Defense test and evaluation often explores complex systems through simulation modeling. These experiments are costly, time-consuming, or computationally intensive. Consequently, experiment designs must support limited executions while preserving broad coverage of multidimensional input spaces. In addition, applying space-filling experiment designs is complicated by optional and conditional inputs, such as in system-of-systems studies with optional subsystems and policies. These cases induce variable-dimension hierarchical design spaces with multiple feasible concept subspaces or distinct sets of nested factors that are conditionally relevant based on branching factors’ values. This research proposes and evaluates algorithms to design space-filling experiments that recognize the hierarchical relevance conditions in these design spaces. We extend space-filling metric computations for hierarchical design spaces and propose metrics to quantify distinct active branching factor combinations, concept subspace, and coverage. In numeric experiments, we evaluate the resulting designs and find that the proposed algorithms create designs with a better maximin design-point distance compared with traditional space-filling designs that ignore the conditional relevance of factors. In a system-of-systems case study, we execute the designs and assess the results for output space-filling properties, finding the proposed algorithms produce statistically significant improvements in output-space-filling metrics relative to random designs and traditional Latin hypercube designs.
Maintenance, repair, and overhaul are crucial in battlefield success. In this paper, we study additive manufacturing–enabled maintenance, repair, and overhaul at the battalion level. We build a discrete-event simulation model of a mechanized battalion maintenance, repair, and overhaul for decisive battles of 2 weeks. The model is based on our previous studies with three submodels: battle damage model, maintenance model, and inventory model. The maintenance system is supported by both metal and plastic additive manufacturing printers, which are used to replenish initial spare parts inventories. Discrete-event simulation modeling, with the best available input parameter values, enables testing combinations of variables for the battalion maintenance, repair, and overhaul performance. Results show that battle intensity remains a problem while initial inventories can be allocated optimally. Additive manufacturing is able to support maintenance, repair, and overhaul only under conditions of low battle intensity for 2-week battles. Under higher battle intensities, the technology is unable to respond quickly enough to the resulting surge in spare parts need. The results of the study enable planning and conducting additive manufacturing–supported maintenance, repair, and overhaul operations for military logistics.
This paper proposes a modeling and simulation framework for quantifying the radar detectability of the AGM-129, employed here as a benchmark low-observable platform for long-range surveillance assessment. A full-scale three-dimensional perfectly electrically conducting model was developed and analyzed using static and dynamic radar cross-section simulations against a ground-based L-band radar. In contrast to studies based on stealth assessments conflating radar-absorbing material effects with stealth, the work adopts a deliberately conservative perfectly electrically conducting-only representation to isolate the contributions of aerodynamic shaping and aspect-dependent scattering to radar visibility. The results show that the AGM-129 maintains low-observable behavior across the frontal and rear sectors and that, even without radar-absorbing material, the combination of stealth geometry and low-altitude flight can reduce the effective detection range by up to 60.9% relative to the nominal radar range. Dynamic simulations further reveal that dynamic radar cross-section significantly reshapes detection, producing scintillation and highlighting the inadequacy of nominal radar range as a reliable proxy for engagement performance against stealth-optimized cruise missiles. By linking validated electromagnetic modeling to detection analysis in an operational scenario, the proposed framework establishes a conservative, reproducible, and operationally relevant benchmark for future studies on radar-absorbing material integration, multipath exploitation, and adaptive routing in next-generation cruise-missile survivability analysis.
This article addresses the critical shortfall in preparedness for sustained high-intensity warfare among many NATO and EU states, with a particular focus on air and missile defence. It identifies not only technological gaps but also a strategic challenge in allocating limited resources to protect high-value assets under fiscal constraints. To inform decision-making, we develop a quantitative optimisation model that integrates probabilistic interception assessment with economic evaluation of ground-based air defence configurations. The model evaluates how alternative force structures influence both defensive effectiveness and cost efficiency, employing marginal analysis to demonstrate the diminishing returns of additional deployments. A scenario-based case study reflecting contemporary operational conditions illustrates the model’s practical application and validates its implications. Our findings indicate that effective air defence is shaped not only by technical capability but also by explicit strategic choices regarding risk tolerance, asset prioritisation, and long-term sustainment. By combining operational modelling with economic reasoning, the framework offers a structured tool for planners and policymakers to balance combat effectiveness with fiscal sustainability and resilience in the protection of critical assets.
Small unmanned aircraft systems (sUAS) are becoming increasingly prevalent in both military and asymmetric conflicts worldwide. Combining small signatures, with often low-altitude and low-speed flight patterns, they pose significant challenges to existing counter-measures. This work examines the engagement/neutralisation segment of counter-sUAS (C-sUAS) killchains by providing a survey of current effector technologies, including guided and unguided kinetic means, electronic warfare technologies, and directed-energy weapons. Furthermore, propagation models used for on-target effect deposition, together with some target vulnerability models, are introduced, offering a structured basis for the analysis of the effectors’ effectiveness and providing guidance for future C-sUAS developments. Finally, a conceptual overarching software architecture is outlined, demonstrating how the different C-sUAS effector aspects can be integrated in a flexible, scalable, and maintainable manner to facilitate further software development.
This research examines the problem of routing multiple assets of different types over a network to service demands, where the demands must be serviced by asset types in sequential order within a bounded amount of time, and minimizing the cumulative service time is of interest. Disrupting these decisions, an opponent seeks to identify effective network disruption strategies with limited resources to maximize the minimal cumulative service time. Within a bilevel programming structure for this Stackelberg game, the upper-level problem determines the disruption strategy, and the lower-level problem routes the assets. Seeking the identification of high-quality solutions with relatively low computational effort, this research identifies and tests three solution procedures: a greedy construction heuristic (GCH) that iteratively identifies each disruptive action, a customized implementation of simulated annealing (SA), and an enhanced variant thereof (eSA) that leverages a prioritized identification of candidate solutions along with a tabu list. Testing compares the solution methods on similar instances over a range of selected algorithmic and instance-specific parameters. Results showed the enhanced SA method performed best, and extended testing explored the effect of increasing selected problem sets on the relative improvement in eSA over GCH, as well as its effect on algorithmic runtimes.
Sustaining complex engineered systems over decades is not only a data-integration challenge, but a semantic one: upgrade decisions require coherent reasoning across configuration identity, degradation and failure behaviour, temporal evolution, and decision rationale-yet these dimensions are typically modelled in separate tools, with incompatible assumptions about "what a system is over time." Existing document-centric approaches and many model-based systems engineering/PLM implementations can store artefacts and trace links, but they do not provide the ontological constructs needed to infer, compare, and justify upgrade trajectories (e.g. how a sequence of baselines across epochs produces capability effects under constraints). This paper, therefore, analyses system upgradability as an ontological problem and derives the System Upgrade Ontology (SUO) as a federated semantic solution. SUO operationalises upgradability as a property of configuration trajectories (not isolated components), grounded in rigorous upper-ontology commitments and aligned to enterprise architecture meta-frameworks. It integrates (1) stable representations of system identity and configuration change, (2) imported maintenance/Prognostics and Health Management semantics for degradation and failure, (3) epoch-era temporal structures for long-horizon evolution, and (4) multi-criteria decision factors that make upgrade trade-offs explicit and auditable. A naval combat system case study demonstrates SUO's explanatory power by showing which upgrade implications are not expressible in operational logistics records alone, and how SUO enables repeatable reasoning over alternative upgrade paths and their multidimensional impacts across service eras.
Creating valid computer models of photonic infrared cameras is crucial for many simulation applications in both civilian and defence settings. We used the European Machine Vision Association (EMVA) standard 1288 following the photon transfer technique to characterise six photonic infrared cameras from Teledyne FLIR. The six cameras spanned the short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) wavebands. The performance parameters of these cameras were imported into the Unreal Engine simulation programme using "Infinite Studio" plugins to create six digital clones. Infinite Studio is a joint development programme between Aurizn and the Defence Science and Technology Group. We compared the simulated outputs with the measured results to validate Infinite Studio's camera model. Our comparison showed sub-percentage-level agreement for the short-wave and mid-wave infrared cameras and showed agreement to the few-per cent level for the long-wave cameras, proving the accuracy of the Infinite Studio suite of radiometric plugins for Unreal Engine.
This paper investigates end-of-life disposal for satellites operating in the southern butterfly orbit family. Previous literature explored transfer capabilities of the southern butterfly family for various missions relative to lunar exploration, but not for disposal. This paper fills this research gap and investigates the practicality of various transfer trajectories for disposal chosen from unstable manifolds propagated from a selected southern butterfly orbit. Using the Circular Restricted 3-Body Problem (CR3BP), a campaign of trajectory cases is investigated to evaluate the feasibility of different trajectory options for cislunar disposal. Seven from a total of 43 disposal routes are showcased, with disposal assessments and the factors contributing to these conclusions identified. Overall, 40 routes were found to be possible for cislunar disposal, with a further subset of 10 assessed as favorable for decommissioning satellites. This paper also explores the possibility of using manifold-based trajectories for cislunar disposal and targeting a Sun-Earth 1:1 resonance orbit as a graveyard orbit for satellites leaving a southern butterfly orbit. Analysis revealed that all satellite cases unable to reach the graveyard orbit due to insufficient propellant for the transfer maneuver will enter quasi-periodic orbits in the Sun-Earth system and eventually re-enter the Earth's gravitational sphere of influence (SOI).
The effort of armed forces worldwide to acquire high-tech equipment is becoming increasingly necessary with the ongoing technological advancement. Therefore, this work aims to technically evaluate the possibilities of the mechanical integration of a Skyfire rocket launcher from Avibras into a Remotely Piloted Aircraft System (RPAS), considering all the challenges of the process. First, an analysis of available aircraft in Brazil and around the world was conducted to select a system that had sufficient infrastructure for the launcher to be integrated. Next, three-dimensional (3D) modeling of the aircraft components and the weapon system was performed, so that it was possible to use this geometry in the simulations. In addition, several computational fluid dynamics (CFD) simulations were carried out in Ansys software to calculate the lift and drag coefficient parameters of the airflow over the aircraft wing, and graphs of the pressure and velocity contours on the wing were obtained. The simulations are crucial for comparing the lift and drag forces on the aircraft wing before and after the integration of the weapon system.
Recent advances in covert underwater acoustic communication have demonstrated the feasibility of embedding signals within marine mammal sounds, such as dolphin clicks and whale vocalizations. While most research focuses on developing concealment techniques, limited work addresses the detection of such covert embeddings. This study investigates the detection of covert messages hidden within authentic sperm whale (Physeter macrocephalus) vocalizations. Using audio from the Watkins Marine Mammal Sound Database, covert messages were embedded using short-duration, high-frequency chirps masked by natural whale clicks and codas. The chirps corresponded to bits using Baudot code to represent characters. Acoustic features, including high-frequency band energy and spectral variance, were extracted as two-dimensional feature vectors. A Siamese neural network was trained on 690 paired feature samples to classify authentic versus embedded sperm whale audio. The model achieved an accuracy of 97.10% with an F1 score of 96.22%. The results highlight the vulnerabilities of marine acoustic environments and contribute to securing underwater communication environments from adversarial acoustic masking.
Digital twins (DTs) have emerged as a transformative technology for modeling and simulation in various industries, including defense. This paper provides a comprehensive review of DT applications in defense modeling and simulation, focusing on how DTs can enhance simulation fidelity, interoperability, and decision support within defense systems. We consolidate existing research into a unified framework that links DT concepts, simulation-driven applications, and real-world deployments in defense scenarios. We discuss the role of the DT in applications like planning, training, execution, monitoring, and debriefing. We introduce a standardized DT characterization framework suitable for defense applications that aligns with industrial modeling and simulation standards and present a taxonomy of defense-specific use cases, highlighting recurring requirements. In addition, practical evidence is provided from a targeted questionnaire distributed to defense stakeholders and the ministries of defense (MoDs), revealing current challenges in DT integration and deployment. Finally, we conclude by identifying key gaps in DTs applications for defense modeling and simulation, including interoperability, security, and system integration, and we outline future research directions and development opportunities. This review aims to inform defense modeling and simulation practitioners and researchers, guiding future work on DT design, implementation, and deployment across defense applications.
This study analyzes the long-term structure of the South Korean Army's non-commissioned officer (NCO) personnel using a stochastic modeling approach. Persistently low recruitment rates and rising discharge rates pose challenges to maintaining the NCO force, and with an impending demographic cliff expected after 2035, examining the personnel structure has become increasingly valuable. Using 2016-2020 data, a Discrete-Time Markov Chain (DTMC) model was developed to estimate steady-state rank distributions. To address limitations in the DTMC's applicability to real-world conditions, a simulation model was constructed incorporating minimum service periods and mandatory retirement ages. Comparative analysis reveals that mid- and senior-level ranks are disproportionately affected by declining NCO intake, and that targeted improvements in Sergeant First Class retention yield the most significant impact on overall force size. These findings suggest that strategic personnel planning should prioritize mid-level NCOs to sustain operational readiness. The study offers a pioneering quantitative framework for evaluating future personnel policies under demographic and structural constraints.
Wargaming is a key component of military strategic decision-making, providing a means to explore human decision-making processes. However, the military frequently depends on scarce wargaming expertise to ensure the quality of wargames. In this context, we examine the potential of large language models (LLMs) to produce useful texts that support wargaming activities. Contrary to previous studies unfavourably juxtaposing LLMs as decision-makers to their human counterparts, we focused on the potential use of these models for generating wargaming components. We conducted a study with wargaming experts to compare the effectiveness of human-created texts and LLM-generated texts across various tasks within the wargaming lifecycle. For all wargaming tasks for which the LLM-generated texts could be compared to the human-created texts, the LLM was able to match or even surpass human-level quality. This demonstrates that despite the dangers of LLMs when in the driving seat, even with minimal training, they can offer significant benefits in support of strategic wargaming, showing effective performance across multiple phases of the lifecycle. By reducing reliance on scarce wargaming expertise, LLMs can make wargaming more accessible. This allows the wargaming process to be more widely used within military strategic decision-making, ultimately enhancing the quality of human decision-making.
After a U.S. Coast Guard (USCG) search and rescue (SAR) case, USCG personnel create an after-action report containing a textual narrative of the situation and Coast Guard response efforts. Data analysts explored how to identify reports involving cases with a verified person in the water. With restricted access to compute resources and limiting policy, large language models (LLMs) could not be utilized, so statistical ('classical' and non-neural) methods were considered for training a classification model to identify SAR case outcomes from report texts. The dataset was severely imbalanced toward the negative class, and the texts were extremely messy, with many typos and abbreviations. Therefore, an extensive text cleaning pipeline was developed and tested for improving classification performance. The Iterative Token Elimination Algorithm (iTEA) was developed to increase differences in vocabulary between classes. Model improvement was further explored through augmentation of the feature space using non-text data. The best model was an XGBoost model, achieving 0.762 recall and precision (and 0.959 accuracy). Errors from the test set are analyzed to guide future improvements until LLMs can be used, which are expected to improve performance and reduce text cleaning requirements.
Modern military operations demand coordinated activity across multiple domains, including land, air, sea, space, and cyberspace. While existing tools well support simulations and visualizations of physical-domain effects, cyber effects are less well instantiated in current training and decision-support environments. Although cybersecurity visualizations do not necessarily require geospatial representation of device locations, in this tactical context, threats and assets across all domains, including cyber, can be depicted together on a traditional two-dimensional (2D) common operating picture map. However, three-dimensional (3D) views better support tactical navigation and targeting by enhancing the apprehension of depth, elevation, aerial assets, and 3D urban and terrain features. In operational contexts, users can view the physical environment and additional information (e.g., device status) via augmented reality. Our XR Cyber Battlefield Effects Correlation and Simulation tool (CYBERCAST) enables interactive 3D visualization of a multi-domain battlespace with a dynamic simulated scenario involving cyber effects. The system visually alerts users to locations of affected devices and enables them to obtain more information via hover. To provide this XR experience, it was necessary to integrate outputs from three different simulation tools (OneSAF, CyberVAN, and CyberBOSS) in a first-of-its-kind visualization and simulation system.
Blast-wave propagation and its interaction with protective equipment are a critical concern in defense and industrial safety applications. In this study, we present a comprehensive numerical investigation of blast loads on protective plates by utilizing OpenFOAM. We tested different parameters and their effects on blasting by conducting 42 different scenarios. These scenarios involve both TNT and C-4 explosives, three standoff distances of 5, 10 and 15 m, and masses ranging from 10 g to 2 kg. Also, we tested two target geometries, namely a rectangular plate and an octagonal plate. The numerical approach employed Eulerian, compressible fluid dynamics with ideal gas equations of state for air and Jones-Wilkins-Lee (JWL) equations for detonation products. Through the simulations, key blast parameters such as peak overpressure, impulse, decay coefficients and positive phase duration were extracted and analyzed. A hyperbolic relationship between overpressure and scaled distance was found for both types of explosives, which is consistent with established empirical methods. Also, the dependency of the blast-wave decay coefficient on explosive loading was found to be linear in the case of the rectangular target and following a power-law dependence for octagonal geometries. The peak overpressure and decay coefficient were found to be higher in the octagonal geometry compared with the rectangular plate, attributed to more coherent wave reflection and reduced edge effects. The study provides validated numerical results and parameters relevant to the development of blast protective equipment.
