Summary

The statement of task that guided the work of the Army Research Laboratory Technical Assessment Board (ARLTAB) is as follows:

The U.S. Army Combat Capabilities Development Command (DEVCOM) ARL is the Army’s corporate research laboratory strategically placed under the Army Futures Command. ARL is the U.S. Army’s sole fundamental research laboratory focused on cutting-edge scientific discovery, technological innovation, and transition of knowledge products that offer great potential to strengthen the U.S. Army. The mission of ARL is to operationalize science for transformational overmatch in support of persistent Army modernization.¹

ARL made a recent organizational shift from a structure of five directorates to three directorates—the Army Research Office (ARO) Directorate, the Army Research Directorate (ARD), and the Research Business Directorate (RBD). RBD centralizes laboratory business operations and fosters intermural and extramural strategic decision-making. ARO, which has been operational since 1951, is composed of more than 100 engineers, scientists, and support staff who manage ARL’s extramural research program. ARO drives cutting-edge and disruptive scientific discoveries with an eye toward enabling crucial future Army technologies and capabilities through high-risk, high-reward research opportunities.² ARD is a largely intramural research directorate that focuses on exploiting concept development, discovery, technology development, and transition of the most promising disruptive science and technology (S&T) to deliver to the Army fundamentally advantageous science-based capabilities through the laboratory’s 11 research competencies. This intramural research directorate also manages the laboratory’s essential research programs, which are flagship research efforts focused on delivering defined outcomes. ARD also co-manages the core competencies within the competencies, which are discussed in greater detail below.^3,4

___________________

¹ U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL), “We Are DEVCOM Army Research Laboratory,” https://www.arl.army.mil, accessed December 20, 2022.

² DEVCOM ARL, “Who We Are,” https://www.arl.army.mil/who-we-are, accessed December 20, 2022.

³ Ibid.

⁴ The text was modified after the release to the sponsor to clarify the Army Research Directorate’s (ARD’s) role in managing the core competencies.

In fall 2022, ARL’s scientific research efforts were reorganized into 11 competencies, which include biological and biotechnology sciences; electromagnetic spectrum; energy sciences; humans in complex systems; mechanical sciences; military information sciences; network, cyber, and computational sciences; photonics, electronics, and quantum sciences; sciences of extreme materials; terminal effects; and weapons sciences. Each competency has within it varying numbers of “core competencies,” which are specially defined areas of research focus. See Appendix B for the full list and descriptions of ARL’s competencies and core competencies. Both ARO’s and ARD’s intramural and extramural work exist, and often integrate, under the umbrella of each competency and its core competencies.

For this 2022 assessment, ARLTAB examined the following competencies: humans in complex systems, terminal effects, and weapons sciences. It was assisted by three panels, the Panel on Assessment of Humans in Complex Systems, the Panel on Assessment of Terminal Effects, and the Panel on Assessment of Weapons Sciences, which contributed important observations and writings for ARLTAB’s development of this assessment report after making site visits to ARL’s Aberdeen Proving Ground in fall 2022. This report presents ARLTAB’s assessment of only the projects and programs presented within these three competencies and is not intended to portray the entirety of the S&T work across ARL. The complete assessment of the work presented in ARL’s 11 competencies will be built over a 4-year period, with this report being the first in a series.

SUMMARY OF COMPETENCY FINDINGS, OPPORTUNITIES, AND KEY RECOMMENDATIONS

This section provides a comprehensive summary of the findings and opportunities identified within each competency and its core competencies. In cases where core competency opportunities were too extensive to summarize, the writings below direct the reader to where these opportunities are found within the report.

The chapter writings on each competency provide additional guidance on where efforts could be increased or shifted in new directions as well as recommendations and commentary largely related to the individual projects and programs presented during the 2022 assessment. Chapter 5 provides a comprehensive view of all findings, conclusions, and recommendations that cut across the three competency chapters into formal competency-specific and crosscutting (those that address issues that appear to affect all three of the competencies reviewed) conclusions and recommendations.

Humans in Complex Systems

Research in the humans in complex systems competency focuses on aspects of how humans interact within a complex social, technological, and socio-technical systems. ARL subdivides this competency into six core competency areas that include bidirectional human–system communication, estimating and predicting humans in complex systems, human-guided system adaptation, human–system team interactions, hybrid human–technology intelligence, and neuroscience and neurotechnologies.

Bidirectional Human–System Communication

Intramural and extramural research presented in the bidirectional human–system communication core competency focuses on enhancing real time multimodal communications between soldiers and systems. This 2022 assessment found that work in this core competency was foundationally strong, adapting and extending existing knowledge to new application domains. The expertise was also very strong—both internally and with the extramural partners.

Opportunities identified included a focus on ensuring the ecological validity of the research (e.g., translating the findings into the real world), and with regard to training opportunistic sensing algorithms on data created by humans, employing more consideration of the possible effects of “bad data” (e.g.,

suboptimal human performance and adversarial human inputs). It will be important to stay connected with the academic community and industry experts in areas such as eye tracking, learning sciences, virtual environment development, and machine learning (ML), as well as domain experts. While the facilities and resources are suitable, a key challenge will be the ability to update computing platforms in a secure environment. This will require investment in state-of-the-art digital infrastructure and technical support personnel.

Estimating and Predicting Humans in Complex Systems

Research in the estimating and predicting humans in complex systems core competency is directed toward approaches to sense, analyze, and predict human responses across time scales with the goal of enhancing the ability to understand the operational environment and promote more efficient adaptation of technology to humans. The research focuses on using open-source data sets to extract information about human behavior in complex environments and on building predictive models for human performance in novel situations. It is shifting to focus more on prediction with limited data sets in real-world scenarios and the development of technology that can enable closed-loop bidirectional control of human states (that includes males and females with varying levels of experience in task-learning to ensure statistical rigor across gender) toward effective team behavior. This is an important and timely thrust for the intramural and extramural teams. The quality of the research was at par with other leading research institutions and the excellent staff expertise is bolstered by collaborations with leading scientists at esteemed extramural institutions. The equipment and facilities supporting this core competency are excellent.

Opportunities include (1) accelerating the transition of focus from less stereotyped or static virtual settings toward more on “in-lab” proxies and real-world scenarios for ecologically relevant human–human and human–agent scenarios; (2) studying smaller samples (i.e., n = 1 or a few individuals) with greater depth and with measurements that are very high resolution across wide scales (e.g., whole brain to individual neuron) to better address adaptability of individuals (i.e., deep phenotyping—physiological measurements of behavior such as movement, heart rate, gaze direction, and non-invasive electrophysiology like electromyography and electroencephalogram [EEG]); and (3) investing in bolstering the data science expertise—including a focus on engaging more scientists and engineers who work in the nascent field of big data (i.e., data with volumes that cannot easily be managed by traditional data-processing approaches) who can develop and apply leading-edge ML algorithms that may also take into account human heterogeneity.

Human-Guided System Adaptation

Research in human-guided system adaptation core competency is focused on developing new approaches to adapt emerging intelligent technologies to create and enhance human–system capabilities. This core competency team is leading the field in terms of algorithm, artificial intelligence (AI), simulation, integration, and implementation of human-guided mechatronic systems. ARL is making good recruitment decisions. The facilities toured were excellent.

Opportunities include (1) investigating the need for additional expertise in embedded systems and mechatronic systems—as the system-level implementation of human-guided system adaptation is so complex, errors will compound without early real-world testing, and the efficacy of the advances will be more meaningful if tested with humans early; (2) prioritizing the incorporation of data scientists to work on statistical analysis of data sets; (3) broadening the diversity of human participants when gathering test data; (4) testing the efficacy of AI agents and other software systems in real hardware earlier in the development cycle and placing more importance on the use of hardware development and qualification in parallel with software efforts; and (5) creating a makerspace (a communal workspace supplied with

relevant tools and technologies) with manufacturing experts in embedded systems and hardware that is more streamlined to use, such a workspace would encourage closing the hardware loop earlier.

Human-System Team Interactions

Research in the human-system team interactions core competency focuses on developing human–human and human–machine integration and teaming technology and is clearly in the lead nationally, if not internationally. The research aimed at developing better understanding of teams operating in complex environments is exceptional. This has been accomplished by developing meaningful metrics for qualities such as team cohesion, trust, warmth, and force-multiplication factors, which have helped to develop technology and techniques that facilitate faster and better decisions in complex operational environments. The expertise with this core competency is very good, and ARL has equipped itself exceptionally well to be able to explore team performance and team effectiveness. Still, some statistical tests used by ARL presenters were not appropriate for the categories. Statistical and data science expertise could be grown locally by working closely with extramural partners or by expanding educational and training opportunities for current staff. Adding interdisciplinary teams of psychologists and behavioral scientists, as well as teaming scientist and colleagues who have expertise in language science and sensory integration and study design expertise, would also be a major asset. While the facilities and resources to support this core competency were outstanding, the Information for Mixed Squads (INFORMS) Laboratory⁵ was found not to have a convenient or uncomplicated means of updating the software in⁶ their large bank of computers and servers due to the local computer security restrictions. This problem poses a significant hindrance to the programmers and research scientists working in the INFORMS Laboratory and needs remediation.

Hybrid Human–Technology Intelligence

The thrust in hybrid human–technology intelligence core competency represents a new start at ARL. The directions chosen for this work are promising—focused, in part, on incorporating recent advances in ML, particularly creative and generative AI, to enhance human decision-making. ARL’s proposed research approach was described to the panelists by ARL as “antidisciplinary,” and this report will continue to use this term to stay consistent with this core competency’s vision of ARL’s work. ARL

___________________

⁵ ARL’s Information for Mixed Squads (INFORMS) Laboratory is a 14-member crew station simulator designed for rapid ideation and prototyping of concepts for the U.S. Army’s next generation combat vehicle. The laboratory allows soldiers to complete platoon-level missions against a human controlled opposing force. For more information on the INFORMS Laboratory, see Information for Mixed Squads (INFORMS) Laboratory, https://www.arl.army.mil/cast/INFORMS, accessed June 1, 2023. Complimenting INFORMS is ARL’s Department of Defense (DoD) Supercomputing Resource Center (DSRC), which contains a high-performance computing (HPC) system that is used by ARL to conduct a wide range of research, including modeling and simulation of complex systems, development of new algorithms and software, data analytics, artificial intelligence (AI), and machine learning (ML). It is a two-system cluster consisting of SCOUT and Centennial. SCOUT is an IBM Power9 system with 22 training nodes, 128 inference nodes, and 2 visualization nodes. Centennial is an SGI ICE XA system with 1,784 standard compute nodes, 32 large-memory compute nodes, and 32 GPU compute nodes (see ARL, “U.S. Army Research Laboratory DoD Supercomputer Center,” https://www.arl.hpc.mil/docs/introGuide.html, accessed June 15, 2023). The total peak performance of DSRC HPC is 1.5 petaFLOPS, which means that the system can perform 1.5 quadrillion floating-point operations per second. HPC is also equipped with 100 TB of memory and 450 PB of storage. Researchers in ARL’s HCxS frequently use DSRC HPC for AI and ML applications to data generated during large-scale experimentation in the INFORMS Laboratory and ML applications to data generated during large-scale experimentation in the INFORMS Laboratory. The text in this footnote was modified after the release to the sponsor to provide a more in-depth and accurate description of the INFORMS Laboratory.

⁶ The text here and throughout was modified after the release to the sponsor to clarify the type of updates that are the focus of this discussion.

defines antidisciplinary as a field of study that develops novel methods, frameworks, and nomenclature that are beyond the boundaries of traditional academic disciplines. ARL believes this is different from a multidisciplinary approach, which would be the combination of different disciplines. At the same time, ARL still utilizes research lessons from multiple fields, disciplines, and domains, including human–computer interaction, psychology, AI, and design. The emerging work is well situated in these fields, and proposed future work promises to embrace this antidisciplinary approach by bringing together leaders in these different fields to inform future research. The team leading this effort is highly qualified in this core competency. The approach of embedding ARL technical staff into research laboratories across the nation is a major strength. Opportunities include adopting an embedded AI ethics approach and disciplinary expansion into the areas of game design and development, and improving staff resources in game development, especially for art assets. Additionally, a dedicated makerspace facility could be developed with technologies such as three-dimensional (3D) printing, laser cutting, and textile prototyping.

Neuroscience and Neurotechnologies

The neuroscience and neurotechnologies core competency focuses on research to harness the power of the human nervous system to enable neuroscientific principles to maximize soldier performance and drive the understanding and development of novel computational approaches necessary for creating future intelligent systems. Neurofeedback systems (brain–computer interface) were proposed to enhance task performance and improve human-agent teaming. Novel research is being pursued in adapting principles of complex physics and network science to both characterize brain dynamics and potentially improve on the brain’s dynamical organization.

High-quality research was demonstrated across all talks and presentations. The work presented, and its research methods and approaches were equivalent or better (in many cases) than leading research institutions in the United States. The intermural and extramural staff had impressive qualifications.

Opportunities include (1) applying more emphasis on knowledge learned into real-world ecological settings; (2) developing more autonomy in the designing of experiments and data collection through the engagement of data scientists; (3) developing a small team, center, or laboratory that is dedicated to providing support to the ARL teams in terms of data science principles for improving reproducibility of experiments and greater autonomy with a stronger data sharing infrastructure; (4) exploring neuroimaging devices other than EEG, such as functional near-infrared spectroscopy and, in the near future, optically pumped magnetometers (OPM-MEG), and (5) incorporating precision neuroscience approaches, which would include using dense sampling of individuals (i.e., n = 1 studies) for better characterization of state changes; using individual-based neuroanatomical features; for example, brain parcellations⁷ or EEG power bands calibrated to individual participants; and modeling changes in environment in addition to changes in brain states to better account for signal variations (e.g., clenching teeth due to environmental stress).

Terminal Effects

Research in the terminal effects competency focuses on advancing the underlying science and pursuing applied research related to weapon–target interactions. Its four core competencies include armor mechanisms and countermeasure; mechanisms for human injury and protection; mechanisms for lethal target interactions; and terminal effects mechanics, modeling, and simulation.

___________________

⁷ P. Moghimi, A.T. Dang, T.I. Netoff, K.O. Lim, and G. Atlurib, 2021, “A Review of MR Based Human Parcellation Methods,” https://arxiv.org/ftp/arxiv/papers/2107/2107.03475.pdf.

Armor Mechanisms and Countermeasure

The armor mechanisms and countermeasure core competency examines techniques to minimize or eliminate lethal effects at minimum space, weight, and power. Within this core competency, there is availability of excellent materials characterization systems, theater-based assessment tools, and performance evaluation and testing facilities. An important contribution of this core competency’s fundamental research is directed at metallurgical approaches to enhance shock resistance in armor. Another goal is to develop next generation armor using ML methods that leverage a rich and unique armor-testing database. Efforts to catalog and make this database more searchable is an important thrust of the work. As ARL prepares to incorporate more physics of materials (e.g., predictive physics of operative mechanisms as well as microstructure quantification) into its ML models, there exists an opportunity to proactively engage with government laboratories, as well as academic and industrial data taskforces (e.g., through the Materials Research Society) to streamline ARL’s data management efforts. There also exists significant opportunities for close coordination of terminal effects expertise and ML expertise (both within ARL divisions and extramurally) to advance the state of the art in armor design.

Mechanisms for Human Injury and Protection

Work within the mechanisms for human injury and protection core competency is directed at gaining understanding of human injury to high-intensity loading from ballistics or blasts. The work is specially focused on mitigation of critical tissue and organ damage with an emphasis on injuries related to skull fracture, lung damage, or armor penetration. Armor simulations, as well as experiments to characterize new materials, have been performed in support of this research. The armor simulations and experiments presented were outstanding.

The core competency has at least one external collaborator for each of the projects. Some of the extramural scientists are well known and highly qualified to support the goals of this core competency. Others did not seem to be doing mechanics at the leading edge that could advance the technical goals of the overall competency. For the most part, ARL appears to have outstanding facilities and resources to support the mechanisms for human injury and projection core competency. The lung project, however, did not seem to be utilizing internal computational resources to the extent needed to adequately support simulating the available experimental test data. It is not clear, however, if individual investigators or projects are limited by inadequate resources in this focus area or a lack of access to them.

ARL is leading in simulations of lung mechanics. However, challenges that need addressing include generating simulations that can recreate the physics from the experiments (e.g., blast loading) to mitigate over- and under-correcting the simulation results. Addressing these challenges will require engaging more outside collaborators that perform experiments using animal models to advance the lung mechanics modeling field. This is an opportunity for ARL to become one of the leaders at linking lung mechanics with local injury. Additionally, there is an opportunity to advance the development of ARL’s surrogate lung model by using the data from ARL’s high-speed X-ray videos of lung deformation for validation.

Lethal Target Interactions

Research in the mechanisms for lethal target interactions core competency focuses on understanding and advancing weapon–target interactions to maximize munition lethality across all calibers and platforms. The research methodology relies heavily on experimentation and is of a quality comparable to research at other top academic and research institutions. Balancing the portfolio with modeling will create an opportunity for hypothesis-driven experimentation and a better path forward for understanding the underlying physics. New discoveries related to projectile–armor interaction are impactful for future applications for the Army.

Terminal Effects Mechanics, Modeling, and Simulation

The terminal effects mechanics, modeling, and simulation core competency focuses on providing the scientific foundation and the tools for the work performed throughout the terminal effects competency. Research efforts are directed at developing a quantitative understanding of structural and material response due to ballistic, blast, and highly energetic events. High-fidelity experiments support the development and validation of theory, algorithms, and computational techniques required to develop technology concepts for protection and lethality.

The overall level of scientific and engineering staff across the breath of the terminal effects mechanics, modeling, and simulation core competency within ARL’s overall terminal effects portfolio (both ARD and ARO) was excellent in its caliber of personnel, their productivity, and their vision driving the direction of their projects. The facilities supporting the research were also excellent.

A concern about the use of different codes was identified. The commercial codes are largely being exercised in extramural projects, while internally the scientists tend to use codes developed by the Department of Energy’s National Nuclear Security Administration (NNSA) national laboratories. To have confidence in the outputs, there needs to be a benchmarking exercise on a common problem between the commercial and NNSA codes. This will also allow a determination of the physics or numerical gaps in each platform, while also pointing to which code is most suitable for a particular task.

Weapons Sciences

Research in the weapons sciences competency focuses on internal, transitional, and external ballistics. It also examines launch, flight, control, and navigation of guided weapons and aerial systems and addresses the development of novel weapon concepts.⁸ Its core competencies include aerodynamics and control; discovery, synthesis, and formulation of energetic materials; guidance and navigation of weapon systems; gun and rocket propulsion; and modeling, simulation, and experimental characterization of energetics.

Aerodynamics and Control

Research activities in the aerodynamics and control core competency span a broad range of flight regimes, from low-speed unmanned aircraft systems (UASs) to hypersonic flight related to gun-launched systems. Fundamental research is being pursued in laminar to turbulence transition, vortex interactions, heat loading and shock wave boundary–layer interactions. University collaborations have facilitated the use of experimental facilities that enable studies of transition from laminar to turbulent flow. The Army has provided a canonical test article configuration, the High-Speed Army Reference Vehicle, which is being used for measurements in Mach 6 low enthalpy, “quiet” test facilities at Texas A&M University and Purdue University. The Aerodynamics Experimental Facility and the Transonic Experimental Facility are being used for flight tests and represent unique capabilities. Computational fluid dynamics (CFD) models are also being studied in close collaboration with researchers at the University of Maryland.

In the context of UAS, the research is focused in three areas: extreme UAS (i.e., hyper maneuverability and use of nonlinear interactions), intelligent UAS (i.e., AI and autonomy), and advanced UAS technologies (e.g., advanced rotor configurations, tethered UAS, and advanced rotor configurations, tethered). Software tools such as the Hybrid Design and Rotorcraft Analysis (HYDRA) tool are being enhanced and evaluated for preliminary assessment of performance, with the goal of improving agility and maneuverability against future threats. Work related to the gust response (the response to rapid changes in wind speed or wind angle) of UAS is an important focus of the design of control systems to

___________________

⁸ DEVCOM ARL, “We Are DEVCOM Army Research Laboratory,” https://arl.devcom.army.mil, accessed December 20, 2023.

accommodate such disturbances in a range of operational environments. This effort is recognized for its quality and high impact. Another intriguing concept involves tethered unmanned aerial vehicles (UAVs) sharing power from a ground station or a “mother ship” to maximize UAS endurance. ARL staff and researchers within the aerodynamics and control core competency are very competent and have a clear understanding of the fundamental physics underlying the applications. It is also clear that the researchers are aware of the relevant science conducted elsewhere. The scientific quality of the research conducted in hypersonics is excellent, enhanced by close interactions with the academic community and other Department of Defense entities. The hypersonic experimentation is state of the art in the low enthalpy arena, and the codes used are also quite advanced. The numerous successes associated with ARL’s rotorcraft research are also noteworthy, and it was pleasing to see the transition of high-quality research and the impact that this research is having. Taken as a whole, the scientific quality in this specific area is not yet state of the art and there is an opportunity to improve the results with the introduction of high-fidelity CFD.

Overall, the facilities supporting the aerodynamics and control core competency were impressive. The flight facilities available for the study of high-speed flight are outstanding and provide unique capabilities for short-range flight testing. They include the Aerodynamics Experimental Facility and the Transonic Experimental Facility. While there was no onsite visit, their capabilities were clearly demonstrated in videos and through presented material. Utilization of NASA facilities, including the transonic facility at NASA Langley Research Center, is also noteworthy. The ability to do infrared imaging of an in-flight high-speed platform is unique. The Actuated Recirculating Gust Generator facility provides a very useful platform for testing UAS performance and developing predictive models to for rapid recovery.

Opportunities identified in the core competency include the following:

• Further work is needed to study vortex-shock interactions, unsteadiness effects, and shock and boundary-layer interactions as well as effects associated with asymmetric platforms. For many applications, these unsteady phenomena may be more important than transition effects. Hypersonic flow studies at higher Mach numbers, along with coupling of associated new physics, are recommended.
• More thorough examination of optimal choices for UAV power and propulsion systems is suggested with attention to the desired endurance, range, and thrust. At short endurances, the engine or power weight becomes a handicap; while at longer endurances, fuel weight dominates and efficiency becomes more important in the optimization process.
• For the tethered UAV studies, more attention could be given to potential problems with snags and tangles in the UAV tethers. Optical fibers might be considered in the UAV tether design because of their light weight, high flexibility, and good power and data transmissions.
• More attention can be given to research on rotorcrafts at NASA’s Ames Research Center. Furthermore, large eddy simulation methods using high-order numerical methods, as exemplified by the work of Z.J. Wang (University of Kansas), could be incorporated for rotor dynamics.
• One opportunity for the Dynamic Inversion Controller would be to expand the Army research scope in this area to include control co-design or multidisciplinary design optimization with control as a method to further ARL objectives of range extension, enhanced target engagement, and evasion of enemy countermeasures.

Additionally, ML and data science can be integrated into computational and experimental research. The latest developments in reduced order modeling could also be considered (see the work of Charbal Farhat [Stanford University], Karen Willcox [Oden Institute, Utah], and Benjamin Peherstorfer [Courant Institute of Mathematical Sciences, New York University]).

Discovery, Synthesis, and Formulation of Energetic Materials

The 2022 research portfolio in the discovery, synthesis, and formulation of energetic materials core competency focuses on the formulation of novel energetic materials to optimize performance, manufacturing processes, and stability for explosive and propellant applications. The research examines the synthesis of a range of energetic materials, including new molecules and formulation of new explosive mixtures via conventional approaches. New methods of generating reactive aluminum are being explored in the plasma chemistry laboratory. Novel methods of co-crystal and high-pressure synthesis are also being pursued. Although still in the development stage, researchers are applying ML techniques to advance material formulation and synthesis.

The overall scientific quality in this competency area was very good. The synthesis and formulation methodologies being pursued are consistent with that being pursued at leading research institutions and funding agencies. The researchers in each area were aware of the state of the art in their respective fields and broadly speaking, appeared to be working at that level with specific studies working to push the state of art forward. The qualifications of the teams of ARL researchers were found to be excellent. The facilities, resources, and equipment available to the researchers was also excellent.

Opportunities related to individual projects start in the “Research Portfolio Challenges and Opportunities” section of this core competency in Chapter 4. Broader opportunities include the development of a database that captures what molecules are available, what prior formulation pathways have been performed with such molecules, and the resulting performance, sensitivity, and stability properties of each attempt. This would serve as a tool to new researchers in the area and capture the experience of senior researchers and as the starting point for application of ML techniques to the synthesis and formulation process.

Guidance and Navigation of Weapon Systems

The focus of research in the guidance and navigation of weapon systems core competency is on guidance, navigation, and control algorithms, electronics, and design of gun-launched or missile guidance systems. It includes the evaluation of new sensing modalities to improve the performance of navigation and enhancing terminal homing accuracy in challenged or degraded environments. The 2022 review looked at projects focused on autonomous, multi-agent collaborative techniques for navigation, guidance, and control. While some elements of work may be considered foundational, the focus is largely on applied research seeking to incorporate new theoretical advances and technologies to enhance Army capabilities for precise weapons delivery in challenging environments. Many of the projects seek to address the complex challenges of midcourse navigation and terminal guidance following a high-g weapon launch. The work includes the use of a unique multicomponent experimental platform and the development of algorithms for collaborative networking, navigation, and terminal guidance. Researchers in the guidance and navigation of weapons systems core competency generally have a broad understanding of the field and are largely building their work on state-of-the-art research in the open literature. The facilities and its resources are also impressive. Extensively detailed opportunities for this core competency are listed within the “Research Portfolio Challenges and Opportunities” section of this core competency in Chapter 4.

Gun and Rocket Propulsion

The focus of the gun and rocket propulsion core competency is to increase range and velocity of projectiles while reducing the weight and form factor of future manned and unmanned weapon systems. The assessed research was limited to two studies on additive manufacturing (AM) of energetics and manufactured grains. The work is at a preliminary level and primarily focused on addressing technical challenges related to proper operation of the AM machines with energetic materials to achieve parts of

desired tolerances. A second thrust was on the conceptual development of AM to enhance mission capability. The scientific quality appeared to be good, and the researchers demonstrated a broad understanding of the underlying science and research conducted elsewhere. Much of the methods presented appeared to rely on exploratory research or prior publications due to the developing nature of the field. These approaches are consistent with that of other large research institutions in this area. While there were no tours of the facilities, there did not appear to be any limitations in facilities, resources, and equipment available to the researchers for this core competency.

Opportunities include improving connections with the discovery, synthesis, and formulation of energetic materials core competency, which may assist in further development of printable formulations with improved properties and with the development of new materials specifically for AM applications and deepening connections with the modeling, simulation, and experimental characterization of energetics core competency, which may assist with more efficient development (and initial prediction) of novel grain geometries for improved capability.

Modeling, Simulation, and Experimental Characterization of Energetics

The modeling, simulation, and experimental characterization of energetics core competency focuses on the development and application of physics-based theoretical models to design advanced energetic materials for increased lethality and extended range. The research projects ranged from modeling and simulation studies to experimental measurements of energetic materials. The modeling efforts ranged from multi-scale energetic material modeling to classical continuum-scale CFD modeling. ML-based energetic material modeling and data analysis was also being considered. The work has augmented experimental studies across the full range of length scales for energetic materials. The overall scientific quality in this core competency area was excellent and is considered cutting edge with respect to what is being done in other leading research institutions and funding agencies. The researchers in each area were aware of the state of the art in their respective fields and actively trying to advance the state of the art by applying novel techniques. The overall balance of the core competency research portfolio appeared effective and well balanced. The facilities, resources, and equipment available to the researchers within the modeling, simulation, and experimental characterization of energetics core competency are also excellent. Extensively detailed opportunities for individual projects are listed within the “Research Portfolio Challenges and Opportunities” section of this core competency in Chapter 4.