analyze-gibbs-convergence

name	analyze_gibbs_convergence
description	Interpret Gibbs energy minimization convergence metrics, analyze Jacobian

Skill: Analyze Gibbs Convergence

Purpose

Interpret Gibbs energy minimization convergence metrics, analyze Jacobian conditioning, verify element balance closure, and produce publication-quality figures for chemical equilibrium papers.

When to Use

• After running Gibbs reactor benchmark experiments
• When analyzing convergence of Newton-Raphson Gibbs minimization
• When comparing solver variants (baseline vs optimized)
• When investigating failure cases in chemical equilibrium

Analysis Procedure

Step 1: Load and Parse Results

import json
import pandas as pd
import numpy as np

def load_reactor_results(results_dir, solver_name):
    """Load JSONL results into DataFrame."""
    records = []
    with open(f"{results_dir}/raw/{solver_name}_results.jsonl") as f:
        for line in f:
            records.append(json.loads(line))
    return pd.DataFrame(records)

Step 2: Equilibrium Composition vs Temperature

The most important figure for a chemical equilibrium paper:

import matplotlib.pyplot as plt

def plot_equilibrium_composition(df, system_name, save_path):
    """Plot equilibrium mole fractions vs temperature for all species."""
    fig, ax = plt.subplots(figsize=(10, 7))

    species = [col for col in df.columns if col.startswith("n_")]

    for species_col in species:
        name = species_col.replace("n_", "")
        ax.semilogy(df["T_K"] - 273.15, df[species_col],
                     label=name, linewidth=2)

    ax.set_xlabel("Temperature (°C)", fontsize=12)
    ax.set_ylabel("Equilibrium mole fraction", fontsize=12)
    ax.set_title(f"Chemical Equilibrium — {system_name}", fontsize=14)
    ax.legend(loc="best", fontsize=10)
    ax.grid(True, alpha=0.3)
    ax.set_ylim(bottom=1e-12)

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches="tight")
    plt.close()

Step 3: Convergence Iteration Analysis

def plot_iteration_heatmap(df, save_path):
    """Heatmap of iteration count in T-P space."""
    fig, ax = plt.subplots(figsize=(10, 7))

    pivot = df.pivot_table(values="iterations", index="P_bara",
                           columns="T_K", aggfunc="mean")

    im = ax.pcolormesh(pivot.columns - 273.15, pivot.index,
                        pivot.values, cmap="YlOrRd", shading="auto")
    plt.colorbar(im, ax=ax, label="Iterations")

    ax.set_xlabel("Temperature (°C)")
    ax.set_ylabel("Pressure (bara)")
    ax.set_title("Newton Iteration Count")
    ax.set_yscale("log")

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches="tight")
    plt.close()

Step 4: Element Balance Verification

Critical for validating Gibbs reactor correctness:

def verify_element_balance(feed_composition, product_composition,
                            element_matrix):
    """Verify element conservation.

    element_matrix: dict mapping element -> {species: count}
    Example: {"H": {"H2S": 2, "H2O": 2, "H2": 2}, "S": {"H2S": 1, "S8": 8}}
    """
    results = {}
    for element, species_counts in element_matrix.items():
        feed_total = sum(feed_composition.get(sp, 0) * count
                         for sp, count in species_counts.items())
        prod_total = sum(product_composition.get(sp, 0) * count
                         for sp, count in species_counts.items())

        if feed_total > 0:
            rel_error = abs(feed_total - prod_total) / feed_total
        else:
            rel_error = 0.0 if prod_total == 0 else float("inf")

        results[element] = {
            "feed": feed_total,
            "product": prod_total,
            "relative_error": rel_error
        }
    return results

def plot_element_balance_closure(df, save_path):
    """Plot element balance errors across all cases."""
    fig, ax = plt.subplots(figsize=(8, 6))

    elements = [col for col in df.columns if col.startswith("elem_err_")]

    for col in elements:
        elem_name = col.replace(, ).upper()
        errors = df[col].replace(, )  
        ax.semilogy(((errors)), (errors),
                     label=elem_name, linewidth=)

    ax.axhline(y=, color=, linestyle=,
               alpha=, label=)
    ax.set_xlabel()
    ax.set_ylabel()
    ax.set_title()
    ax.legend()
    ax.grid(, alpha=)

    plt.tight_layout()
    plt.savefig(save_path, dpi=, bbox_inches=)
    plt.close()

Step 5: Jacobian Condition Number Analysis

def plot_jacobian_conditioning(df, save_path):
    """Analyze Jacobian conditioning across conditions."""
    fig, axes = plt.subplots(1, 2, figsize=(14, 6))

    # Condition number vs temperature
    ax = axes[0]
    ax.scatter(df["T_K"] - 273.15, df["jacobian_cond_number"],
               c=df["iterations"], cmap="viridis", alpha=0.5, s=20)
    ax.set_xlabel("Temperature (°C)")
    ax.set_ylabel("log₁₀(Condition Number)")
    ax.set_title("Jacobian Conditioning vs Temperature")
    ax.grid(True, alpha=0.3)

    # Condition number vs iterations
    ax = axes[1]
    ax.scatter(df["jacobian_cond_number"], df["iterations"],
               alpha=0.3, s=20)
    ax.set_xlabel("log₁₀(Condition Number)")
    ax.set_ylabel("Iterations to Convergence")
    ax.set_title("Conditioning vs Convergence Speed")
    ax.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches="tight")
    plt.close()

Step 6: Adiabatic vs Isothermal Comparison

def plot_adiabatic_vs_isothermal(df_iso, df_adi, save_path):
    """Compare equilibrium outcomes between modes."""
    fig, axes = plt.subplots(1, 2, figsize=(14, 6))

    # Temperature change in adiabatic mode
    ax = axes[0]
    dT = df_adi["outlet_T_K"] - df_adi["T_K"]
    ax.scatter(df_adi["T_K"] - 273.15, dT, alpha=0.5, s=20)
    ax.axhline(y=0, color="black", linestyle="-", alpha=0.3)
    ax.set_xlabel("Feed Temperature (°C)")
    ax.set_ylabel("ΔT (K)")
    ax.set_title("Adiabatic Temperature Change")
    ax.grid(True, alpha=0.3)

    # Iteration comparison
    ax = axes[1]
    ax.scatter(df_iso["iterations"], df_adi["iterations"], alpha=0.3, s=20)
    max_iter = max(df_iso["iterations"].max(), df_adi["iterations"].max())
    ax.plot([0, max_iter], [0, max_iter], "k--", alpha=0.5)
    ax.set_xlabel("Isothermal Iterations")
    ax.set_ylabel("Adiabatic Iterations")
    ax.set_title("Iteration Cost: Adiabatic vs Isothermal")
    ax.grid(True, alpha=0.3)
    ax.set_aspect("equal")

    plt.tight_layout()
    plt.savefig(save_path, dpi=, bbox_inches=)
    plt.close()

Step 7: Trace Species Behavior

def plot_trace_species(df, species_list, save_path):
    """Show how trace species evolve with temperature on log scale."""
    fig, ax = plt.subplots(figsize=(10, 7))

    for species in species_list:
        col = f"n_{species}"
        if col in df.columns:
            vals = df[col].replace(0, np.nan)
            ax.semilogy(df["T_K"] - 273.15, vals,
                         label=species, linewidth=2, marker="o", markersize=3)

    ax.set_xlabel("Temperature (°C)")
    ax.set_ylabel("Equilibrium moles")
    ax.set_title("Trace Species at Chemical Equilibrium")
    ax.legend()
    ax.grid(True, alpha=0.3)

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches="tight")
    plt.close()

Step 8: Validation Against Reference Data

def plot_validation_parity(neqsim_results, reference_results,
                            species_name, save_path):
    """Parity plot: NeqSim vs reference (NASA CEA, JANAF, etc.)."""
    fig, ax = plt.subplots(figsize=(7, 7))

    ax.scatter(reference_results, neqsim_results, s=40, alpha=0.7,
               edgecolors="black", linewidths=0.5)

    # Diagonal
    lims = [min(min(reference_results), min(neqsim_results)),
            max(max(reference_results), max(neqsim_results))]
    ax.plot(lims, lims, "k-", alpha=0.5, label="Perfect agreement")

    # ±10% bands
    ax.plot(lims, [l * 1.1 for l in lims], "r--", alpha=0.3, label="±10%")
    ax.plot(lims, [l * 0.9 for l in lims], "r--", alpha=0.3)

    ax.set_xlabel(f"Reference {species_name}")
    ax.set_ylabel(f"Calculated {species_name}")
    ax.set_title(f"Validation: {species_name}")
    ax.legend()
    ax.grid(True, alpha=0.3)
    ax.set_aspect("equal")

    plt.tight_layout()
    plt.savefig(save_path, dpi=300, bbox_inches="tight")
    plt.close()

Figure Catalog for Gibbs Reactor Papers

Figure	Shows	Section
Equilibrium composition vs T	Species distribution at equilibrium	Results
Iteration heatmap (T-P)	Where solver works hard	Results
Element balance closure	Conservation law verification	Validation
Jacobian conditioning	Numerical stability	Discussion
Adiabatic vs isothermal	Mode comparison	Results
Trace species	Low-abundance species behavior	Results
Parity plot vs reference	Accuracy validation	Validation
Convergence history	Residual vs iteration for selected cases	Methods/Discussion

Table Catalog

Table	Shows	Section
Reaction systems tested	Feed, products, conditions	Methods
Convergence summary	Rate, iterations, timing by system	Results
Element balance summary	Max error per element per system	Validation
Reference comparison	AAD% vs NASA CEA / JANAF	Validation
Solver variant comparison	If comparing solver settings	Results

Key Validation Criteria

For a Gibbs reactor paper to be credible:

1. Element balance: Relative error < 1e-10 for ALL elements in ALL cases
2. Gibbs energy: Total G strictly decreases (or unchanged) each iteration
3. Reference agreement: AAD < 5% vs NASA CEA for major species at equilibrium
4. Trace species: Non-negative mole numbers (no unphysical negative values)
5. Mass balance: |mass_in - mass_out| / mass_in < 1e-10