1_fdf,2*λa,ζₓ,ρS) aS₁_2r,∂aS₁∂ρS_2r = @f(aS_1_fdf,2*λr,ζₓ,ρS) aS₁_ar,∂aS₁∂ρS_ar = @f(aS_1_fdf,λa+λr,ζₓ,ρS) B_2a,∂B∂ρS_2a = @f(B_fdf,2*λa,x_0ij,ζₓ,ρS) B_2r,∂B∂ρS_2r = @f(B_fdf,2*λr,x_0ij,ζₓ,ρS) B_ar,∂B∂ρS_ar = @f(B_fdf,λr+λa,x_0ij,ζₓ,ρS) α = _C*(1/(λa-3)-1/(λr-3)) f1,f2,f3,f4,f5,f6 = @f(f123456,α) _χ = f1*_ζst + f2*_ζst5 + f3*_ζst8 a2_ij = π*_KHS*(1+_χ)*ρS*ϵ^2*dij3*_C^2 * (x_0ij_2λa*(aS₁_2a+B_2a) - 2*x_0ij_λaλr*(aS₁_ar+B_ar) + x_0ij_2λr*(aS₁_2r+B_2r) ) #calculations for a3 - diagonal a3_ij = -ϵ^3*f4*_ζst*exp(_ζst*(f5 + f6*_ζst)) #adding - diagonal a₁ += a1_ij*x_Si*x_Sj a₂ += a2_ij*x_Si*x_Sj a₃ += a3_ij*x_Si*x_Sj g_HSi = @f(g_HS,x_0ij,ζₓ) ∂a_1∂ρ_S = _C*(x_0ij_λa*(∂aS₁∂ρS_a+∂B∂ρS_a) - x_0ij_λr*(∂aS₁∂ρS_r+∂B∂ρS_r) ) #calculus for g1 g_1_ = 3*∂a_1∂ρ_S - _C*(λa*x_0ij_λa*(aS₁_a + B_a) - λr*x_0ij_λr*(aS₁_r + B_r)) θ = expm1(τ) γc = 10 * (-tanh(10*(0.57 - α)) + 1) * _ζst*θ*exp(_ζst*(-6.7 - 8*_ζst)) ∂a_2∂ρ_S = 0.5*_C^2 * (ρS_∂KHS*(x_0ij_2λa*(aS₁_2a+B_2a) - 2*x_0ij_λaλr*(aS₁_ar+B_ar) + x_0ij_2λr*(aS₁_2r+B_2r) ) + _KHS*(x_0ij_2λa*(∂aS₁∂ρS_2a + ∂B∂ρS_2a) - 2*x_0ij_λaλr*(∂aS₁∂ρS_ar + ∂B∂ρS_ar) + x_0ij_2λr*(∂aS₁∂ρS_2r + ∂B∂ρS_2r) ) ) gMCA2 = 3*∂a_2∂ρ_S-_KHS*_C^2 * (λr*x_0ij_2λr*(aS₁_2r+B_2r) - (λa+λr)*x_0ij_λaλr*(aS₁_ar+B_ar) + λa*x_0ij_2λa*(aS₁_2a+B_2a) ) g_2_ = (1 + γc)*gMCA2 g_Mie_ = g_HSi*exp(τ*g_1_/g_HSi+τ^2*g_2_/g_HSi) achain -= z[i]*(log(g_Mie_)*(mi - 1)) for j ∈ 1:i-1 x_Sj = z[j]*m[j]*m̄inv ϵ = _ϵ[i,j] λa = _λa[i,j] λr = _λr[i,j] σ = _σ[i,j] _C = @f(Cλ,λa,λr) dij = 0.5*(_d[i]+_d[j]) x_0ij = σ/dij dij3 = dij^3 #calculations for a1 a1_ij = (2*π*ϵ*dij3)*_C*ρS* (x_0ij^λa*(@f(aS_1,λa,ζₓ)+@f(B,λa,x_0ij,ζₓ)) - x_0ij^λr*(@f(aS_1,λr,ζₓ)+@f(B,λr,x_0ij,ζₓ))) #calculations for a2 α = _C*(1/(λa-3)-1/(λr-3)) f1,f2,f3,f4,f5,f6 = @f(f123456,α) _χ = f1*_ζst+f2*_ζst5+f3*_ζst8 a2_ij = π*_KHS*(1+_χ)*ρS*ϵ^2*dij3*_C^2 * (x_0ij^(2*λa)*(@f(aS_1,2*λa,ζₓ)+@f(B,2*λa,x_0ij,ζₓ)) - 2*x_0ij^(λa+λr)*(@f(aS_1,λa+λr,ζₓ)+@f(B,λa+λr,x_0ij,ζₓ)) + x_0ij^(2*λr)*(@f(aS_1,2λr,ζₓ)+@f(B,2*λr,x_0ij,ζₓ))) #calculations for a3 a3_ij = -ϵ^3*f4*_ζst * exp(_ζst*(f5+f6*_ζst)) #adding a₁ += 2*a1_ij*x_Si*x_Sj a₂ += 2*a2_ij*x_Si*x_Sj a₃ += 2*a3_ij*x_Si*x_Sj end end a₁ = a₁*m̄/T/∑z a₂ = a₂*m̄/(T*T)/∑z a₃ = a₃*m̄/(T*T*T)/∑z adisp = a₁ + a₂ + a₃ return adisp + achain/∑z end function a_disp(model::SAFTVRMieModel, V, T, z,_data = @f(data)) _d,ρS,ζi,_ζ_X,_ζst,_,m̄ = _data comps = 1:length(z) #this is a magic trick. we normally (should) expect length(z) = length(model), #but on GC models, @comps != @groups #if we pass Xgc instead of z, the equation is exactly the same. #we need to add the divide the result by sum(z) later. m = model.params.segment.values _ϵ = model.params.epsilon.values _λr = model.params.lambda_r.values _λa = model.params.lambda_a.values _σ = model.params.sigma.values m̄inv = 1/m̄ a₁ = zero(V+T+first(z)+one(eltype(model))) a₂ = a₁ a₃ = a₁ _ζst5 = _ζst^5 _ζst8 = _ζst^8 _KHS = @f(KHS,_ζ_X,ρS) for i ∈ comps j = i x_Si = z[i]*m[i]*m̄inv x_Sj = x_Si ϵ = _ϵ[i,j] λa = _λa[i,i] λr = _λr[i,i] σ = _σ[i,i] _C = @f(Cλ,λa,λr) dij = _d[i] dij3 = dij^3 x_0ij = σ/dij #calculations for a1 - diagonal aS_1_a = @f(aS_1,λa,_ζ_X) aS_1_r = @f(aS_1,λr,_ζ_X) B_a = @f(B,λa,x_0ij,_ζ_X) B_r = @f(B,λr,x_0ij,_ζ_X) a1_ij = (2*π*ϵ*dij3)*_C*ρS* (x_0ij^λa*(aS_1_a+B_a) - x_0ij^λr*(aS_1_r+B_r)) #calculations for a2 - diagonal aS_1_2a = @f(aS_1,2*λa,_ζ_X) aS_1_2r = @f(aS_1,2*λr,_ζ_X) aS_1_ar = @f(aS_1,λa+λr,_ζ_X) B_2a = @f(B,2*λa,x_0ij,_ζ_X) B_2r = @f(B,2*λr,x_0ij,_ζ_X) B_ar = @f(B,λr+λa,x_0ij,_ζ_X) α = _C*(1/(λa-3)-1/(λr-3)) f1,f2,f3,f4,f5,f6 = @f(f123456,α) _χ = f1*_ζst+f2*_ζst5+f3*_ζst8 a2_ij = π*_KHS*(1+_χ)*ρS*ϵ^2*dij3*_C^2 * (x_0ij^(2*λa)*(aS_1_2a+B_2a) - 2*x_0ij^(λa+λr)*(aS_1_ar+B_ar) + x_0ij^(2*λr)*(aS_1_2r+B_2r)) #calculations for a3 - diagonal a3_ij = -ϵ^3*f4*_ζst * exp(f5*_ζst+f6*_ζst^2) #adding - diagonal a₁ += a1_ij*x_Si*x_Si a₂ += a2_ij*x_Si*x_Si a₃ += a3_ij*x_Si*x_Si for j ∈ 1:(i-1) x_Sj = z[j]*m[j]*m̄inv ϵ = _ϵ[i,j] λa = _λa[i,j] λr = _λr[i,j] σ = _σ[i,j] _C = @f(Cλ,λa,λr) dij = 0.5*(_d[i]+_d[j]) x_0ij = σ/dij dij3 = dij^3 x_0ij = σ/dij #calculations for a1 a1_ij = (2*π*ϵ*dij3)*_C*ρS* (x_0ij^λa*(@f(aS_1,λa,_ζ_X)+@f(B,λa,x_0ij,_ζ_X)) - x_0ij^λr*(@f(aS_1,λr,_ζ_X)+@f(B,λr,x_0ij,_ζ_X))) #calculations for a2 α = _C*(1/(λa-3)-1/(λr-3)) f1,f2,f3,f4,f5,f6 = @f(f123456,α) _χ = f1*_ζst+f2*_ζst5+f3*_ζst8 a2_ij = π*_KHS*(1+_χ)*ρS*ϵ^2*dij3*_C^2 * (x_0ij^(2*λa)*(@f(aS_1,2*λa,_ζ_X)+@f(B,2*λa,x_0ij,_ζ_X)) - 2*x_0ij^(λa+λr)*(@f(aS_1,λa+λr,_ζ_X)+@f(B,λa+λr,x_0ij,_ζ_X)) + x_0ij^(2*λr)*(@f(aS_1,2λr,_ζ_X)+@f(B,2*λr,x_0ij,_ζ_X))) #calculations for a3 a3_ij = -ϵ^3*f4*_ζst * exp(f5*_ζst+f6*_ζst^2) #adding a₁ += 2*a1_ij*x_Si*x_Sj a₂ += 2*a2_ij*x_Si*x_Sj a₃ += 2*a3_ij*x_Si*x_Sj end end a₁ = a₁*m̄/T #/sum(z) a₂ = a₂*m̄/(T*T) #/sum(z) a₃ = a₃*m̄/(T*T*T) #/sum(z) #@show (a₁,a₂,a₃) adisp = a₁ + a₂ + a₃ return adisp end function a_chain(model::SAFTVRMieModel, V, T, z,_data = @f(data)) _d,ρS,ζi,_ζ_X,_ζst,_,m̄ = _data l = length(z) comps = 1:l ∑z = ∑(z) m = model.params.segment.values _ϵ = model.params.epsilon.values _λr = model.params.lambda_r.values _λa = model.params.lambda_a.values _σ = model.params.sigma.values m̄inv = 1/m̄ a₁ = zero(V+T+first(z)+one(eltype(model))) a₂ = a₁ a₃ = a₁ achain = a₁ _ζst5 = _ζst^5 _ζst8 = _ζst^8 _KHS,ρS_∂KHS = @f(KHS_f_ρdf,_ζ_X) for i ∈ comps x_Si = z[i]*m[i]*m̄inv x_Sj = x_Si ϵ = _ϵ[i,i] λa = _λa[i,i] λr = _λr[i,i] σ = _σ[i,i] _C = @f(Cλ,λa,λr) dij = _d[i] x_0ij = σ/dij dij3 = dij^3 x_0ij = σ/dij #calculations for a1 - diagonal aS_1_a,∂aS_1∂ρS_a = @f(aS_1_fdf,λa,_ζ_X,ρS) aS_1_r,∂aS_1∂ρS_r = @f(aS_1_fdf,λr,_ζ_X,ρS) B_a,∂B∂ρS_a = @f(B_fdf,λa,x_0ij,_ζ_X,ρS) B_r,∂B∂ρS_r = @f(B_fdf,λr,x_0ij,_ζ_X,ρS) a1_ij = (2*π*ϵ*dij3)*_C*ρS* (x_0ij^λa*(aS_1_a+B_a) - x_0ij^λr*(aS_1_r+B_r)) #calculations for a2 - diagonal aS_1_2a,∂aS_1∂ρS_2a = @f(aS_1_fdf,2*λa,_ζ_X,ρS) aS_1_2r,∂aS_1∂ρS_2r = @f(aS_1_fdf,2*λr,_ζ_X,ρS) aS_1_ar,∂aS_1∂ρS_ar = @f(aS_1_fdf,λa+λr,_ζ_X,ρS) B_2a,∂B∂ρS_2a = @f(B_fdf,2*λa,x_0ij,_ζ_X,ρS) B_2r,∂B∂ρS_2r = @f(B_fdf,2*λr,x_0ij,_ζ_X,ρS) B_ar,∂B∂ρS_ar = @f(B_fdf,λr+λa,x_0ij,_ζ_X,ρS) α = _C*(1/(λa-3)-1/(λr-3)) f1,f2,f3,f4,f5,f6 = @f(f123456,α) _χ = f1*_ζst+f2*_ζst5+f3*_ζst8 a2_ij = π*_KHS*(1+_χ)*ρS*ϵ^2*dij3*_C^2 * (x_0ij^(2*λa)*(aS_1_2a+B_2a) - 2*x_0ij^(λa+λr)*(aS_1_ar+B_ar) + x_0ij^(2*λr)*(aS_1_2r+B_2r)) #calculations for a3 - diagonal a3_ij = -ϵ^3*f4*_ζst * exp(f5*_ζst+f6*_ζst^2) #adding - diagonal a₁ += a1_ij*x_Si*x_Sj a₂ += a2_ij*x_Si*x_Sj a₃ += a3_ij*x_Si*x_Sj g_HSi = @f(g_HS,x_0ij,_ζ_X) #@show (g_HSi,i) ∂a_1∂ρ_S = _C*(x_0ij^λa*(∂aS_1∂ρS_a+∂B∂ρS_a) - x_0ij^λr*(∂aS_1∂ρS_r+∂B∂ρS_r)) #@show (∂a_1∂ρ_S,1) g_1_ = 3*∂a_1∂ρ_S-_C*(λa*x_0ij^λa*(aS_1_a+B_a)-λr*x_0ij^λr*(aS_1_r+B_r)) #@show (g_1_,i) θ = exp(ϵ/T)-1 γc = 10 * (-tanh(10*(0.57-α))+1) * _ζst*θ*exp(-6.7*_ζst-8*_ζst^2) ∂a_2∂ρ_S = 0.5*_C^2 * (ρS_∂KHS*(x_0ij^(2*λa)*(aS_1_2a+B_2a) - 2*x_0ij^(λa+λr)*(aS_1_ar+B_ar) + x_0ij^(2*λr)*(aS_1_2r+B_2r)) + _KHS*(x_0ij^(2*λa)*(∂aS_1∂ρS_2a+∂B∂ρS_2a) - 2*x_0ij^(λa+λr)*(∂aS_1∂ρS_ar+∂B∂ρS_ar) + x_0ij^(2*λr)*(∂aS_1∂ρS_2r+∂B∂ρS_2r))) gMCA2 = 3*∂a_2∂ρ_S-_KHS*_C^2 * (λr*x_0ij^(2*λr)*(aS_1_2r+B_2r)- (λa+λr)*x_0ij^(λa+λr)*(aS_1_ar+B_ar)+ λa*x_0ij^(2*λa)*(aS_1_2a+B_2a)) g_2_ = (1+γc)*gMCA2 #@show (g_2_,i) g_Mie_ = g_HSi*exp(ϵ/T*g_1_/g_HSi+(ϵ/T)^2*g_2_/g_HSi) #@show (g_Mie_,i) achain += z[i]*(log(g_Mie_)*(m[i]-1)) end return -achain/∑z end const SAFTVRMieconsts = ( A = SA[0.81096 1.7888 -37.578 92.284; 1.02050 -19.341 151.26 -463.50; -1.90570 22.845 -228.14 973.92; 1.08850 -6.1962 106.98 -677.64], ϕ = ((7.5365557, -359.440, 1550.9, -1.199320, -1911.2800, 9236.9), (-37.604630, 1825.60, -5070.1, 9.063632, 21390.175, -129430.0), (71.745953, -3168.00, 6534.6, -17.94820, -51320.700, 357230.0), (-46.835520, 1884.20, -3288.7, 11.34027, 37064.540, -315530.0), (-2.4679820,- 0.82376, -2.7171, 20.52142, 1103.7420, 1390.2), (-0.5027200, -3.19350, 2.0883, -56.63770, -3264.6100, -4518.2), (8.0956883, 3.70900, 0.0000, 40.53683, 2556.1810, 4241.6)), c = [0.0756425183020431 -0.128667137050961 0.128350632316055 -0.0725321780970292 0.0257782547511452 -0.00601170055221687 0.000933363147191978 -9.55607377143667e-05 6.19576039900837e-06 -2.30466608213628e-07 3.74605718435540e-09 0.134228218276565 -0.182682168504886 0.0771662412959262 -0.000717458641164565 -0.00872427344283170 0.00297971836051287 -0.000484863997651451 4.35262491516424e-05 -2.07789181640066e-06 4.13749349344802e-08 0 -0.565116428942893 1.00930692226792 -0.660166945915607 0.214492212294301 -0.0388462990166792 0.00406016982985030 -0.000239515566373142 7.25488368831468e-06 -8.58904640281928e-08 0 0 -0.387336382687019 -0.211614570109503 0.450442894490509 -0.176931752538907 0.0317171522104923 -0.00291368915845693 0.000130193710011706 -2.14505500786531e-06 0 0 0 2.13713180911797 -2.02798460133021 0.336709255682693 0.00118106507393722 -0.00600058423301506 0.000626343952584415 -2.03636395699819e-05 0 0 0 0 -0.300527494795524 2.89920714512243 -0.567134839686498 0.0518085125423494 -0.00239326776760414 4.15107362643844e-05 0 0 0 0 0 -6.21028065719194 -1.92883360342573 0.284109761066570 -0.0157606767372364 0.000368599073256615 0 0 0 0 0 0 11.6083532818029 0.742215544511197 -0.0823976531246117 0.00186167650098254 0 0 0 0 0 0 0 -10.2632535542427 -0.125035689035085 0.0114299144831867 0 0 0 0 0 0 0 0 4.65297446837297 -0.00192518067137033 0 0 0 0 0 0 0 0 0 -0.867296219639940 0 0 0 0 0 0 0 0 0 0], ) ######## #= Optimizations for single component SAFTVRMie =# ####### function d(model::SAFTVRMie, V, T, z::SingleComp) ϵ = model.params.epsilon.values[1,1] σ = model.params.sigma.values[1,1] λa = model.params.lambda_a.values[1,1] λr = model.params.lambda_r.values[1,1] return SA[d_vrmie(T,λa[1],λr[1],σ[1],ϵ[1])] end 注释并解释代码
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