Services / Integrity Modeling Services

Integrity Modeling Services

Mechanism-specific models for integrity decisions and continuous asset tracking.

We convert qualified inspection evidence, material information and operating history into mechanism-specific representations of asset condition and how it evolves under defined operating scenarios. The output is a forecast from a model tuned to the governing physics — a consumed-life fraction, fatigue usage, remaining-wall trajectory, transformed-phase fraction or capacity margin — that tells Integrity Managers what to inspect, when to reassess and when an RBI update or Fitness-for-Service assessment is due.

Discuss an integrity challenge Read: sigma-phase modelling

01 — The need it solves

Inspection tells you what is there. It does not tell you what happens next.

Thickness readings, metallography, thermography and process monitoring establish condition at a single moment and location. The Integrity Manager still has to decide what that evidence means for continued operation — and that question is far broader than calculating a generic damage factor.

The questions an Integrity Manager actually faces

What physical or metallurgical state is evolving?
How did the current state develop under the actual operating history?
Which asset sections, material populations or weld regions are most susceptible?
Which local thermal, mechanical, chemical or flow variables control the evolution?
Does the evidence indicate stable progression, acceleration or a change in degradation regime?
How would credible changes in operation, loading, refractory condition, process chemistry or cycling alter the forecast?
Which additional measurement would most reduce decision uncertainty?

Why equipment averages fail

Industrial assets are not physically uniform. Material condition, weld metallurgy, local stress, wall temperature, flow pattern, deposits, refractory condition and exposure history vary within a single equipment item. Decisions based on equipment-average variables overlook the local conditions that govern degradation.

Bulk-stream chemistry that does not represent the chemistry beneath a deposit at the metal surface.
Process-fluid temperature that does not equal local metal-wall temperature.
Nominal velocity that does not describe the local particle-impact field at an elbow, nozzle, transition or cyclone connection.
Equipment-average loading that does not represent stress at a weld, attachment, discontinuity or support.
Calendar time that does not distinguish periods accumulated at materially different temperature and stress combinations.
A nominal alloy designation that does not distinguish base metal, weld metal, heat-affected zone, repair region or service-evolved microstructure.

02 — When it is applicable

Scalable, but not indiscriminate

Integrity Modeling is built for organisations with established inspection, RBI and FFS systems that need greater analytical resolution for selected assets, mechanisms or decisions. Every engagement passes a technical qualification step before any quantitative projection is made.

Most valuable when

Degradation depends strongly on local conditions — welds, hot spots, impact zones, deposits
Historical exposure can be reconstructed from historian, inspection and maintenance records
Alternative operating scenarios must be compared before a decision is made
Recurring inspection data must become a governed, continuously updated assessment
An RBI update, FFS assessment, repair or replacement decision needs stronger technical evidence

A mature reliability and integrity culture is required. Where foundations are still developing, work begins with a bounded modelability and data-readiness engagement (S1).

Material definition

Grade, product form, composition where it affects kinetics, base/weld/HAZ distinction, heat treatment, repairs and service-evolved microstructure. Correlations are not transferable between alloys or conditions.

Exposure history

Metal temperature rather than process temperature, stress and pressure, start-ups, shutdowns and upsets, cycle counts, chemistry, flow and solids loading, refractory and hot-spot history.

Local condition

An initial or calibration state at the section: thickness mapping, replication or metallography, phase quantification, crack characterisation, hardness, deposit analysis or dimensional survey.

Reference basis

Creep-rupture data, fatigue curves, transformation kinetics, corrosion or erosion correlations applicable to the material–mechanism pair. Without one, the engagement defines the testing programme rather than presenting unsupported precision.

Model maturity defines permitted use

Maturity is stated explicitly in every deliverable. Progression is driven by evidence and decision consequence — not software complexity. A preliminary model already creates value by identifying the evidence needed for the next level of confidence.

IMechanism and modelability assessmentScope definition and inspection/data planning
IIScreening or bounding modelPrioritisation and targeted evidence acquisition
IIIField-calibrated modelState estimation, scenario assessment and support to RBI review
IVValidated decision-support modelInput to specified higher-consequence integrity decisions
VOperational digital modelContinuous integrity surveillance and repeatable decision support

03 — Formal foundation

The output is selected by the physics — no universal damage index

Each model is a mathematical representation of a specific mechanism in a defined asset section. Its general form connects the mechanism-specific state to operating history, material condition, geometry and calibrated parameters:

z(t) = M [ z₀, x(0:t), m, g, θ ]

z0: Initial mechanism-specific state
x(0:t): Operating history
m: Material condition
g: Geometry and boundary conditions
θ: Calibrated or referenced parameters

Mechanism → calculation → decision

Mechanism class	Representative calculation	Decision relevance
Time-dependent rupture (creep)	Consumed-life fraction, rupture-time distribution or remaining-life interval	High-temperature inspection, metallurgical verification, operating-envelope review and FFS initiation
Cyclic degradation (fatigue)	Cycle spectrum, alternating-stress history and fatigue usage	Transient management, crack-detection strategy, structural refinement and inspection timing
Material loss (corrosion, erosion)	Corrosion or erosion rate, penetration depth and remaining-wall trajectory	Thickness-monitoring locations, mitigation, inspection timing and metal-loss assessment
Metallurgical transformation	Phase fraction, transformation-rate band, morphology or degraded-property estimate	Targeted metallography, property recharacterisation, brittle-fracture screening and replacement planning
Local thermal, stress or flow condition	Temperature, stress, strain, heat-flux or particle-impact field	Critical-location definition and input to the governing mechanism model
Multi-mechanism condition	Mechanism-specific outputs evaluated together, without unsupported aggregation	Identification of interacting threats and coordinated inspection or assessment scope

A sigma-phase model calculates a transformed phase fraction — a microstructural condition, not residual life. That is exactly why a single universal damage index would be technically inappropriate.

Ten-stage technical methodology

01
Define the decision
The engagement starts with the action that needs support — inspection targeting, scenario evaluation, damage reconstruction, FFS initiation or post-inspection updating. The decision sets the required fidelity, validation burden and update frequency.
02
Confirm the mechanism
The candidate mechanism is tested against material susceptibility, operating conditions, observed morphology, fabrication and repair history. Damage mode (what is observed) is kept distinct from damage mechanism (how it developed).
03
Section the asset
Equipment is divided into physically coherent sections by base metal, weld and HAZ, microstructure, repairs, geometry, metal-temperature regime, chemistry, flow, refractory condition, restraint and inspection coverage.
04
Qualify the evidence
Every input — drawings, weld maps, historian signals, NDE, thickness grids, metallography, thermography, deposit analysis — is classified as measured, derived, inferred, referenced or assumed.
05
Select the state variable
The output is chosen by the mechanism: life fraction, fatigue usage, remaining wall, erosion rate, phase fraction, degraded property, local field, capacity margin or time to a defined threshold.
06
Reconstruct history
The operating record is segmented for the mechanism: continuous exposure integrated over time, cyclic histories decomposed by cycle counting, loss rates integrated into wall trajectories, kinetics advanced through non-isothermal blocks.
07
Calibrate and validate
Uncertain parameters are calibrated against repeat thickness, phase quantification, cavity grading, hardness, replication or wear profiles, and validated against independent observations. Unvalidated models are labelled for screening use.
08
Assess scenarios
Explicit combinations of operating and condition variables — current envelope, degraded refractory, altered throughput, solids loading, chemistry, start-up practice or mitigation — show which scenario is more severe and why.
09
Translate results into action
Every output carries a decision interpretation: current state and confidence, principal drivers, sections requiring attention, forecasts, next evidence required, triggers for RBI or FFS, and permitted uses.
10
Operationalise the model
Where continuing use is required, the calculation is deployed in a governed digital environment with data-ingestion rules, versioning, engineering review, dashboards and defined ownership.

Positioned within API practice — not above it

Inspection, RBI and FFS retain their distinct functions. Integrity Modeling adds asset-specific analytical resolution where the decision warrants it and the evidence supports it.

Integrity function	Primary purpose	Relationship to Integrity Modeling
Damage-mechanism review (API 571)	Establish credible degradation mechanisms, susceptible materials, critical factors and inspection implications	Defines the physical phenomenon that may justify modelling
Inspection and condition monitoring	Determine the observed condition and detect relevant damage	Provides the evidence used to initialise, calibrate and validate the model
Risk-Based Inspection (API 580/581)	Integrate probability and consequence of failure to prioritise inspection	Can refine damage-state assumptions, inspection effectiveness and scenario inputs, subject to owner-operator governance
Fitness-for-Service (API 579-1/ASME FFS-1)	Evaluate structural acceptability of a defined damage state under specified loads	Provides the acceptance framework when modelled damage must be translated into component adequacy
Integrity operating limits	Maintain operation within defined integrity boundaries	Supplies operating variables and a pathway for scenario control and model updating

What remains unchanged

Not superseded: Applicable laws, regulations or authorities having jurisdiction
Not superseded: Construction and in-service inspection codes
Not superseded: API 571-based damage-mechanism identification
Not superseded: API 580/581 RBI governance and risk acceptance
Not superseded: API 579-1/ASME FFS-1 assessment procedures
Not superseded: Owner-operator inspection requirements and engineering practices
Not superseded: Approved material, welding, repair or operating requirements
Not superseded: The need for direct inspection and qualified field evidence

The final integrity decision remains with the owner-operator and its authorised integrity organisation. Model outputs are engineering inputs whose permitted use is governed by model maturity, data quality, uncertainty and the significance of the decision.

04 — Deliverables

An integrated engineering and digital package — not a one-time calculation

The centre of the service is the mathematical representation of the mechanism. The full package keeps it traceable, maintainable, connected to real evidence and usable by the integrity organisation on a recurring basis. Configuration is proportional to the decision and the model's maturity.

Technical basis

Mechanism definition and evidence basis
Applicable standards, recommended practices and references
Material and microstructural basis
Physical interpretation of the state variable
Equations, algorithms and parameter sources
Assumptions, applicability range and interaction review

Asset and data architecture

Damage-relevant asset segmentation
Material, weld and repair population definition
Mechanism–variable–measurement map
Input register and data dictionary
Data-lineage and quality assessment
Historian, inspection and laboratory interface specification

Computational model

Reproducible calculation engine
Historical reconstruction logic
Calibration and validation routines
Scenario, sensitivity and uncertainty engine
Rules for incorporating new evidence
Test cases and calculation-verification records

Web application

Asset and section navigation
Controlled data upload or system connection
Approved recalculation workflow
State, forecast and scenario dashboards
Inspection-to-prediction comparison
Model-version and source-data traceability with role-based approval

Engineering results

Reconstructed exposure and state evolution
Current calculated, calibrated or bounded state
Baseline and alternative operating scenarios
Controlling-variable and sensitivity assessment
Time to a formally established threshold, where supported
Ranked inspection, mitigation or assessment actions

Integrity decision package

Executive technical summary
Decision-oriented result cards
Triggers for inspection, model update, RBI review or FFS
Prioritised data-gap and evidence-acquisition plan
Model-maturity statement
Governance and update roadmap

Typical digital architecture

Historian / inspection / laboratory / engineering data
Controlled ingestion and validation
Mechanism-specific calculation services
Results, scenarios and decision triggers
Governed web interface

The application does not convert an unqualified measurement into decision-grade evidence. Once the agreed data architecture is in place, every new inspection becomes a controlled model update rather than a disconnected report.

Types of engagement

Modelability and data readiness

Defines the mechanism, analytical sections, candidate state variable, reference basis and evidence required to proceed.

Outputs: Modelability decision, preliminary sectioning, data-readiness map, prioritised evidence plan

Model development

Builds the mechanism-specific model from available material, operating and inspection evidence.

Outputs: Technical basis, computational model, historical reconstruction, scenarios, maturity statement

Digital deployment

Implements the approved model in a governed web application connected to agreed data flows.

Outputs: Data interfaces, validation rules, dashboards, traceability controls, deployment documentation

Calibration and validation

Incorporates new inspection, metallurgical, laboratory or operating evidence into an existing model.

Outputs: Parameter update, forecast-to-observation comparison, revised prediction bounds

Continuing model service

Maintains the model and application as operating and inspection evidence accumulates.

Outputs: Periodic state updates, scenario review, trigger monitoring, version-controlled engineering record

05 — Decision-making & business value

Better decisions through better definition of damage — not simply more calculation

Integrity Modeling creates a continuous line of sight from the physical mechanism to the management decision, so inspection budgets, shutdown scope and run-or-repair calls are grounded in how each section is actually evolving.

Evidence
Mechanism
State variable
Forecast & scenarios
Decision trigger
Integrity action

Where should inspection be concentrated?

Identifies sections where local exposure, material condition and mechanism kinetics produce the highest susceptibility or fastest evolution.

What should be measured?

Defines the inspection method, location, data resolution and metallurgical or chemical evidence needed to calibrate the active mechanism.

When is reassessment required?

Tracks the relevant state variable and establishes technical triggers tied to inspection, characterisation, FFS or operating review.

Which operating variable matters most?

Quantifies sensitivity to temperature, stress, cycling, flow, solids loading, chemistry, refractory condition and other drivers.

Can operating alternatives be compared?

Evaluates baseline, mitigated and severe-but-credible scenarios on a common physical basis.

Is a simplified assessment sufficient?

Starts simple, then determines whether screening or bounding is enough or a calibrated higher-fidelity model is required.

How should new inspection data be used?

Updates the initial condition and calibration, compares forecast with observation and revises confidence bounds.

How does the result enter the integrity system?

Produces traceable inputs, triggers and result summaries for RBI, inspection planning and FFS workflows.

The business value

Inspection spend where it matters
Effort concentrates on the sections evolving fastest, not spread evenly across equipment averages.
Defensible run-or-repair decisions
Repair, replacement and FFS initiation are triggered by a tracked state variable with stated confidence.
Operating flexibility with known consequences
Throughput, cycling and chemistry changes are evaluated for their integrity cost before they are made.
A compounding analytical asset
Every inspection improves the model, so integrity knowledge accumulates instead of resetting with each report.

Representative developments

Anonymised consulting developments showing how different mechanisms require different mathematical outputs, evidence chains and decision pathways.

High-temperature creep

D_creep = Σ Δt_i / t_r(T_i, σ_i)

Refractory-lined high-temperature equipment, operating history segmented by effective stress and metal-wall temperature. Applicable stress–temperature–rupture correlations gave segment-specific rupture time and cumulative life consumption, with refractory condition treated as a driver of metal temperature.

Integrity decisions supported

Identify sections accumulating creep life most rapidly
Determine whether refractory degradation or loading uncertainty controls the forecast
Prioritise replication, metallography or local temperature verification
Define when a formal high-temperature FFS or life-assessment update is required

Solid-particle erosion

Penetration rate = f(ṁ_p, v^n, g(α), A, target properties)

High-velocity gas–solid system with a protective refractory lining. Operating records were converted to a time-dependent erosion rate and integrated into cumulative material loss, revealing a decisive regime change once lining loss exposes the pressure-retaining metal.

Integrity decisions supported

Prioritise refractory inspection where loss of protection exposes metal
Identify geometry-defined impact zones for thickness mapping
Compare wear under velocity, solids-loading and impact-angle scenarios
Decide whether a simplified correlation is sufficient or CFD is warranted

Thermal fatigue

Temperature history → thermal stress → rainflow → fatigue usage

Section-specific metal temperatures reconstructed from process signals and thermography, converted into thermal-stress histories, rainflow-counted and assessed against an applicable fatigue basis. A post-intervention shift in thermal response showed why recalibration is mandatory.

Integrity decisions supported

Identify sections exposed to the most damaging temperature differentials
Determine which start-up, shutdown or process transients dominate usage
Evaluate the benefit of modified cycling practice
Define when detailed thermo-mechanical FEA is required

Sigma-phase embrittlement

f_σ(t, T) = f_max · [1 − exp(−k(T)·t^n)]

JMAK kinetics calculated transformed sigma fraction for susceptible stainless microstructures under non-isothermal histories, separating base metal from weld and ferrite-bearing populations. The output is a metallurgical state, not residual life — its value comes from linking it to toughness and loading demand.

Integrity decisions supported

Identify material and weld populations requiring separate treatment
Determine where targeted phase quantification is required
Set a metallurgical change-of-regime trigger for property recharacterisation
Provide material-state input to FFS without misrepresenting phase fraction as probability of failure

Standards define what must remain acceptable. Integrity Modeling establishes how the relevant physical or metallurgical state is evolving, which evidence controls confidence, and what action it requires.

Start with a modelability assessment

Engineering Context

This page connects Knar's work to established engineering, reliability, and asset-management disciplines where those connections are materially relevant.

Asset integrity management — For mechanical integrity and asset integrity management teams, this service strengthens Risk-Based Inspection (API 580/581) and Fitness-for-Service (API 579) decisions with section-specific damage evidence — without replacing the governing codes.
Damage mechanism modelling and remaining life assessment — Remaining life assessment for creep, thermal fatigue, corrosion, erosion and sigma-phase embrittlement, using physics-based damage mechanism models calibrated to your inspection and metallography data.
Scenario analysis — Operating changes in temperature, throughput, cycling, solids loading or chemistry are compared as explicit what-if scenarios, showing how each alters the integrity forecast and why.
Uncertainty quantification — Every forecast carries prediction bands and a stated model-maturity level, and the service identifies which additional measurement would most reduce uncertainty in the integrity decision.
Industrial Digital Assets — Each integrity model is deployed as a governed, updateable Industrial Digital Asset, so every new inspection becomes a controlled model update instead of a disconnected report.
Industrial decision support — Model outputs become decision triggers for inspection planning, mitigation, repair or replacement, giving Integrity Managers a traceable line of sight from evidence to action.

Integrity Modeling Services

Mechanism-specific models for integrity decisions and continuous asset tracking.

Mechanism class

Representative calculation

Decision relevance

Time-dependent rupture (creep)

Consumed-life fraction, rupture-time distribution or remaining-life interval

High-temperature inspection, metallurgical verification, operating-envelope review and FFS initiation

Cyclic degradation (fatigue)

Cycle spectrum, alternating-stress history and fatigue usage

Transient management, crack-detection strategy, structural refinement and inspection timing

Material loss (corrosion, erosion)

Corrosion or erosion rate, penetration depth and remaining-wall trajectory

Thickness-monitoring locations, mitigation, inspection timing and metal-loss assessment

Metallurgical transformation

Phase fraction, transformation-rate band, morphology or degraded-property estimate

Targeted metallography, property recharacterisation, brittle-fracture screening and replacement planning

Local thermal, stress or flow condition

Temperature, stress, strain, heat-flux or particle-impact field

Critical-location definition and input to the governing mechanism model

Multi-mechanism condition

Mechanism-specific outputs evaluated together, without unsupported aggregation

Identification of interacting threats and coordinated inspection or assessment scope

Integrity function

Primary purpose

Relationship to Integrity Modeling

Damage-mechanism review (API 571)

Establish credible degradation mechanisms, susceptible materials, critical factors and inspection implications

Defines the physical phenomenon that may justify modelling

Inspection and condition monitoring

Determine the observed condition and detect relevant damage

Provides the evidence used to initialise, calibrate and validate the model

Risk-Based Inspection (API 580/581)

Integrate probability and consequence of failure to prioritise inspection

Can refine damage-state assumptions, inspection effectiveness and scenario inputs, subject to owner-operator governance

Fitness-for-Service (API 579-1/ASME FFS-1)

Evaluate structural acceptability of a defined damage state under specified loads

Provides the acceptance framework when modelled damage must be translated into component adequacy

Integrity operating limits

Maintain operation within defined integrity boundaries

Supplies operating variables and a pathway for scenario control and model updating

Engineering Context

This page connects Knar's work to established engineering, reliability, and asset-management disciplines where those connections are materially relevant.

Asset integrity management — For mechanical integrity and asset integrity management teams, this service strengthens Risk-Based Inspection (API 580/581) and Fitness-for-Service (API 579) decisions with section-specific damage evidence — without replacing the governing codes.

Damage mechanism modelling and remaining life assessment — Remaining life assessment for creep, thermal fatigue, corrosion, erosion and sigma-phase embrittlement, using physics-based damage mechanism models calibrated to your inspection and metallography data.

Scenario analysis — Operating changes in temperature, throughput, cycling, solids loading or chemistry are compared as explicit what-if scenarios, showing how each alters the integrity forecast and why.

Uncertainty quantification — Every forecast carries prediction bands and a stated model-maturity level, and the service identifies which additional measurement would most reduce uncertainty in the integrity decision.

Industrial Digital Assets — Each integrity model is deployed as a governed, updateable Industrial Digital Asset, so every new inspection becomes a controlled model update instead of a disconnected report.

Industrial decision support — Model outputs become decision triggers for inspection planning, mitigation, repair or replacement, giving Integrity Managers a traceable line of sight from evidence to action.

Integrity Modeling Services

Inspection tells you what is there. It does not tell you what happens next.

The questions an Integrity Manager actually faces

Why equipment averages fail

Scalable, but not indiscriminate

Most valuable when

Material definition

Exposure history

Local condition

Reference basis

Model maturity defines permitted use

The output is selected by the physics — no universal damage index

Mechanism → calculation → decision

Ten-stage technical methodology

Define the decision

Confirm the mechanism

Section the asset

Qualify the evidence

Select the state variable

Reconstruct history

Calibrate and validate

Assess scenarios

Translate results into action

Operationalise the model

Positioned within API practice — not above it

What remains unchanged

An integrated engineering and digital package — not a one-time calculation

Technical basis

Asset and data architecture

Computational model

Web application

Engineering results

Integrity decision package

Typical digital architecture

Types of engagement

Modelability and data readiness

Model development

Digital deployment

Calibration and validation

Continuing model service

Better decisions through better definition of damage — not simply more calculation

Where should inspection be concentrated?

What should be measured?

When is reassessment required?

Which operating variable matters most?

Can operating alternatives be compared?

Is a simplified assessment sufficient?

How should new inspection data be used?

How does the result enter the integrity system?

The business value

Inspection spend where it matters

Defensible run-or-repair decisions

Operating flexibility with known consequences

A compounding analytical asset

Representative developments

High-temperature creep

Solid-particle erosion

Thermal fatigue

Sigma-phase embrittlement

Engineering Context

Integrity Modeling Services

Inspection tells you what is there. It does not tell you what happens next.

The questions an Integrity Manager actually faces

Why equipment averages fail

Scalable, but not indiscriminate

Most valuable when

Material definition

Exposure history

Local condition

Reference basis

Model maturity defines permitted use

The output is selected by the physics — no universal damage index

Mechanism → calculation → decision

Ten-stage technical methodology

Define the decision

Confirm the mechanism

Section the asset

Qualify the evidence

Select the state variable

Reconstruct history