Decompression Sickness Risk Modeling for EVA Operations

Physics-based and probabilistic models for predicting and managing decompression risk in high-frequency, exploration-class EVA.

Future lunar and Mars missions will require frequent, high-duration extravehicular activities (EVAs) under hypobaric conditions. Unlike current operations, these missions involve repeated exposures with incomplete inert gas washout, making decompression sickness (DCS) a cumulative and dynamically evolving risk.

Our research develops predictive models that quantify DCS risk as a function of physiology, workload, and mission profile. These models extend beyond legacy approaches by incorporating dynamic cardiovascular behavior, individual variability, and repeated exposure effects.

We develop an integrated modeling framework that combines mechanistic physiology with probabilistic risk estimation. The framework separates the problem into two coupled components:

• Deterministic modeling: simulation of inert gas kinetics and bubble growth under variable pressure, workload, and gravity
• Probabilistic inference: mapping decompression stress to severity-specific risk using statistically calibrated models

The deterministic component extends NASA’s Tissue Bubble Dynamics Model by incorporating dynamic perfusion, exercise effects, and altered-gravity physiology, producing a time-resolved measure of decompression stress.

This is coupled to a probabilistic framework that estimates the likelihood of DCS occurrence and severity, enabling risk-based evaluation of EVA protocols and operational constraints.

Conceptual framework for decompression risk modeling combining inert gas kinetics, bubble dynamics, and probabilistic risk estimation.

• Dynamic modeling of inert gas uptake and elimination under variable workload and gravity
• Explicit representation of exercise effects on decompression stress during EVA
• Integration of cardiovascular-perfusion models to capture physiological variability
• Extension from population-based models to individualized risk prediction

These developments address key limitations in current models, including fixed perfusion assumptions, lack of exercise representation, and inability to capture repeated exposure effects.

Model validation is performed using historical hypobaric exposure datasets and NASA altitude chamber studies, enabling direct comparison with established decompression risk frameworks.

Deterministic model outputs are evaluated against observed decompression outcomes, while probabilistic models are calibrated to reproduce time-to-event behavior and severity distributions under operational conditions.

Example model output showing dynamic decompression stress and compartmental inert gas behavior under representative EVA conditions.

The resulting models are designed to support both mission planning and real-time decision-making.

• Evaluation of prebreathe protocols and suit pressure configurations
• Assessment of repeated EVA schedules and cumulative exposure risk
• Individualized risk estimation based on physiology and workload
• Integration into decision-support tools for EVA operations

This work enables a transition from static, population-based decompression limits to dynamic, individualized risk prediction. The resulting capability supports safer EVA operations, improved mission planning, and the development of exploration-class protocols for sustained human presence beyond low Earth orbit.