SEARCH · Engineering Papers

Results for “System Health Management”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

768 records

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Development of a Digital Twin for Electrified Aircraft Powertrain Health Management

The augmentation of aircraft powertrains with electrical power systems is a promising path to reducing aircraft fuel consumption, emissions, and noise. Like conventional propulsion systems, electrified aircraft propulsion (EAP) systems will be subject to wear and tear throughout their lifecycles. System health management for EAP will enable efficient flight and maintenance scheduling, realizing economic, safety, and reliability benefits. A digital twin is, broadly, a dynamically updated virtual representation of an individual physical asset. This paper presents a Kalman filter-based approach for the development of a digital twin for an electrified powertrain and applies the approach to an EAP controls testbed. Measurements from nominal testbed operations are used to update a nonlinear model of the testbed. A Kalman filter is then created and used to identify and isolate anomalous testbed behavior based on measurements from off- nominal operations. Results show that the Kalman filter-based digital twin can monitor individual powertrain components’ health for degradation or other changes in performance. The applicability of the presented digital twin approach to any hybrid- or fully-electrified powertrain is emphasized.

Electrified Aircraft Propulsion↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗

NASA NTRSDate not supplied

Thermal Data-driven Model Reduction for Enhanced Battery Health Monitoring

Electric aviation faces a major challenge of avoiding potentially catastrophic consequences of the battery’s thermal runaway while keeping the weight of the battery low. Detection of early warning signals of battery failures requires accurate monitoring of the battery’s health throughout its lifespan. However, identifying the parameters of the battery from field data is notoriously difficult. We investigate this problem within the framework of modeling the temperature dynamics of a Li-ion cell during tests simulating loading in electric aircraft flights. It is found that the parameters of a higher-fidelity physics-based thermal model cannot be identified from the simulated flight data. To resolve this issue, we reduce the higher-fidelity thermal model to a model with fewer parameters. The resulting reduced-order model can predict temperature dynamics accurately and is identifiable throughout the cell’s lifespan which allows using the model’s parameters to monitor the state-of-health of the aging cell and detect anomalies in thermal behavior.

Li ion batteries↗

NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗

NASA NTRS2011

Exploring the Model Design Space for Battery Health Management

Battery Health Management (BHM) is a core enabling technology for the success and widespread adoption of the emerging electric vehicles of today. Although battery chemistries have been studied in detail in literature, an accurate run-time battery life prediction algorithm has eluded us. Current reliability-based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This paper presents a Particle Filter (PF) based BHM framework with plug-and-play modules for battery models and uncertainty management. The batteries are modeled at three different levels of granularity with associated uncertainty distributions, encoding the basic electrochemical processes of a Lithium-polymer battery. The effects of different choices in the model design space are explored in the context of prediction performance in an electric unmanned aerial vehicle (UAV) application with emulated flight profiles.

Saha, Bhaskar↗

NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.

Satellites↗

NASA NTRS1990

Development of nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed employs the nickel-cadmium battery expert system (NICBES-2) which supports the evaluation of performances of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 also provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort to modify NICBES-2 to accommodate nickel-hydrogen battery environment in testbed is described.

Shiva, Sajjan G.↗

NASA NTRSDate not supplied

Thin-Film Embedded Sensors for Battery Health Monitoring

Hybrid or all-electric aircraft are being developed as the next generation of aircraft to both allow new forms of aviation and decrease environmental impact. Since these types of aircraft are based on high-capacity battery technology, safe operation of these batteries becomes increasingly important. In particular, the potential for battery failure due to uncontrolled chemical reactions resulting in thermal runaway, catastrophic failure, and battery fires must be addressed in order for such battery technology to have the level of safety needed for standard aviation implementation. Efforts to ensure battery safety often involve engineering solutions that seek to contain rather than prevent such events by early detection. Such approaches increase the system weight and decrease the power per unit mass provided by the battery system. Existing methods for measuring battery parameters to determine the battery state-of-health are limited. These methods include electrical measurements of the cell current and/or voltage output as well as temperature measurements taken externally on the cell surface. Such external temperature measurements are limited in their ability to provide early warning of impending battery failure. In response, an effort to develop sensors operating internal to battery for health monitoring has been ongoing in the NASA Sensor-based Prognostics to Avoid Runaway Reactions & Catastrophic Ignition (SPARRCI) project. The basic approach associated with this sensor work is the deposition of thin film sensors on the battery separator located between the anode and cathode of the battery. These thin film sensors are then monitored to determine changes in battery parameters and health. Microfabrication techniques are employed to minimize the overall impact of the sensors on battery operation through the implementation of sensors with minimal size, weight, and power consumption. The thickness of the films, which are fabricated through physical vapor deposition (sputtering), are on the order of thousands of angstroms and can have minimal surface area. Thin film sensors for system health management have been implemented for a many decades on complex components for aerospace applications. However, the application of thin films of this type on a battery separator for internal battery monitoring applications has not previously been demonstrated to our knowledge. This paper describes the development of sensors for the internal battery monitoring through the use of thin film sensor technology. Thin metal films were successfully deposited on a battery separator polymer material with good adherence and electrical continuity. Multiple types of sensors have been deposited, as well as lead connections from the sensor to the edge of the separator material. The ability of these thin film sensors immersed in electrolyte to perform multiple types of battery parameter measurements has been demonstrated. For example, a multiparameter sensor system measured multiple properties simultaneously inside of a pouch cell over a wide temperature range. Further, real time measurement of interior temperature changes in a battery pouch cell with an integrated interior temperature sensor was demonstrated. These changes include detecting a fault in the battery (shorting) in situ with rapid response time (less than a minute) corresponding to a more limited response by a temperature sensor mounted externally. Other aspects of monitoring battery health were also explored, such as real-time measurement of simulated dendrite growth/metal deposition by sensor on separator material demonstrated. Future efforts will include improvements in the durability of the sensor structure to allow introduction of the approach into standard battery fabrication techniques. Overall, this work is a step forward in providing a method to prevent catastrophic battery failures and provide a foundation for safer, lighter, and higher energy batteries for the electric aircraft industry.

thin film battery health↗

NASA NTRS2006

Data Mining for ISHM of Liquid Rocket Propulsion Status Update

This document consists of presentation slides that review the current status of data mining to support the work with the Integrated Systems Health Management (ISHM) for the systems associated with Liquid Rocket Propulsion. The aim of this project is to have test stand data from Rocketdyne to design algorithms that will aid in the early detection of impending failures during operation. These methods will be extended and improved for future platforms (i.e., CEV/CLV).

Ashok Srivastava↗

NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗

NASA NTRS1991

SHARP: Automated Monitoring of Spacecraft Health and Status

This paper briefly describes the spacecraft and ground systems monitoring process at the Jet Propulsion Laboratory and highlights some difficulties associated with the existing technology used in mission operations. A new automated system based on artificial intelligence technology is described which seeks to overcome many of these limitations. The system, called the Spacecraft Health Automated Reasoning Prototype (SHARP), is designed to automate health and status analysis for multi-mission spacecraft and ground data systems operations. The SHARP system has proved to be effective for detecting and analyzing potential spacecraft and ground systems problems by performing real-time analysis of spacecraft and ground data systems engineering telemetry. Telecommunications link analysis of the Voyager 2 spacecraft was the initial focus for evaluation of the system in a real-time operations setting during the Voyager spacecraft encounter with Neptune in August, 1989. The SHARP system will be delivered to the JPL Space Flight Operations Center for regular use by planetary flight projects, including the Galileo and Magellan spacecraft, and will also be applied to monitoring and control applications in the Deep Space Network's Network Operations Control Center.

David J Atkinson↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Thermal Modeling↗

NASA NTRS1991

Nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed at MSFC uses the Nickel Cadmium (NiCd) Battery Expert System (NICBES-2) which supports the evaluation of performance of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort is summarized which was used to modify NICBES-2 to accommodate Nickel Hydrogen (NiH2) battery environment now in MSFC testbed. The NICBES-2 is implemented on a Sun Microsystem and is written in SunOS C and Quintus Prolog. The system now operates in a multitasking environment. NICBES-2 spawns three processes: serial port process (SPP); data handler process (DHP); and the expert system process (ESP) in order to process the telemetry data and provide the status and action advice. NICBES-2 performs orbit data gathering, data evaluation, alarm diagnosis and action advice and status and history display functions. The adaptation of NICBES-2 to work with NiH2 battery environment required modification to all of the three component processes.

Shiva, Sajjan G.↗

NASA NTRSDate not supplied

Enabling Mission Flexibility to Battery Driven Deep Space Endeavors With Generalized Battery-Health-Monitoring Using Physics-Based and Data-Driven Reduced-Order Models

The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.

Automation↗

NASA NTRSDate not supplied

Numerical Investigation of Heat Transfer and Fluid Flow within Electrochemical Hydrogen Peroxide Generation Unit

Long-term manned space missions require the onboard production of disinfectants essential for maintaining crew health and supporting life systems. Currently, disinfection aboard the International Space Station (ISS) relies on disposable wetted wipes, which are regularly resupplied from Earth. This approach imposes a significant burden on resupply logistics, storage, and waste management. To address these challenges and support future missions, efforts are underway to develop an in-situ solution that electrochemically generates hydrogen peroxide disinfectant using onboard resources. In collaboration with NASA, Faraday Technology, Inc. has advanced this concept through a series of Small Business Innovation Research (SBIR) projects, resulting in the development of a Peroxide Generation Unit (PGU). The PGU can produce up to 3 wt.% hydrogen peroxide on-demand at a rate of 1 liter per day, providing a sustainable alternative to Earth-dependent supplies. The resulting aqueous hydrogen peroxide (H₂O₂) is an effective disinfectant, safe for crew use, compatible with spacecraft systems, and free from volatiles, off-gassing, or residues. This innovation offers a reliable, efficient solution for onboard disinfection, reducing dependence on Earth-based resupply while ensuring the health and safety of space crews. Generating hydrogen peroxide at the required rate needs high voltages and currents, exceeding 20V and 2A respectively, which leads to significant heat generation from Joule heating. This temperature rise poses a risk to sensitive system components, especially critical and expensive membranes that can degrade under thermal stress. To mitigate this risk, the thermal, fluid, and electrical flows within the system are modeled computationally using the commercial software COMSOL. The numerical simulations are validated against experimental data from both sub-scale and alpha-scale systems. Once verified, the model is employed to identify thermal hotspots, investigate their underlying causes, and explore solutions to prevent them.

Life Support Systems (LSS)↗