design-flash-benchmark

name	design_flash_benchmark
description	Create a structured test matrix for comparing flash algorithm performance

Skill: Design Flash Benchmark

Purpose

Create a structured test matrix for comparing flash algorithm performance across fluid types, thermodynamic conditions, and difficulty levels.

When to Use

• Starting a new flash algorithm comparison study
• Extending an existing benchmark to new fluid families
• Designing stress tests for near-critical or multi-phase regions

Benchmark Design Procedure

Step 1: Select Fluid Families

Choose from these standard families:

Family	Components	Mole Fractions	Characteristics
Lean gas	CH4(0.90), C2(0.05), C3(0.03), N2(0.01), CO2(0.01)	Fixed or ±10%	Easy, mostly single-phase
Rich gas	CH4(0.70), C2(0.10), C3(0.08), nC4(0.05), nC5(0.03), N2(0.02), CO2(0.02)	Fixed or ±15%	Moderate, clear two-phase
Gas condensate	CH4(0.65), C2(0.08), C3(0.06), nC4(0.04), nC5(0.03), nC6(0.02), nC7(0.02), nC10(0.05), N2(0.02), CO2(0.03)	±20%	Near-critical behavior
CO2-rich	CO2(0.80), CH4(0.10), N2(0.05), H2S(0.03), C2(0.02)	±15%	Strong non-ideality
Wide-boiling	CH4(0.50), nC4(0.15), nC10(0.15), nC16(0.10), nC20(0.10)	±20%	Large volatility range
Sour gas	CH4(0.60), CO2(0.15), H2S(0.10), C2(0.08), C3(0.05), N2(0.02)	±15%	Acid gas behavior

Step 2: Define PT Space

For each family, define the pressure-temperature sampling grid:

import numpy as np

def generate_pt_grid(T_min_K, T_max_K, P_min_bara, P_max_bara, n_T=20, n_P=20):
    """Generate a regular PT grid."""
    T_values = np.linspace(T_min_K, T_max_K, n_T)
    P_values = np.logspace(np.log10(P_min_bara), np.log10(P_max_bara), n_P)
    cases = []
    for T in T_values:
        for P in P_values:
            cases.append({"T_K": float(T), "P_bara": float(P)})
    return cases

Standard ranges by family:

Family	T range (K)	P range (bara)	Focus region
Lean gas	200–400	1–200	Dew point region
Rich gas	220–450	5–300	Two-phase dome
Gas condensate	250–500	10–500	Near cricondenbar
CO2-rich	250–400	10–200	CO2 critical region
Wide-boiling	300–600	1–100	Large T range

Step 3: Add Stress Cases

Add cases specifically designed to challenge the algorithm:

1. Near bubble point: T slightly above Tbub at given P
2. Near dew point: T slightly below Tdew at given P
3. Near critical: T ≈ Tc ± 5K, P ≈ Pc ± 5 bar
4. Very low vapor fraction: β ≈ 0.001
5. Very high vapor fraction: β ≈ 0.999
6. Single phase (verification): Known single-phase conditions
7. Trace components: One component at < 1e-6 mole fraction

Step 4: Define Composition Perturbation

Use Dirichlet sampling to generate composition variants:

from numpy.random import dirichlet

def perturb_composition(base_comp, n_variants=10, concentration=50):
    """Generate composition variants around a base composition.

    Higher concentration = less perturbation.
    """
    names = list(base_comp.keys())
    alpha = np.array([base_comp[n] for n in names]) * concentration
    variants = []
    for _ in range(n_variants):
        x = dirichlet(alpha)
        variants.append(dict(zip(names, x.tolist())))
    return variants

Step 5: Define Metrics

Every benchmark case must record:

Metric	Type	Unit	How to Measure
converged	bool	—	Did the flash converge?
iterations	int	—	Total iterations (SS + NR)
ss_iterations	int	—	Successive substitution iterations only
nr_iterations	int	—	Newton-Raphson iterations only
cpu_time_ms	float	ms	Wall-clock time (median of 3 runs)
residual_norm	float	—	Final norm of equilibrium residuals
stability_tested	bool	—	Was stability analysis triggered?
stability_iters	int	—	TPD minimization iterations
n_phases	int	—	Number of phases at equilibrium
beta_vapor	float	—	Vapor phase fraction
phase_id_correct	bool	—	Correct phase identification?

Step 6: Estimate Total Cases

Target: 500–2000 cases per algorithm version.

Component	Cases
6 families × 20 PT points	120 base cases
10 composition variants each	1200 cases
50 stress cases	50 cases
Total	~1250 cases

Step 7: Generate Config File

Output benchmark_config.json:

{
  "benchmark_id": "tpflash_2026_01",
  "created": "2026-03-31",
  "algorithms": ["baseline", "candidate_eigenvalue_switch"],
  "eos_models": ["SRK"],
  "timing_repeats": 3,
  "families": [
    {
      "name": "lean_gas",
      "base_composition": {"methane": 0.90, "ethane": 0.05, "propane": 0.03, "nitrogen": 0.01, "CO2": 0.01},
       
       
        
        
       
       
    
  
   
     
     
     
     
  

Checklist

• All components available in NeqSim database
• PT ranges cover the two-phase region for each family
• Stress cases are well-defined
• Metrics list is complete
• Total case count is between 500 and 2000
• Random seed is fixed for reproducibility