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NASA NTRS2015

Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform.

Robotics↗

DOE OSTI2026

Infrared triggered dwell and active cooling thermal control effects on microstructural uniformity in DED

Directed Energy Deposition (DED) offers rapid large scale fabrication, but difficulty in delivering consistent microstructures and properties hinders the use of DED fabricated components in safety or performance critical applications. Variability stems from the complex thermal cycles generated by the toolpath used to print the required geometry. Several practical methods have become established in DED to regulate overheating, such as active cooling of the baseplate structure or the use of an infrared camera to inject interlayer pauses to ensure the top layer of the component cools to a set temperature, which have been shown to affect microstructure. However, no critical assessment has been performed as to how effective these controls are in promoting microstructural uniformity in the context of complex layer timing commonly generated by non-prismatic geometries. Here we show how controls influence the thermal field, phase transformations, and dynamic annealing of a low-temperature transformation steel using infrared imaging and operando neutron diffraction. Counterintuitively, common thermal homogenization process controls can reduce microstructural uniformity because these approaches stabilize peak temperature while overlooking temperatures near the solid-state phase transformation fronts. Instead, the cyclic reheating induces spatially-variant dynamically annealed regions which can be modulated via control parameters. We show that these controls have spatially linked effects centimeters away from the active weld, which implies that microstructure control must co-optimize thermal input across many subsequent layers. In conclusion, our results demonstrate the pressing need for higher order controls that integrate predictive elements of simulation data to stabilize printed properties for future qualification of DED components.

Directed energy deposition↗

NASA NTRS2003

Mechanical Properties of Triaxial Braided Carbon/Epoxy Composites

In an on-going effort to increase the safety and efficiency of turbine engines, the National Aeronautics and Space Administration is exploring lightweight alternatives to the metal containment structures that currently encase commercial jet engines. Epoxy reinforced with braided carbon fibers is a candidate structural material which may be suitable for an engine case. This paper reports flat-coupon mechanical-property experiments performed to compliment previously reported subcomponent impact testing and analytical simulation of containment structures. Triaxial-braid T700/5208 epoxy and triaxial-braid T700h436 toughened epoxy composites were evaluated. Also, two triaxial-braid architectures (0 degrees plus or minus 60 degrees, and 0 degrees plus or minus 45 degrees) with the M36 resin were evaluated through tension, compression, and shear testing. Tensile behavior was compared between standard straight-sided specimens (ASTM D3039) and bow-tie specimens. Both double-notch shear (ASTM D3846) and Iosepescu (ASTM D5379) tests were performed as well. The M36/O degrees plus or minus 45 degrees configuration yield the best response when measurements were made parallel to the axial tows. Conversely, the M36/0 degrees plus or minus 60 degrees configuration was best when measurements were made perpendicular to the axial tows. The results were used to identify critical properties and to augment the analysis of impact experiments.

Bowman, C. L.↗

NASA NTRS2003

Mechanical Properties of Triaxial Braided Carbon/Epoxy Composites

In an on-going effort to increase the safety and efficiency of turbine engines, the National Aeronautics and Space Administration is exploring lightweight alternatives to the metal containment structures that currently encase commercial jet engines. Epoxy reinforced with braided carbon fibers is a candidate structural material which may be suitable for an engine case. This paper reports flat-coupon mechanical-property experiments performed to compliment previously reported subcomponent impact testing and analytical simulation of containment structures. Triaxial-braid T700/5208 epoxy and triaxial-braid T700/M36 toughened epoxy composites were evaluated. Also, two triaxial-braid architectures (0 +/- 60 deg., 0 +/- 45 deg.) with the M36 resin were evaluated through tension, compression, and shear testing. Tensile behavior was compared between standard straight-sided specimens (ASTM D3039) and bowtie specimens. Both double-notch shear (ASTM D3846) and Iosepescu (ASTM D5379) tests were performed as well. The M36/0 +/- 45 deg. configuration yield the best response when measurements were made parallel to the axial tows. Conversely, the M36/0 +/- 60 deg. configuration was best when measurements were made perpendicular to the axial tows. The results were used to identify critical properties and to augment the analysis of impact experiments.

Bowman, C. L.↗

DOE OSTI2024

"Source Term Modeling for Advanced Gas Micro-Reactors"

Maintaining the safety of the public, environment, and operating personnel is the most important factor in designing, operating, maintaining, and decommissioning nuclear reactors. In recent years, there has been a growing interest in the development of micro-reactors employing TRi-structural ISOtropic (TRISO)-coated particle fuel. In gas reactors, TRISO fuel plays an important role in the safety case for high temperature reactors because of the fission product retention properties of the fuel. This ability enables the use of a functional containment strategy for the reactor where multiple barriers are used to prevent fission product release to the environment. Part of the safety analysis of these advanced reactors is the assessment of radionuclide releases under normal and accident conditions through the multiple credited safety barriers. Using conservative assumptions, a mechanistic analysis can be performed to quantify these releases that combines the probabilistic assessment of failure with analytic solutions to radionuclide transport equations. Source term modeling for TRISO fuel has been performed for previous reactor designs; however, these models are outdated, in many cases proprietary, and need updates to be applied to the current state of TRISO fuel technology and alternative gas reactor core configurations [1]. Currently, the only publicly available source term assessment for gas reactors is an expert-based Monte Carlo simulation based on the effectiveness of the fuel kernel, coating layers, and graphite block in a modular high temperature gas reactor [2]. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that could be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. The release is calculated by the diffusion of the key safety important fission products through the kernel, silicon carbide (SiC), graphite for both intact and defective TRISO particles based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system to remove fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. The model then can calculate the fission product release for any transient temperature profile and fission product releases can then be used to assess radiological dose to the workers and the public using conventional dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy (DOE) Advanced Gas Reactor (AGR) TRISO fuel development program. The model is coded in python with inputs and outputs in excel spreadsheets, as well as python plotting utilities to aid in the interpretation of the results. References: [1] INL, NGNP Mechanistic Source Term White Paper, INL-10-17997, July 2010. [2] David A. Petti, Richard R. Hobbins, Peter Lowry, Hans Gougar, “Representative Source Terms and The Influence of Reactor Attributes on Functional Containment in Modular High Temperature Gas-cooled Reactors,” Nuclear Technology, Vol. 184, p. 181-197, Nov. 2013.

07 ISOTOPE AND RADIATION SOURCES↗

NASA NTRSDate not supplied

Solid-state Architecture Batteries for Enhanced Rechargeability and Safety for Electric Aircraft

All electric vertical take-off and landing vehicles (eVTOL) for urban air mobility (UAM) concepts face numerous challenging technical barriers before their introduction into the consumer marketplace. The most challenging of these technical barriers to overcome is developing an energy storage system capable of meeting the rigorous aerospace safety and performance criteria. The performance metrics for eVTOL craft are at least 2 times greater than those of electric automobiles. Furthermore, safety is essential for operation of commercial electric aerovehicles. Preliminary systems level analysis has indicated that there are five key properties which must be optimized for successful implementation of battery systems. Those five key criteria are: safety, energy density, power, packaging design and scalability. Current state-of-the-art (SOA) lithium-ion batteries meet or exceed the requirements for electric aviation in the areas of power and scalability, yet are insufficient in the key performance criteria of energy, safety and packaging design. The SABERS concept proposes a battery that meets all five key performance criteria through development of a solid-state architecture battery utilizing high energy density and power density sulfur-selenium cathode with a lithium metal anode. The combination of sulfur and selenium offers a balanced energy-to-power density ratio, which can be tailored to the specific application by altering the stoichiometric ratios of sulfur to selenium. This cathode will be developed by implementing NASA patented holey graphene technology as a highly conductive, ultra-lightweight electrode scaffold. A solid-state electrolyte will be used as a safe, non-flammable replacement to the highly flammable liquid organic electrolytes currently used in SOA lithium-ion batteries. This solid-state lithium-sulfur-selenium cell will be designed into a serial stacking configuration to enable dense packaging of the battery cells. The serial stacking configuration is termed a bipolar stack, which has the advantages of reducing overall cell weight, reducing the amount of interfaced connections for the cell, and minimizing the cooling requirements for the cell. Lastly, optimization of battery components will occur through a robust and rigorous combination of various computational modeling techniques covering multiple length scales. The expected result will be a fully solid-state battery with operational temperatures from 0 °C to 150 °C which provides the required energy density, discharge rates, and inherent safety to meet the strict aerospace performance criteria. This presentation will show initial results that demonstrate the SABERS Team has developed a composite carbon-sulfur cathode which exceeds 1100 Wh/kg at a discharge rate of 0.4C, and 804 Wh/kg at a discharge rate of 1C. Additionally, this presentation will show the SABERS Team multiscale computational modeling approach and has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM is on the 1-10 µm scale and provides electromechanical and grain interactions for predictive design guidelines for the experimental team to follow.

Urban Air Mobility (UAM) Vehicles↗

NASA NTRSDate not supplied

Computational Modeling Development of Solid-state Architecture Batteries for Enhanced Rechargeability and Safety for Electric Aircraft

Urban Air Mobility (UAM) Vehicles↗

DOE OSTI2020

Nanocomposite Ionogel Electrolytes for Solid‐State Rechargeable Batteries

Abstract Ionogels composed of ionic liquids and gelling solid matrices offer several advantages as solid‐state electrolytes for rechargeable batteries, including safety under diverse operating conditions, favorable electrochemical and thermal properties, and wide processing compatibility. Among gelling solid matrices, nanoscale materials have shown particular promise due to their ability to concurrently enhance ionogel mechanical properties, thermal stability, ionic conductivity, and electrochemical stability. These beneficial attributes suggest that ionogel electrolytes are not only of interest for incumbent lithium‐ion batteries but also for next‐generation rechargeable battery technologies. Herein, recent advances in nanocomposite ionogel electrolytes are discussed to highlight their advantages as solid‐state electrolytes for rechargeable batteries. By exploring a range of different nanoscale gelling solid matrices, relationships between nanoscale material structure and ionogel properties are developed. Furthermore, key research challenges are delineated to help guide and accelerate the incorporation of nanocomposite ionogel electrolytes in high‐performance solid‐state rechargeable batteries.

Hyun, Woo Jin↗

DOE OSTI2022

In situ infrared nanospectroscopy of the local processes at the Li/polymer electrolyte interface

Solid-state batteries possess the potential to significantly impact energy storage industries by enabling diverse benefits, such as increased safety and energy density. However, challenges persist with physicochemical properties and processes at electrode/electrolyte interfaces. Thus, there is great need to characterize such interfaces in situ, and unveil scientific understanding that catalyzes engineering solutions. To address this, we conduct multiscale in situ microscopies (optical, atomic force, and infrared near-field) and Fourier transform infrared spectroscopies (near-field nanospectroscopy and attenuated total reflection) of intact and electrochemically operational graphene/solid polymer electrolyte interfaces. We find nanoscale structural and chemical heterogeneities intrinsic to the solid polymer electrolyte initiate a cascade of additional interfacial nanoscale heterogeneities during Li plating and stripping; including Li-ion conductivity, electrolyte decomposition, and interphase formation. Moreover, our methodology to nondestructively characterize buried interfaces and interphases in their native environment with nanoscale resolution is readily adaptable to a number of other electrochemical systems and battery chemistries.

42 ENGINEERING↗

DOE OSTI2021

A Risk-Informed Performance-Based Methodology to Manage Fire Protection Systems in Nuclear Facilities

Fire protection systems (FPSs) and features are installed in U.S. Department of Energy (DOE) Hazard Category 1, 2, or 3 nuclear facilities to protect property (maximum possible fire loss thresholds), life, and nuclear safety (i.e., structures, systems, and components). These FPSs and features are designed and maintained in accordance with the prescriptive guidance provided in applicable building codes and National Fire Protection Association codes and standards. Management, operations, and maintenance activities of FPSs involve significant effort. A DOE facility’s documented safety analysis or other safety basis document could also rely on FPSs to provide either a safety significant or safety class function to mitigate fire hazards and minimize radiological consequences. In some cases, the designation of safety significant or safety class may be determined to provide a layer of defense-in-depth to minimize nuclear safety risks independent of the fire risk. DOE standards allow the use of performance-based design alternatives developed by the fire industry but do not consider the defense-in-depth layers of protection provided in DOE facilities to prevent or mitigate the risks associated with unintended release of radioactive materials into the environment. Pacific Northwest National Laboratory developed a decision-making methodology tailored for DOE non-reactor nuclear facilities to manage FPSs and features by integrating nuclear safety risk insights into a performance-based analysis. This risk-informed, performance-based (RIPB) methodology can be used to provide the technical basis for classifying an FPS as safety class and safety significant, tailoring administrative controls (e.g., technical safety requirements), and ranking the importance of FPSs to prioritize maintenance, upgrades, and replacement activities. The RIPB methodology is a graded approach to inform DOE facility owners and Fire Protection Program managers of the most risk-significant FPSs and equipment, and those systems would be cost-beneficial to relax rigor if there is a need to re-design the FPS coverage or deviate from DOE and National Fire Protection Association standards for those systems that would be less significant. This paper describes the framework used to develop the RIPB methodology and the outcome of implementing this methodology in a use-case nuclear facility. This paper also discusses the impact to DOE policies and standards and the safety margins and defense-in-depth measures credited in nuclear safety assessments in a facility’s documented safety analysis and the benefits of implementing an RIPB methodology in lieu of a prescriptive method to comply with fire protection requirements.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2021

Represent precipitation-induced geological hazards in Earth system models using artificial intelligence

Precipitation-induced geological hazards, such as debris flow, landslides, mudflow and rockfalls (hereafter referred to as landslides), pose serious threats to public safety in many areas through the world. As residential properties and infrastructure in the US have increasingly expanded into landslide-prone areas and the drivers of landslides (e.g., wildfires and hurricanes) are predicted to intensify under climate warming, losses and fatalities from landslides are likely to increase in the future. Although our understanding of geoenvironmental factors and mechanisms contributing to landslides has greatly improved, only moderate progress has been made in predicting landslides out of well-studied watersheds. Furthermore, explicitly representing various landslide-related processes in Earth system models (ESMs), from the buckling of local bearing elements in granular materials, to frictional sliding between grains, formation of microcracks in the soil matrix, rupture of capillary bridges, or breakage of plant roots3 is still very unlikely within the next decade, even with the help of exascale computers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗

NASA NTRS1979

Inexpensive cross-linked polymeric separators made from water soluble polymers

Polyvinyl alcohol (PVA) crosslinked chemically with aldehyde reagents produces membranes which demonstrate oxidation resistance, dimensional stability, low ionic resistivity, low zincate diffusivity, and low zinc dendrite penetration rate which make them suitable for use as alkaline battery separators. They are intrinsically low in cost and environmental health and safety problems associated with commercial production appear minimal. Preparation, property measurements, and cell test results in Ni/Zn and Ag/Zn cells are described and discussed.

Hsu, L. C.↗

NASA NTRS1982

Inexpensive cross-linked polymeric separators made from water-soluble polymers

Polyvinyl alcohol (PVA), cross-linked chemically with aldehyde reagents, produces membranes which demonstrate oxidation resistance, dimensional stability, low ionic resistivity (less than 0.8 Ohms sq cm), low zincate diffusivity (less than 1 x 10 to the -7th mols/sq cm per min), and low zinc dendrite penetration rate (greater than 350 min) which make them suitable for use as alkaline battery separators. They are intrinsically low in cost, and environmental health and safety problems associated with commercial production appear minimal. Preparation, property measurements, and cell test results in Ni/Zn and Ag/Zn cells are described and discussed.

Hsu, L.-C.↗

NASA NTRS1989

Weld stresses beyond elastic limit: Materials discontinuity

When welded structures depend on properties beyond the elastic limit to qualify their ultimate safety factor, and weld-parent materials abruptly change at the interface, then stress discontinuity is inevitable. The stress concentration is mildly sensitive to material relative strain hardening and acutely sensitive to applied stress fields. Peak stresses occur on the weld surface, at the interface, and dissipate within a 0.01-inch band. When the stress is intense, the weld will always fracture at the interface. The analysis incorporates a classical mechanics model to more sharply define stress spikes within the bandwidth, and suggests a relative material index and Poisson's ratio related to strain hardening. Implications are discussed which are applicable to industries of high performance structures.

Verderaime, V.↗

NASA NTRS2002

Radiation Exposure Effects and Shielding Analysis of Carbon Nanotube Materials

Carbon nanotube materials promise to be the basis for a variety of emerging technologies with aerospace applications. Potential applications to human space flight include spacecraft shielding, hydrogen storage, structures and fixtures and nano-electronics. Appropriate risk analysis on the properties of nanotube materials is essential for future mission safety. Along with other environmental hazards, materials used in space flight encounter a hostile radiation environment for all mission profiles, from low earth orbit to interplanetary space.

Wilkins, Richard↗

NASA NTRS2023

Comparison of Liquefaction Testing with Liquid Nitrogen and Liquid Oxygen

Liquid nitrogen is often used as a substitute for oxygen testing due to safety concerns, and the general similarity between their fluid properties. During the Cryogenic Fluid In-situ Liquefaction for Landers (CryoFILL) testing, an opportunity arose to compare liquid nitrogen and liquid oxygen behavior using the same test hardware in similar test conditions. Comparative testing would verify whether the system response to nitrogen and oxygen would behave in a similar manner. Tests that were investigated include system boil-off heat load determination, autogenous pressurization, cryocooler loop initiation, and cryocooler loop operations. Results from testing are shown and compared to verify that liquid nitrogen and liquid oxygen tests yield similar system responses, and that nitrogen can be used as a substitute for oxygen in developmental tests at cryogenic temperatures.

Cryogenic Fluid Management↗

DOE OSTI2026

Understanding Isomeric Effects on Properties of Aviation Fuels via a Group Contribution Method: Preprint

The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.The molecular composition of aviation fuels, including conventional and sustainable aviation fuels (SAFs), significantly influences their performance, safety, and environmental impact. This study examines the effect of isomeric variation for compounds with the same carbon number and chemical family on key fuel properties, focusing on compounds commonly found in conventional jet fuels and SAFs. A group contribution method (GCM) is employed to predict thermophysical and combustion properties, providing an efficient analytical approach to evaluate the contributions of individual compounds to overall fuel mixture behavior. As part of this work, we introduce FuelLib, an open-source Python tool built around the GCM, to calculate individual compound and fuel mixture properties using various mixing rules. Our work evaluates whether the GCM can capture isomeric effects, that are often overlooked in traditional fuel property estimation. This is particularly important for SAFs, which often are composed of a more limited set of compound classes than conventional fuels, making isomeric differences more critical. Two-dimensional gas chromatography (GCxGC) data, which can be obtained from small fuel samples, provides weight percentages of compounds grouped by chemical family and carbon number rather than detailed information about individual compounds. As a result, assumptions must be made when decomposing GCxGC data into functional groups for GCM applications. Using GCxGC data, we show that the FuelLib tool can be used to predict the fuel properties of conventional jet fuels, with validation against experimental data. The provided tool enables researchers to predict fuel properties of candidate fuels and supports the design of new SAFs at both the component and mixture levels. This capability provides a foundation for studying fuel and combustion properties during SAF development, reducing reliance on costly experimental methods and advancing progress toward certification of new SAFs.

33 ADVANCED PROPULSION SYSTEMS↗