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K-Load Polymer Software
Constitutive modeling and loss of performance due to coupled aging fatigue
K-Load: Simulating Multi-stressor Damage
Available Modules
What is K-Load?​
Case Studies
Download our case studies here
Case Study: Predicting Thermal Aging of NBR with Digital Twin Modeling
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This case study highlights the use of the K-Load Thermal Aging Module to model the long-term reliability of nitrile butadiene rubber (NBR) under accelerated aging. Stress–strain and relaxation data from unaged samples and two accelerated exposures at 80 °C (1.5 and 6 days) were used to train the digital twin. These datasets captured early degradation trends, enabling the model to simulate both intermittent constitutive behavior and long-term relaxation response of elastomers.
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To validate performance, the trained model was applied to predict NBR behavior at 60 °C. Notably, no 60 °C data were used in training, making this a pure forward prediction. The predictions closely matched experimental data, demonstrating the accuracy of the hybrid physics-informed machine learning approach. Unlike conventional Arrhenius models, K-Load accounts for multi-mechanism kinetics, reducing the number of required test conditions. This enables faster, more reliable qualification of rubber seals, gaskets, and elastomeric components in aerospace, automotive, and energy applications.
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Case Study 2 : Predicting Relaxation and Shelf-Life of Elastomers in Oil & Gas Environments
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Elastomer seals, gaskets, and packers used in oil and gas, geothermal, and petrochemical industries face unique challenges when exposed to hot brine, hydrocarbons, and high-pressure high-temperature (HPHT) fluids. One critical failure mode is stress relaxation, where constant strain leads to gradual stress loss due to thermal oxidation, hydrolysis, and network degradation. This directly impacts shelf-life, sealing reliability, and long-term safety of downhole and surface equipment.
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Using K-Load’s Relaxation and Diffusion-Limited Oxidation (DLO) modules, a digital twin was trained with a minimal dataset (relaxation at 20% strain in air at 60 °C, 50% in air at 100 °C, and 25% in oil at 60 °C). With this input, the model accurately predicted relaxation behavior across multiple temperatures (60–100 °C), pre-stretch levels (20–50%), and environments (air vs. oil). The oil retraining process captured hydrolysis and ingress effects while assuming non-corrosive oil, extending predictions to field-relevant conditions.
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This capability has broad relevance across oil and gas, automotive, aerospace, nuclear, and pharmaceutical industries, where polymers must maintain functionality under thermal, chemical, and mechanical stress. By reducing experimental campaigns by up to 70%, K-Load enables faster qualification of elastomers, improved lifetime prediction, and optimized maintenance planning, ultimately lowering operational risk.
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Polymer degradation
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Polymer aging prediction
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Polymer durability
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Polymer life prediction
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Polymer performance degradation
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Polymer material aging
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Polymer part life cycle
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Polymer component degradation
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Polymer part aging
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Polymer part life prediction
Brochures
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K-Load is a physics-informed simulation tool that enables engineers and scientists to:
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Predict polymer response to static and dynamic loading
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Analyze cyclic fatigue, creep, and stress-relaxation behavior
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Model multiaxial loading and time-dependent mechanical degradation
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Account for temperature-coupled mechanical aging and viscoelastic loss
Built using multi-physics solvers and validated with lab data, K-Load supports digital qualification and helps reduce reliance on long-term bench testing.