point 2 + 3: LOCA entry reach + scram fat-entry + steam-dump heatup
Morning-review items 2 and 3.
Point 2 (scram X_entry expansion):
- reach_loca_operation.jl: LQR reach under Q_sg widened to
[0, 1.5*P_0] (steam-line break envelope) for 3 s horizon.
Longer horizons cause numerical blowup in the box-hull
reach propagator due to slow precursor modes amplifying
under large disturbance — documented in the script.
- reach_scram_pj_fat.jl: computes bounding-box union of
hot-standby + heatup-tight + operation + LOCA reach
envelopes, clamps obvious numerical outliers on precursors
and temperatures, builds a fat X_entry(scram), runs scram
PJ reach. Result pending (TMJets compiling).
Point 3 (heatup steam-dump Q_sg):
- configs/heatup/with_steam_dump.toml: Q_sg ∈ [0, 0.05·P_0]
as bounded parameter.
- reach_heatup_pj_sd.jl: 12-state RHS with x[10]=Q_sg (dx=0,
augmented bounded param) and x[11]=t. Running in
background.
Tight-entry heatup via the new TOML-config reach_heatup_pj.jl
reproduces the previous all-6-halfspaces-discharged result
(300s horizon, T_c envelope [281.05, 291.0]). Refactor
preserves semantics.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
Dane Sabo
parent
2bbb1871cc
commit
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90
code/scripts/reach/reach_loca_operation.jl
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90
code/scripts/reach/reach_loca_operation.jl
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#!/usr/bin/env julia
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#
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# reach_loca_operation.jl — operation mode under a secondary-side LOCA.
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#
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# Simulates the fast transient just BEFORE scram fires: Q_sg jumps from
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# nominal toward its worst-case runaway value (steam-line break = Q_sg
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# spikes to ~1.5·P_0 as the primary cools rapidly). LQR can't handle
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# this — scram is expected to fire. We care about WHERE the plant state
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# ends up at the moment scram triggers, because that state becomes part
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# of X_entry(scram).
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#
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# Uses the full-state linearized closed-loop under LQR, reach propagated
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# with the existing hand-rolled reach_linear machinery (fast, sound for
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# the linear model over short horizons).
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using Pkg
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Pkg.activate(joinpath(@__DIR__, "..", ".."))
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using Printf
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using LinearAlgebra
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using MatrixEquations
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using MAT
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include(joinpath(@__DIR__, "..", "..", "src", "pke_params.jl"))
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include(joinpath(@__DIR__, "..", "..", "src", "pke_th_rhs.jl"))
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include(joinpath(@__DIR__, "..", "..", "src", "pke_linearize.jl"))
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include(joinpath(@__DIR__, "..", "..", "src", "reach_linear.jl"))
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plant = pke_params()
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x_op = pke_initial_conditions(plant)
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A, B, B_w, _, _, _ = pke_linearize(plant)
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Q_lqr = Diagonal([1.0, 1e-3, 1e-3, 1e-3, 1e-3, 1e-3, 1e-3, 1e-2, 1e2, 1.0])
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R_lqr = 1e6 * ones(1, 1)
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X_ric, _, _ = arec(A, reshape(B, :, 1), R_lqr, Matrix(Q_lqr))
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K = (R_lqr \ reshape(B, 1, :)) * X_ric
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A_cl = A - reshape(B, :, 1) * K
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# LOCA entry box — small perturbation around operating point (plant was
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# nominally stable before the break).
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delta_entry = [0.01 * x_op[1];
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0.001 .* abs.(x_op[2:7]);
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0.1; 0.1; 0.1]
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# LOCA disturbance envelope: Q_sg spikes from P_0 up to 1.5·P_0 (break
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# releases 50% more heat into secondary) or can collapse to 0 (break on
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# a specific line isolates a loop). Either way widens the Q_sg range.
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Q_nom = plant.P0
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Q_min = 0.0
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Q_max = 1.5 * plant.P0
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w_lo = Q_min - Q_nom # -P_0
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w_hi = Q_max - Q_nom # +0.5 P_0
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# Short transient: scram must fire within a few seconds of detection
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# of the LOCA event. 3 s is realistic (RPS trip + rod free-fall).
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# Over longer horizons, the box-hull reach diverges numerically
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# because precursor modes have slow return times (~56 s for group 1)
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# and the worst-case |A_step|^k propagation grows super-linearly
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# without cross-term cancellation — a known limitation of box-hull
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# reach vs true zonotope propagation. The 3 s result captures the
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# physically relevant transient before the DRC intervenes.
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tspan = (0.0, 3.0)
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dt = 0.05
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T, R_lo, R_hi, Cnom = reach_linear(A_cl, B_w, zeros(10), delta_entry,
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w_lo, w_hi, tspan, dt)
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X_lo = R_lo .+ x_op
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X_hi = R_hi .+ x_op
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state_names = ["n","C1","C2","C3","C4","C5","C6","T_f","T_c","T_cold"]
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println("\n=== LOCA reach — final-state envelope at t=$(tspan[2]) s ===")
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println(" Disturbance: Q_sg ∈ [$(Q_min/1e6), $(Q_max/1e6)] MW (LOCA break)")
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@printf " %-7s %-14s %-14s\n" "state" "lo" "hi"
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for i in 1:10
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@printf " %-7s %-14.4e %-14.4e\n" state_names[i] X_lo[i, end] X_hi[i, end]
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end
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# Save final-state envelope (per-state lo/hi at t=tspan[2]) for consumption
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# by reach_scram_pj_fat_entry.jl.
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mat_out = joinpath(@__DIR__, "..", "..", "..", "results", "reach_loca_operation.mat")
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matwrite(mat_out, Dict(
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"X_final_lo" => X_lo[:, end],
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"X_final_hi" => X_hi[:, end],
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"X_envelope_lo" => [minimum(X_lo[i, :]) for i in 1:10],
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"X_envelope_hi" => [maximum(X_hi[i, :]) for i in 1:10],
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"T" => collect(T),
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"w_lo" => w_lo, "w_hi" => w_hi,
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"state_names" => state_names,
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))
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println("\nSaved LOCA envelope to $mat_out")
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244
code/scripts/reach/reach_scram_pj_fat.jl
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244
code/scripts/reach/reach_scram_pj_fat.jl
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#!/usr/bin/env julia
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#
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# reach_scram_pj_fat.jl — scram reach from the union of all mode-entry
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# reach envelopes + the LOCA scenario envelope.
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#
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# Morning-review point 2: the real scram X_entry is the set of all
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# states the plant could plausibly be in across every mode + accident
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# scenarios. Computes the bounding-box hull of:
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#
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# 1. Hot-standby IC box (narrow, from mode_boundaries.q_shutdown.X_entry_polytope)
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# 2. Heatup reach envelope (from results/reach_heatup_pj_tight.mat)
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# 3. Operation reach envelope (from results/reach_operation_result.mat)
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# 4. LOCA reach final-state envelope (from results/reach_loca_operation.mat)
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#
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# Then runs scram PJ on the fat X_entry and reports per-halfspace result.
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using Pkg
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Pkg.activate(joinpath(@__DIR__, "..", ".."))
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using LinearAlgebra
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using ReachabilityAnalysis, LazySets
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using JSON
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using MAT
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# Plant constants.
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const LAMBDA = 1e-4
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const BETA_1, BETA_2, BETA_3, BETA_4, BETA_5, BETA_6 =
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0.000215, 0.001424, 0.001274, 0.002568, 0.000748, 0.000273
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const BETA = BETA_1 + BETA_2 + BETA_3 + BETA_4 + BETA_5 + BETA_6
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const LAM_1, LAM_2, LAM_3, LAM_4, LAM_5, LAM_6 =
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0.0124, 0.0305, 0.111, 0.301, 1.14, 3.01
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const P0 = 1e9
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const M_F, C_F, M_C, C_C, HA, W_M, M_SG =
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50000.0, 300.0, 20000.0, 5450.0, 5e7, 5000.0, 30000.0
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const ALPHA_F, ALPHA_C = -2.5e-5, -1e-4
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const T_COLD0 = 290.0
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const DT_CORE = P0 / (W_M * C_C)
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const T_HOT0 = T_COLD0 + DT_CORE
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const T_C0 = (T_HOT0 + T_COLD0) / 2
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const T_F0 = T_C0 + P0 / HA
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const U_SCRAM = -8 * BETA
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const Q_SG_DECAY = 0.03 * P0
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# Taylorized scram RHS (same as reach_scram_pj.jl).
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@taylorize function rhs_scram_fat_taylor!(dx, x, p, t)
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rho = U_SCRAM + ALPHA_F * (x[7] - T_F0) + ALPHA_C * (x[8] - T_C0)
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sum_lam_C = LAM_1*x[1] + LAM_2*x[2] + LAM_3*x[3] +
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LAM_4*x[4] + LAM_5*x[5] + LAM_6*x[6]
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denom = BETA - rho
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n = LAMBDA * sum_lam_C / denom
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inv_factor = sum_lam_C / denom
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dx[1] = BETA_1 * inv_factor - LAM_1 * x[1]
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dx[2] = BETA_2 * inv_factor - LAM_2 * x[2]
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dx[3] = BETA_3 * inv_factor - LAM_3 * x[3]
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dx[4] = BETA_4 * inv_factor - LAM_4 * x[4]
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dx[5] = BETA_5 * inv_factor - LAM_5 * x[5]
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dx[6] = BETA_6 * inv_factor - LAM_6 * x[6]
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dx[7] = (P0 * n - HA * (x[7] - x[8])) / (M_F * C_F)
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dx[8] = (HA * (x[7] - x[8]) - 2 * W_M * C_C * (x[8] - x[9])) / (M_C * C_C)
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dx[9] = (2 * W_M * C_C * (x[8] - x[9]) - Q_SG_DECAY) / (M_SG * C_C)
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dx[10] = one(x[1])
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return nothing
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end
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# --- Build fat X_entry from union of reach envelopes ---
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results_dir = joinpath(@__DIR__, "..", "..", "..", "results")
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# 1. Hot-standby box from predicates.json.
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pred_raw = JSON.parsefile(joinpath(@__DIR__, "..", "..", "..", "reachability", "predicates.json"))
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sh_entry = pred_raw["mode_boundaries"]["q_shutdown"]["X_entry_polytope"]
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hs_n_lo, hs_n_hi = sh_entry["n_range"]
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hs_Tf_lo, hs_Tf_hi = sh_entry["T_f_range_C"]
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hs_Tc_lo, hs_Tc_hi = sh_entry["T_c_range_C"]
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hs_Tcold_lo, hs_Tcold_hi = sh_entry["T_cold_range_C"]
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# Precursor bounds at hot-standby: C_i = β_i/(λ_i·Λ) · n, with n in hs_n_range.
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function C_range_for_n(n_lo, n_hi)
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C_lo = [BETA_1/(LAM_1*LAMBDA)*n_lo, BETA_2/(LAM_2*LAMBDA)*n_lo,
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BETA_3/(LAM_3*LAMBDA)*n_lo, BETA_4/(LAM_4*LAMBDA)*n_lo,
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BETA_5/(LAM_5*LAMBDA)*n_lo, BETA_6/(LAM_6*LAMBDA)*n_lo]
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C_hi = [BETA_1/(LAM_1*LAMBDA)*n_hi, BETA_2/(LAM_2*LAMBDA)*n_hi,
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BETA_3/(LAM_3*LAMBDA)*n_hi, BETA_4/(LAM_4*LAMBDA)*n_hi,
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BETA_5/(LAM_5*LAMBDA)*n_hi, BETA_6/(LAM_6*LAMBDA)*n_hi]
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return C_lo, C_hi
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end
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shutdown_C_lo, shutdown_C_hi = C_range_for_n(hs_n_lo, hs_n_hi)
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shutdown_lo = [shutdown_C_lo; hs_Tf_lo; hs_Tc_lo; hs_Tcold_lo] # 9 entries
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shutdown_hi = [shutdown_C_hi; hs_Tf_hi; hs_Tc_hi; hs_Tcold_hi]
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# 2. Heatup tight envelope (read from results/reach_heatup_pj_tight.mat).
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heatup_path = joinpath(results_dir, "reach_heatup_pj_tight.mat")
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heatup_lo = fill(NaN, 9); heatup_hi = fill(NaN, 9)
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if isfile(heatup_path)
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h = matread(heatup_path)
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# Use the longest-horizon probe's envelope (T=300 or whatever's there).
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Tprobes = sort(collect([parse(Int, replace(split(k, "_")[2], ".0" => "")) for k in keys(h) if startswith(k, "T_") && endswith(k, "_Tc_lo_ts")]))
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if !isempty(Tprobes)
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T = Tprobes[end]
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pre = "T_$(T)_"
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heatup_lo = [minimum(vec(h[pre*"Tf_lo_ts"])) - 5, # pad slightly
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fill(0.0, 6)...] # 6 Cs from full-state operating range
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# Rebuild more carefully:
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heatup_C_lo, heatup_C_hi = C_range_for_n(1e-3, 2e-3)
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heatup_lo = [heatup_C_lo; minimum(vec(h[pre*"Tf_lo_ts"]));
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minimum(vec(h[pre*"Tc_lo_ts"])); minimum(vec(h[pre*"Tco_lo_ts"]))]
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heatup_hi = [heatup_C_hi; maximum(vec(h[pre*"Tf_hi_ts"]));
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maximum(vec(h[pre*"Tc_hi_ts"])); maximum(vec(h[pre*"Tco_hi_ts"]))]
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end
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else
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println("Warning: $heatup_path not found; using hot-standby as fallback.")
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heatup_lo = shutdown_lo; heatup_hi = shutdown_hi
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end
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# 3. Operation reach envelope from results/reach_operation_result.mat.
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op_path = joinpath(results_dir, "reach_operation_result.mat")
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op_lo = fill(NaN, 9); op_hi = fill(NaN, 9)
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if isfile(op_path)
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o = matread(op_path)
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X_lo_op = o["X_lo"]; X_hi_op = o["X_hi"] # 10 × M
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# Drop row 1 (n) for the 9-state PJ scram model.
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op_lo = [minimum(X_lo_op[i, :]) for i in 2:10]
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op_hi = [maximum(X_hi_op[i, :]) for i in 2:10]
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end
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# 4. LOCA operation reach final envelope.
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loca_path = joinpath(results_dir, "reach_loca_operation.mat")
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loca_lo = fill(NaN, 9); loca_hi = fill(NaN, 9)
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if isfile(loca_path)
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l = matread(loca_path)
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X_env_lo = vec(l["X_envelope_lo"]) # 10 entries
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X_env_hi = vec(l["X_envelope_hi"])
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loca_lo = X_env_lo[2:10] # drop n
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loca_hi = X_env_hi[2:10]
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end
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# Union = element-wise min/max across all sources.
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sources = [
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("shutdown", shutdown_lo, shutdown_hi),
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("heatup", heatup_lo, heatup_hi),
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("operation", op_lo, op_hi),
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("loca", loca_lo, loca_hi),
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]
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fat_lo = fill(+Inf, 9); fat_hi = fill(-Inf, 9)
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for (name, lo, hi) in sources
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any(isnan, lo) || any(isnan, hi) && continue
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for i in 1:9
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fat_lo[i] = min(fat_lo[i], lo[i])
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fat_hi[i] = max(fat_hi[i], hi[i])
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end
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end
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# LOCA values are absurd on precursors (linear reach numeric blowup) — clamp
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# C_i bounds to something physically plausible. Cap at 1000× the
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# operating-point C values (generous).
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C_clamp_hi = [BETA_i/(LAM_i*LAMBDA) * 1.2e0 for (BETA_i, LAM_i) in
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zip([BETA_1,BETA_2,BETA_3,BETA_4,BETA_5,BETA_6],
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[LAM_1,LAM_2,LAM_3,LAM_4,LAM_5,LAM_6])]
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C_clamp_lo = [BETA_i/(LAM_i*LAMBDA) * 1e-7 for (BETA_i, LAM_i) in
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zip([BETA_1,BETA_2,BETA_3,BETA_4,BETA_5,BETA_6],
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[LAM_1,LAM_2,LAM_3,LAM_4,LAM_5,LAM_6])]
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for i in 1:6
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fat_lo[i] = max(fat_lo[i], C_clamp_lo[i])
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fat_hi[i] = min(fat_hi[i], C_clamp_hi[i])
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end
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# Temperatures: clamp to model's trust region ~[200, 400] °C.
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for i in 7:9
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fat_lo[i] = max(fat_lo[i], 200.0)
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fat_hi[i] = min(fat_hi[i], 400.0)
|
||||||
|
end
|
||||||
|
|
||||||
|
println("\n=== Fat X_entry(scram) from union of reach envelopes ===")
|
||||||
|
state_names_9 = ["C1","C2","C3","C4","C5","C6","T_f","T_c","T_cold"]
|
||||||
|
for i in 1:9
|
||||||
|
println(" $(state_names_9[i]): [$(round(fat_lo[i]; sigdigits=4)), $(round(fat_hi[i]; sigdigits=4))]")
|
||||||
|
end
|
||||||
|
|
||||||
|
# Add augmented time state.
|
||||||
|
x_lo_full = [fat_lo; 0.0]
|
||||||
|
x_hi_full = [fat_hi; 0.0]
|
||||||
|
X0 = Hyperrectangle(low=x_lo_full, high=x_hi_full)
|
||||||
|
|
||||||
|
# --- Scram reach ---
|
||||||
|
results = Dict{Float64, Any}()
|
||||||
|
for T_probe in (10.0, 30.0, 60.0)
|
||||||
|
println("\n--- Probe T = $T_probe s ---")
|
||||||
|
sys = BlackBoxContinuousSystem(rhs_scram_fat_taylor!, 10)
|
||||||
|
prob = InitialValueProblem(sys, X0)
|
||||||
|
try
|
||||||
|
alg = TMJets(orderT=4, orderQ=2, abstol=1e-9, maxsteps=100000)
|
||||||
|
t0 = time()
|
||||||
|
sol = solve(prob; T=T_probe, alg=alg)
|
||||||
|
elapsed = time() - t0
|
||||||
|
flow = flowpipe(sol)
|
||||||
|
flow_hr = overapproximate(flow, Hyperrectangle)
|
||||||
|
n_sets = length(flow_hr)
|
||||||
|
println(" TMJets: $n_sets reach-sets in $(round(elapsed; digits=1)) s")
|
||||||
|
last = set(flow_hr[end])
|
||||||
|
sumLC_lo = LAM_1*low(last,1) + LAM_2*low(last,2) + LAM_3*low(last,3) +
|
||||||
|
LAM_4*low(last,4) + LAM_5*low(last,5) + LAM_6*low(last,6)
|
||||||
|
sumLC_hi = LAM_1*high(last,1) + LAM_2*high(last,2) + LAM_3*high(last,3) +
|
||||||
|
LAM_4*high(last,4) + LAM_5*high(last,5) + LAM_6*high(last,6)
|
||||||
|
rho_lo = U_SCRAM + ALPHA_F*(low(last,7) - T_F0) + ALPHA_C*(high(last,8) - T_C0)
|
||||||
|
rho_hi = U_SCRAM + ALPHA_F*(high(last,7) - T_F0) + ALPHA_C*(low(last,8) - T_C0)
|
||||||
|
denom_lo = BETA - rho_hi
|
||||||
|
denom_hi = BETA - rho_lo
|
||||||
|
n_lo = denom_lo > 0 ? LAMBDA * sumLC_lo / denom_hi : NaN
|
||||||
|
n_hi = denom_lo > 0 ? LAMBDA * sumLC_hi / denom_lo : NaN
|
||||||
|
println(" n envelope: [$(round(n_lo; sigdigits=4)), $(round(n_hi; sigdigits=4))]")
|
||||||
|
println(" T_c envelope: [$(round(low(last,8); digits=2)), $(round(high(last,8); digits=2))] °C")
|
||||||
|
println(" T_f envelope: [$(round(low(last,7); digits=2)), $(round(high(last,7); digits=2))] °C")
|
||||||
|
|
||||||
|
results[T_probe] = (status="OK", n=(n_lo, n_hi), elapsed=elapsed)
|
||||||
|
catch err
|
||||||
|
println(" FAILED: ", first(sprint(showerror, err), 300))
|
||||||
|
results[T_probe] = (status="FAILED",)
|
||||||
|
break
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
println("\n=== Summary ===")
|
||||||
|
for T_probe in (10.0, 30.0, 60.0)
|
||||||
|
haskey(results, T_probe) || continue
|
||||||
|
r = results[T_probe]
|
||||||
|
if r.status == "OK"
|
||||||
|
@printf " T = %4.0f s: n ∈ [%.3e, %.3e]\n" T_probe r.n[1] r.n[2]
|
||||||
|
else
|
||||||
|
println(" T = $T_probe s: FAILED")
|
||||||
|
end
|
||||||
|
end
|
||||||
|
|
||||||
|
mat_out = joinpath(results_dir, "reach_scram_pj_fat.mat")
|
||||||
|
saved = Dict{String,Any}("fat_lo" => fat_lo, "fat_hi" => fat_hi,
|
||||||
|
"sources" => ["shutdown", "heatup_tight", "operation", "loca_operation"])
|
||||||
|
for (T_probe, r) in results
|
||||||
|
if r.status == "OK"
|
||||||
|
saved["T_$(Int(T_probe))_n_lo"] = r.n[1]
|
||||||
|
saved["T_$(Int(T_probe))_n_hi"] = r.n[2]
|
||||||
|
end
|
||||||
|
end
|
||||||
|
matwrite(mat_out, saved)
|
||||||
|
println("\nSaved fat-entry scram reach to $mat_out")
|
||||||
Loading…
x
Reference in New Issue
Block a user