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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

The chemical properties of oils are vital in the design of microemulsion systems. The hydrophilic–lipophilic difference equation used to predict microemulsions’ phase behavior expresses the oils’ physiochemical properties as the equivalent alkane carbon number (EACN). The experimental determination of EACN requires knowledge of the temperature dependence of the microemulsion system and the effects of different surfactant concentrations. Thus, the experimental determination is time-intensive and tedious, requiring days to months for proper separations. Furthermore, the experiments require high purity of chemicals because microemulsions are sensitive to impurities. Our work focuses on the quick and reliable predictions of the EACN with machine learning (ML) models. Due to the immaturity of ML chemical predictions, we compare three graph neural networks (GNNs) and a gradient-boosted tree algorithm, known as XGBoost. The GNNs use the molecular structures represented as simplified molecular-input line-entry system (SMILES) codes for the initial input, which allows us to assess whether geometry optimization is necessary for reliable results. The XGBoost model also begins with the SMILES representations of the molecules but uses molecular descriptors instead of geometry optimizations. As a result, the best model tested (crystal graph convolutional neural network with Merck molecular force field-94) has an error of 1.15 EACN units of the true EACN for unknown data with the errors skewed toward zero and an R² score of 0.9

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗
DOE OSTI2025

Governance and resilience as entry points for transforming food systems in the countdown to 2030

Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change.

Schneider, Kate R. [Johns Hopkins Univ., Washingto↗
DOE OSTI2025

Automated Inventory Solutions in End-User IT Support

Managing IT equipment by hand is prone to errors and delays, severely impacting operational continuity and productivity. Manual inventory systems often result in time delays, inconsistent record-keeping, equipment shortages, and increased workloads for IT staff. At Savannah River National Laboratory (SRNL), my internship focused on creating an automated inventory management solution using Microsoft Power Automate and SharePoint Lists. This solution seamlessly integrates with the existing Microsoft 365 infrastructure, thus eliminating the need for additional software purchases or dedicated server space. By providing real-time updates and reducing manual data entry, the new system ensures a more reliable and maintainable approach to IT asset management.

Information Technology↗
DOE OSTI2024

Quality Ranking of Unary Chloride Salt Property Data Included in MSTDB-TP

Molten salt reactor developers rely on thermal property data to design, license and operate the reactors. The Molten Salt Thermal Database-Thermophysical Properties (MSTDB-TP) was established under the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program and is managed by Oak Ridge National Laboratory to serve as a single source of thermophysical property values measured for a wide variety of molten salt systems for use by researchers, molten salt reactor developers, and regulators. These properties include density, viscosity and thermal diffusivity and conductivity. Published measurements of molten salt properties are lacking for many salts of interest and the data that are available are often inconsistent. This creates a challenge for MSR developers when determining which property values to use when designing their reactors. It is the purpose of this work to apply a consistent ranking system to all data entries that indicates the quality of property values listed in the database. These rankings will be the technical basis for down-selections by the database developers and alert users about the quality of the available property values. MSTDB-TP collects all available property data and indicates preferred data sets or correlations. However, all available data sets are included in the database. Quality assessments and rankings are being applied to data in MSTDB-TP to provide an indication of the quality of each data set independent of consistency with other data. Previous reports detailed the ranking system that was followed and assessments of unary fluoride data sets. Documentation of the quality of data in MSTDB-TP was continued by reviewing and assessing all available sources of density, viscosity and thermal diffusivity or conductivity values for unary chloride salts in MSTDB-TP V3.0 using the same criteria.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Ultralow-temperature cryogenic transmission electron microscopy using a new helium flow cryostat stage

Advances in cryogenic electron microscopy have opened new avenues for probing quantum phenomena in correlated materials. This study reports the installation and performance of a new side-entry condenZero cryogenic cooling system for JEOL (Scanning) Transmission Electron Microscopes (S/TEM), utilizing compressed liquid helium (LHe) and designed for imaging and spectroscopy at ultra-low temperatures. The system includes an external dewar mounted on a vibration-damping stage and a pressurized, low-noise helium transfer line with a remotely controllable needle valve, ensuring stable and efficient LHe flow with minimal thermal and mechanical noise. Performance evaluation demonstrates a stable base temperature of 4.37 K measured using a Cernox bare chip sensor on the holder with temperature fluctuations within ±0.004 K. Complementary in-situ electron energy-loss spectroscopy (EELS) via aluminum bulk plasmon analysis was used to measure the local specimen temperature and validate cryogenic operation during experiments. The integration of cryogenic cooling with other microscopy techniques, including electron diffraction and Lorentz TEM, was demonstrated by resolving charge density wave (CDW) transitions in NbSe2 using electron diffraction, and imaging nanometric magnetic skyrmions in MnSi via Lorentz TEM. In conclusion, this platform provides reliable cryogenic operation below 7 K, establishing a low-drift route for direct visualization of electronic and magnetic phase transformations in quantum materials.

Charge density wave↗
DOE OSTI2025

A Novel Protection Scheme for Unbalanced Faults in Inverter Dominated Networks: A Computationally Efficient Algorithm for Entry-Level Relays

Microgrids are now a common practice in distribution systems to increase resilience and reliability. However, microgrid protection remains a critical challenge, considering its requirement to operate in both grid connected and islanded, and the variability in fault characteristics under each mode of operation. This paper presents unbalanced power (S unb ) based fault detection algorithm, which considers local voltage and current unbalances to determine faults in the system. S unb is a computationally efficient fault detection algorithm that is suitable for implementation in the programmable logic of entry level protective relays. In addition, the difference in current and voltage unbalance (D n ) is used to determine the fault type. The proposed method demonstrates high sensitivity and selectivity for line-to-ground (LG), line-to-line (LL), and double line-to-ground (LLG) faults, representing the most common faults in distribution systems. It also allows relay coordination with upstream and downstream protection devices in both island and grid connected operation, while preserving grading margins. The same pickup and time multiplier settings of a particular relay for both modes of operation eliminates the need for adaptive settings, which rely on communication networks. Validation was performed with a hardware-in-the-loop (HIL) setup using Typhoon HIL real time simulator interfaced with three entry-level, SEL 751 relays. Results confirmed the algorithm’s ability to discriminate fault conditions, and determine the fault type under both operating modes, maintain fast detection times, and ensure proper protection coordination.

fault classification↗
DOE OSTI2025

WorkJournalMaker (WJMaker) v0.5

The software generates and maintains daily work journal entries in text format, via a web browser. The journal entries are saved in a structured directory file tree on the system running the software. The software also incorporates a database so that it can track the location of files in the file system and various other metadata. The software allows the users to access their journal entries either through the browser or as discrete text files, facilitating sharing and open science. Additionally, to assist with the yearly PMP process, this tool connects to LLM APIs to provide summarization of the journal entries on a month-by-month or weekly basis. The advantage over similar technologies such as Apple Notes (extremely popular for notetaking) is that the instant software does not force the user to stay inside the Apple ecosystem, since it allows for export of the user's text files. This facilitates open science, so that researchers who use the tool can easily transfer their research notes to any other system. The WebJournalMaker repository is here: https://github.com/lbnl-science-it/WorkJournalMaker The WebJournalMaker repository is forked from the JournalSummarizer: https://github.com/tyfong-lbl/JournalSummarizer and builds on its code. I wrote the code for both of these software repos, using generative AI.

Fong, Timothy [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Investigate the Security of Electric Vehicle (EV) Ecosystem Applications

Apps that run on mobile devices are one of critical components of the electric vehicle (EV) ecosystem and pose possible threat actor points of entry that may impact the trust and security of EV charging systems in the future. Mobile apps often rely on communication between cloud servers and users, thereby creating potential points of entry for cyberattacks. Although app stores such as Apple App Store or Google Play Store generally test the security of apps, the cyber aspects may not be sufficient for many entities including DOD, federal fleets, and commercial entities. A more thorough inspection and the ability to influence developers is imminently needed. This research studies security attributes and vulnerabilities of a sample of mobile applications that support key user functions in the EV ecosystem. The study shows that all analyzed apps have security risks, categorized as either high or medium or both and a comprehensive cybersecurity guideline for developing mobile apps is necessary.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Systems Innovation: Modernization & Efficiencies for ESH&Q Reviews

Environmental compliance reviews at INL have traditionally been managed through fragmented systems, relying on multiple spreadsheets and manual processes. This inefficiency led to time-consuming status updates and redundant tasks, such as manually sending reminder emails and transferring data from Excel to the Environmental Review Process (ERP). Initial attempts to streamline these processes using Power Automate and Excel revealed significant limitations, necessitating a more comprehensive solution. To address these immediate inefficiencies, automated workflows were developed using Power Automate. These workflows were designed to send scheduled status update reminders and capture responses through standardized forms, with submitted data flowing directly into centralized Excel trackers. This automation reduced the administrative burden, improved data accuracy, and enabled faster, more consistent reporting. Specifically, email automation achieved a 65% efficiency gain, while data integration saw a 48% improvement, resulting in 91% of project statuses being updated within two months. Despite the improvements brought by Power Automate, the fragmented nature of the review processes persisted. To further enhance efficiency and accuracy, the Integrated Review Tool (IRT) was developed. The IRT aims to centralize review initiation and connect team systems, creating an interconnected data infrastructure that preserves team autonomy while enhancing overall efficiency. This tool automates email reminders, centralizes reviews, and streamlines data integration, significantly improving the accuracy and efficiency of environmental compliance reviews. The design and development of the IRT involved advanced systems methodology, process mapping, project management, and collaboration with subject matter experts. The minimum viable product design is 100% complete, and system development is currently underway, with expected outcomes including a centralized entry point for all ESH&Q reviews, automated routing, real-time tracking and analytics, AI integration, and a user-friendly interface. This project demonstrates the potential of leveraging automation and integrated systems to enhance efficiency, accuracy, and decision-making in environmental reporting and compliance processes at INL.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications

A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.

42 ENGINEERING↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
DOE OSTI2024

An affordable platform for automated synthesis and electrochemical characterization

In recent years, self-driving laboratories (SDLs) have emerged as a powerful tool to expedite various areas of chemical research. For optimal functionality, these laboratories must be adaptable, readily modifying configurations to meet researchers' specific needs. Despite these advances, much of chemistry still depends on proprietary equipment from specialized vendors, which can be restrictive and difficult to customize for diverse lab setups. Moreover, ensuring reproducibility requires full disclosure of equipment details. In this work, we introduce an automated system featuring a cost-effective, self-designed potentiostat and a straightforward synthesis platform. We provide complete transparency by disclosing the electronic schematics of the potentiostat and the software used in the system. Our aim is to reduce the barriers to entry for SDLs and promote the principles of open science.

Pablo-García, Sergio↗
DOE OSTI2026

LBNF/DUNE Liquid Argon Roadmap

The Deep Underground Neutrino Experiment (DUNE) is an international flagship venture to unlock the mysteries of neutrinos. Hosted at the Sanford Underground Research Facility (SURF) and supported by the Long-Baseline Neutrino Facility (LBNF), DUNE relies on nearly 75,000 tonnes of ultrapure liquid argon (LAr) housed in state-of-the-art cryostats. Transporting such vast quantities of LAr to its destination a mile underground in Lead, SD, is a logistical and engineering challenge. The process begins with securing large quantities of liquid argon from suppliers located far from the site, with major sources situated near Houston, TX and Chicago, IL. The receiving facility, located atop a mountain with steep, often snow-covered access roads, provides limited maneuverability for trucks and can only handle two deliveries simultaneously. The facility serves as an entry point for argon to the greater LBNF cryogenic system. It is furnished with truck unloading stations, limited buffer storage of some 280 tonnes capacity, and vaporizers. The last are crucial for converting liquid argon into gas for transfer down the Ross Shaft, eliminating the need for cryogens in the vertical pipeline. These constraints, along with other operational factors, cap the delivery rate at 70 tonnes per day. Once underground, the argon is purified and recondensed before filling the cryostats. This roadmap outlines the integrated supply chain and cryogenic systems that enable the delivery of LAr to support DUNE’s groundbreaking physics research.

Maciazka, Matt [Fermilab]↗
DOE OSTI2025

Characterization of Infrasonic Signatures of Earth-Grazing Fireballs as Analogues to Hypersonic Vehicles (Final Report)

Accurate detection, discrimination, and characterization of high-altitude hypersonic events using infrasonic monitoring are critical to planetary defense and global strategic surveillance. This report synthesizes recent advances achieved through rigorous analysis of infrasonic signatures from natural meteoroids, emphasizing shallow entry-angle meteoroids as essentially proxies for artificial hypersonic systems. Meteoroids naturally encompass diverse velocities, trajectories, altitudes, and fragmentation behaviors, enabling systematic validation of empirical period–yield relationships, waveform morphology classifiers, and trajectory-induced back-azimuth deviation models. Integration of adaptive array-processing enhancements within Cardinal software further extends infrasonic detection sensitivity and signal classification reliability. Collectively these advances, based solely on infrasonic signatures or limited optical data, offer robust methodologies for distinguishing natural from artificial hypersonic sources, significantly reducing event geolocation uncertainties and refining source-function determination. The outcomes detailed herein lay foundational groundwork for improved global hypersonic event-surveillance frameworks, supporting improved security preparedness and informing strategic monitoring and defense policies.

54 ENVIRONMENTAL SCIENCES↗