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NASA NTRS2009

Towards Behavioral Reflexion Models

Software architecture has become essential in the struggle to manage today s increasingly large and complex systems. Software architecture views are created to capture important system characteristics on an abstract and, thus, comprehensible level. As the system is implemented and later maintained, it often deviates from the original design specification. Such deviations can have implication for the quality of the system, such as reliability, security, and maintainability. Software architecture compliance checking approaches, such as the reflexion model technique, have been proposed to address this issue by comparing the implementation to a model of the systems architecture design. However, architecture compliance checking approaches focus solely on structural characteristics and ignore behavioral conformance. This is especially an issue in Systems-of- Systems. Systems-of-Systems (SoS) are decompositions of large systems, into smaller systems for the sake of flexibility. Deviations of the implementation to its behavioral design often reduce the reliability of the entire SoS. An approach is needed that supports the reasoning about behavioral conformance on architecture level. In order to address this issue, we have developed an approach for comparing the implementation of a SoS to an architecture model of its behavioral design. The approach follows the idea of reflexion models and adopts it to support the compliance checking of behaviors. In this paper, we focus on sequencing properties as they play an important role in many SoS. Sequencing deviations potentially have a severe impact on the SoS correctness and qualities. The desired behavioral specification is defined in UML sequence diagram notation and behaviors are extracted from the SoS implementation. The behaviors are then mapped to the model of the desired behavior and the two are compared. Finally, a reflexion model is constructed that shows the deviations between behavioral design and implementation. This paper discusses the approach and shows how it can be applied to investigate reliability issues in SoS.

Ackermann, Christopher↗

NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗

NASA NTRS2025

A Preliminary Study on the Feasibility of Large Language Models for Detecting Micro-Behaviors Among Team Members in Space Missions

Large-language models (LLMs) have been recently used for spoken language understanding (SLU) to infer meaning and semantics from speech in tasks such as speaker intent and sentiment classification. Due to being trained on large amounts of data, and their ability to understand context and relationships between words, LLMs are competent, enabling them to generalize across tasks without requiring many task-specific training samples. This research examines the feasibility of few-shot learning in LLMs for detecting subtle, brief, and possibly unconscious interactions between team members, called ``micro-behaviors," and provides insights into the appropriate design of LLMs for this task. Our data came from 5 teams participating in a 45-day mission at the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA). More specifically we used data collected from team interaction battery (TIB) tasks teams performed five times in-mission which comprise an average 1.5 hours of conversation data per day. Micro-behaviors were coded according to an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). We explore the ability of LLMs to detect the presence and intensity of micro-behaviors. We examine employing and fine-tuning readily available LLMs (i.e., RoBERTa, DistilBERT), as well as prompting state-of-the-art sequence classification models (i.e., Llama-2, Llama-3). In a total of 13,058 conversational turns (17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls), we compute the macro F1-score of the 3-way micro-behavior classification task (i.e., classifying among uplifting, discouraging, and neutral; 33% chance). Results indicate that the RoBERTa model achieves a F1-score of 36.2% (uplift: 43.3% precision (P), 15.1% recall (R); discourage: 20% P, 0.5% R). These results significantly improve when we augment the data via paraphrasing in the RoBERTa model, reaching a 41.2% macro F1-score (uplift: 37.7% P, 86.3% R; discourage: 3.5% P, 1.8% R). Finally, the Llama-2 model with 3-shot prompting yields 38% macro F1-score (uplift: 28.7% P, 20% R; discourage: 7.2% P, 18% R), which is slightly better compared to the RoBERTa model without data augmentation, highlighting the effectiveness of sequence classification models in detecting minority classes with a small sample size. Findings indicate that LLMs hold potential to detect subtle behaviors in conversations, which could be valuable in assessing team behavior in space exploration missions. Future studies will evaluate the performance of different LLM prompting strategies or fine-tuning methods.

Ankush Raut↗

NASA NTRS1997

Effect of environmental enrichment devices on behaviors of single- and group-housed squirrel monkeys (Saimiri sciureus)

Squirrel monkeys display an interest in novel places, habituate to new situations, and spend most of their daily activity in the wild in large groups engaging in feeding behaviors over a broad area. Captivity limits these behaviors and consequently may disrupt normal social organizations. In captivity, squirrel monkeys may exhibit stereotypical behaviors that are believed to indicate decreased psychologic well-being. When a monkey's behavior can be made to approach that seen in the wild, and stereotypical behaviors are minimal, it is assumed that psychologic well-being is adequate. Environmental enrichment devices have been used to address the Animal Welfare Act requirement that psychologic well-being of captive nonhuman primates be considered. The purpose of the study reported here was to examine whether various environmental enrichment devices improve the psychologic well-being of captive squirrel monkeys. In the study, we used behavioral observation to quantify the effectiveness of several environmental enrichment devices for reducing stereotypical behaviors in squirrel monkeys housed alone or in groups. Analysis of our results revealed that the environmental enrichment devices did not affect the expression of normal or stereotypical behaviors, but that the type of housing did.

NASA Discipline Neuroscience↗

NASA NTRSDate not supplied

The Effects of Placental-expanded (PLX-PAD) Stromal Cell Treatment, Hindlimb Unloading, and Isolation on the Behavior of Female Mice

Spaceflight can lead to altered immune responses and inflammation (Crucian et al. 2014) and elevated levels of inflammation are connected to anxiety and depression. A recent study on the International Space Station showed that mice exhibited a novel circling behavior during spaceflight (Ronca et al. 2019). However, there is still a gap in knowledge on how microgravity impacts behavior. In this current study, we performed 30 days of hindlimb unloading (HU) on four-month old female mice and analyzed select behavior from video image capture. We also determined the effects of PLacental-eXpanded stromal cells derived from the maternal placenta (PLX-PAD), alone and in combination with HU and isolation, on behavior. We have previously shown that PLX-PAD mitigates select inflammatory responses and changes in cytokine expression caused by HU. In-cage behaviors were analyzed in HU or control female mice treated with 2 injections of PlasmaLyte (Sham) or PLX-PAD (n=7/group). We found that PLX-PAD decreased exploratory behaviors compared to Sham-treated mice at night. Normally loaded (NL) PLX mice slept less than NL Sham mice during the day, and HU animals had significantly higher involuntary movement during sleep compared to NL animals, suggesting sleep disruption. Overall, we show that both HU and PLX affect important behavioral factors. This experiment is the first to study the effects of PLX-PAD on behavior. Additional studies are needed to define the behavioral changes in spaceflight and test possible countermeasures.

PLacental-EXpanded stromal cells, behavior, microg↗

NASA NTRS2021

The Effects of Simulated Microgravity and PLacental-EXpanded (PLX-PAD) Stromal Cell Treatment on The Behavior and Correlation with Cytokine Profiles in Female Mice

Spacelight can lead to altered immune responses and inflammation (Crucian et al. 2014) and elevated levels of inflammation are connected to anxiety and depression. A recent study on the International Space Station showed that mice exhibited a novel circling behavior during spaceflight (Ronca 2019). However, there is still a gap in knowledge on how microgravity impacts behavior. In this current study, we performed 30 days of hindlimb unloading (HU) on four-month old female mice and analyzed select behavior from video image capture. We also determined the effects of PLacental-eXpanded stromal cells dervided from the maternal placenta (PLX-PAD), alone and in combination with HU and isolation, on behavior. We have previoulst shown that PLX-PAD mitigated select inflammatory responses and changes in cytokine expression causes by HU. In-cage behaviors were analyzed in HU or control female mice treated with 2 injections of PlasmaLyte (Sham) or PLX-PAD (n=7/group). We found that PLX-PAD decreased exploratory behaviors compared to Sham treated mice at night. Normally loaded (NL) PLX mice slept less than NL Sham mice during the day, and HU animals had signficantly higher involuntary movement during sleep compared to NL animals, suggesting sleep distruption. Overall, we show that both HU and PLX affect important behavioral factors., This experiment is the first to study the effects of PLX-PAD on behavior. Additional studies are needed to define the behavioral changes in spaceflight and test possible countermeasures. Funding for this project was provided through the 2019 Ames Research Innovation Award (ARIA) provided by NASA Ames Research Center.

Stromal cell treatment↗

NASA NTRS2017

Behavioral Adaptations of Female Mice on the International Space Station

Adult female mice were sent to the International Space Station (ISS) as part of an early life science mission utilizing NASA's Rodent Habitat. Its primary purpose was to provide further insight into the influence of a microgravity environment on various aspects of mammalian physiology and well-being as part of an ongoing program of research aimed ultimately at understanding and ameliorating the deleterious influences of space on the human body. The present study took advantage of video collected from fixed, in-flight cameras within the habitat itself, to assess behavioral adaptations observed among in-flight mice aboard the ISS and differences in behavior with respect to a control group on the ground. Data collection consisted of several behavioral measures recorded by a trained observer with the assistance of interactive behavior analysis software. Specific behavioral measures included frequencies of conspecific interactionsociability, time spent feeding and conducting hygienic behavior, and relative durations of thigmotactic behavior, which is commonly used as an index of anxiety. Data were used to test tentative hypotheses that such behaviors differ significantly across mice under microgravity versus 1g conditions, and the assumption that the novel experience of microgravity itself may represent an initially anxiogenic stimulus which an animal will eventually acclimate to, perhaps through habituation.

Behavior in space↗

NASA NTRS2017

Behavioral Adaptations of Female Mice on the International Space Station

rodent behavior↗

NASA NTRS2004

Habitat Utilization Assessment - Building in Behaviors

Habitability, and the associated architectural and design attributes of an environment, is a powerful performance shaping factor. By identifying how inhabitants use an area, we can draw conclusions about what design or architectural attributes cause what behaviors and systematically design in desired human performance. We are analyzing how a crew uses a long duration habitat and work environment during a four-day underwater mission and identifying certain architectural and design attributes that are related to, and potential enablers of, certain crew behaviors. By identifying how inhabitants use the habitat, we can draw conclusions about what habitability attributes cause what behaviors and systematically design in desired human performance (applicable to NASA's Bioastronautics Human Behavior and Performance Critical Path Roadmap question 6.12). This assessment replicates a methodology reported in a chapter titled "Sociokinetic Analysis as a Tool for Optimization of Environmental Design" by C. Adams.' That study collected video imagery of certain areas of a closed habitat during a 91 day test and from that data calculated time spent in different volumes during the mission, and characterized the behaviors occurring in certain habitat volumes thus concluding various rules for design of such habitats. This study assesses the utilization of the Aquarius Habitat, an underwater station, which will support six Aquanauts for a fourteen-day mission during which the crew will perform specific scientific and engineering studies. Video is recorded for long uninterrupted periods of time during the mission and from that data the time spent in each area is calculated. In addition, qualitative and descriptive analysis of the types of behaviors in each area is performed with the purpose of identifying any behaviors that are not typical of a certain area. If a participant uses an area in a way different from expected, a subsequent analysis of the features of that area may result in conclusions of performance shaping factors. With the addition of this study, we can make comparisons between the two different habitats and begin drawing correlation judgments about design features and behavior. Ideally, this methodology should be repeated in additional Aquarius missions and other analog environments because the real information will come from comparisons between habitats.

Whitmore, Mihriban↗

DOE OSTI2025

E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders

In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.

E-scooter↗

NASA NTRS2021

Quirky Circling Behavior in Mice Informs Research on Humans in Space

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. We previously conducted a detailed phenotypic analysis of mouse behavior during long duration (33-day) spaceflight in the NASA Rodent Habitat (Rodent Research-1; RR1 mission). Notably, we documented the emergence within the 8-10 days of launch of spontaneous ambulatory behavior in the form of circling or ‘race-tracking’ behavior in spaceflight but not in an identical ground control condition. Circling is unique to the NASA RH not having been reported to occur in other mouse habitats that provide limited opportunity for grasping, and/or are characterized by smaller habitable volumes viz., Italian Mice Drawer System (MDS) flown on ISS; Russian Block Obespecheniya Soderzhaniya (BOS) flown on Bion M-1, or JAXA Habitat Cage Unit (HCU) flown in the ISS Kibo module. Over time, circling became the primary dark cycle activity of FLT mice, occurring in individuals then as a coordinated group activity. Here we discuss possible interpretations of circling behavior including: (1) Stereotypic or abnormal repetitive behaviors (ARBs) that are unvarying, and apparently functionless behavior patterns documented numerous species in laboratory and zoo settings, possibility related to insufficient environmental enrichment and stress, (2) Rewarding effects of physical activity that are well-documented in terrestrial studies of rodents given the opportunity to run in wheels, and (3) Vestibular self-stimulation, i.e., the generation biologically-relevant amounts of vestibular sensory input to reduce the effects of microgravity. Affording mice the opportunity to grab and run in the RH resembles physical activities that the crew participate in routinely. Our approach is yielding an interesting analogue for better understanding human responses to spaceflight, and providing the opportunity to begin to address how physical movement influences responses to microgravity.

spaceflight↗

DOE OSTI2026

Heterogeneous estimations of non-pharmaceutical mitigation behavior during the COVID-19 pandemic

The COVID-19 pandemic highlighted the importance of human behavior in mitigating the spread of disease. Nonetheless, human behavior is often overlooked in models of disease spread, particularly by underutilizing real-world data. We address this by estimating probabilities that individuals engage in behaviors that influence SARS-CoV-2 transmission risk during the COVID-19 pandemic, between September 2020 and June 2022. These behaviors include wearing a mask, using public transportation, spending time with others, avoiding contact with others, and going to work. Our estimates account for the age and sex of individuals and are generated for every county in the United States. We utilized multiple open-source datasets and United States Census data to produce these estimates. Multiple datasets were used for validation, showing our estimates demonstrated comparable accuracy and robustness. Our estimates aid in understanding human behavior dynamics during the COVID-19 pandemic and could be used to inform monthly or longer-term behavior in simulations of COVID-19. Moreover, the methods presented can be applied to other behaviors and features for future simulations of infectious disease.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2024

Explaining Health Risk Behaviors in the U.S. with Social Deprivation at Local and Regional Levels

Health risk behaviors are precursors to many chronic health outcomes, and hence, they pose a challenge to public health. Social deprivation undoubtedly creates circumstances that limit access to healthy habits. Moreover, broad regional effects (weather patterns, political ideology, social norms), and local characteristics (cultural notions and barriers, urban places) also influence lifestyle choices and must be accounted for to truly understand the impact of social deprivation on risky behaviors. This research fills the knowledge gap in epidemiological modeling of health risk behaviors by leveraging machine learning to find associations between social deprivation and health risk behaviors, when adjusted by regional and local effects. Four health risk behaviors, namely, binge drinking, smoking, lack of sleep, and lack of physical activity from the CDC PLACES project are considered in a single framework to understand and compare the interplay between local/regional characteristics and seven measures of social deprivation. Our results indicate that local and/or regional factors rise to the top for three out of four risk behaviors (binge drinking, smoking and lack of sleep) out-competing social deprivation measures. Un-entangling the geographical effects reveals that poverty, educational attainment and non-employment are the three deprivation measures most significantly associated with all four health risk factors. The research thus indicates that public health policies to promote healthy lifestyle behaviors must seek to remedy social deprivation, but using socially and culturally sensitive interventions.

Gokhale, Swapna↗

DOE OSTI2024

Different structural behavior of MgSiO 3 and CaSiO 3 glasses at high pressures

Knowledge of the structural behavior of silicate melts and/or glasses at high pressures provides fundamental information for discussing the nature and properties of silicate magmas in the Earth’s interior. The behavior of Si-O structures under high-pressure conditions has been widely studied, while the effect of cation atoms on the high-pressure structural behavior of silicate melts or glasses has not been well investigated. Here, in this study, we investigated the structures of MgSiO 3 and CaSiO 3 glasses up to 5.4 GPa by in situ X-ray pair distribution function measurements to understand the effect of different cations (Mg 2+ and Ca 2+ ) on high-pressure structural behavior of silicate glasses. We found that the structural behavior of MgSiO 3 and CaSiO 3 glasses are different at high pressures. The structure of MgSiO 3 glass changes by shrinking of Si-O-Si angle with increasing pressures, which is consistent with previous studies for SiO 2 and MgSiO 3 glasses. On the other hand, CaSiO 3 glass shows almost no change in Si-Si distance at high pressures, while the intensities of two peaks at ~3.0 and ~3.5 Å change with increasing pressure. The structural change in CaSiO 3 glass at high pressure is interpreted as the change in the fraction of the edge-shared and corner-shared CaO 6 -SiO 4 structures. The different high-pressure structural behavior observed in MgSiO 3 and CaSiO 3 glasses may be the origin of differences in properties, such as viscosity between MgSiO 3 and CaSiO 3 melts at high pressures. This signifies the importance of different structural behaviors due to different cations in investigations of the nature and properties of silicate magmas in Earth’s interior.

36 MATERIALS SCIENCE↗

NASA NTRS1990

Unique failure behavior of metal/composite aircraft structural components under crash type loads

Failure behavior results are presented on some of the crash dynamics research conducted with concepts of aircraft elements and substructure which have not necessarily been designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs which incorporate improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash type loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures which include individual fuselage frames, skeleton subfloors with stringers and floor beams but without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static/dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models. It is believed that the thread of similarity in behavior is telling the designer and dynamists a great deal about what to expect in the crash behavior of these structures and can guide designs for improving the energy absorption and crash behavior of such structures.

Carden, Huey D.↗

NASA NTRS1990

Behavior of composite/metal aircraft structural elements and components under crash type loads: What are they telling us

Failure behavior results are presented from crash dynamics research using concepts of aircraft elements and substructure not necessarily designed or optimized for energy absorption or crash loading considerations. To achieve desired new designs which incorporate improved energy absorption capabilities often requires an understanding of how more conventional designs behave under crash loadings. Experimental and analytical data are presented which indicate some general trends in the failure behavior of a class of composite structures which include individual fuselage frames, skeleton subfloors with stringers and floor beams but without skin covering, and subfloors with skin added to the frame-stringer arrangement. Although the behavior is complex, a strong similarity in the static and dynamic failure behavior among these structures is illustrated through photographs of the experimental results and through analytical data of generic composite structural models. It is believed that the similarity in behavior is giving the designer and dynamists much information about what to expect in the crash behavior of these structures and can guide designs for improving the energy absorption and crash behavior of such structures.

Carden, Huey D.↗

NASA NTRS1990

Behavior of composite/metal aircraft structural elements and components under crash type loads - What are they telling us?

Carden, Huey D.↗