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Aerospace Systems Engineering
Aerospace Systems Engineering
ASYST Lab focuses on design, modeling, and safety of aerospace systems through data-driven and simulation-based approaches. Projects use systems modeling techniques, such as aviation safety modeling, agent-based simulations, and model-based systems engineering, to improve the efficiency and reliability of aerospace systems.
Modeling of General Aviation Fixed-wing Aircraft Accidents
Modeling of General Aviation Fixed-wing Aircraft Accidents
A state-based modeling framework to analyze aviation accidents, using structured representations of aircraft and pilot states and triggers. The state-based model identifies how normal flights transition into hazardous conditions and ultimately result in accidents. This work, completed at Purdue University, and funded by the Federal Aviation Administration (FAA), provides a systematic approach to understanding General Aviation accident causation and human factors.
Previous work led to Dr. Majumdar's PhD dissertation, MS thesis, a journal paper publication, and 2 FAA technical reports.
Collaborators, Relevant Publications, & Funding
Collaborators
Arjun Rao, Lead Engineer, Boeing
Karen Marais, Professor of Aeronautical and Astronautical Engineering, Purdue University
Publications
Majumdar, N., Marais, K., and Rao, A. (2021). Analysis of General Aviation Fixed-wing Aircraft Accidents Involving Inflight Loss of Control Using a State-based Approach. Aviation, 25(4), 283-294. doi: 10.3846/aviation.2021.15837
Majumdar, N. (2023). State-based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots’ Perspectives (Order No. 30499136). ProQuest Dissertations & Theses Global. (2806444500). doi: 10.25394/PGS.22677526.v2
Majumdar, N. (2018). A State-based Approach for Modeling General Aviation Fixed-wing Accidents. M.S. Thesis, Aeronautical and Astronautical Engineering, Purdue University, West Lafayette, Indiana, United States. Available online.
Funding
Federal Aviation Administration (FAA), Center of Excellence (PEGASAS)
Systems Modeling of Unmanned Aerial Vehicles (UAVs)
Systems Modeling of Unmanned Aerial Vehicles (UAVs)
Development of an agent-based simulation, modeling a coordinated swarm of UAVs for crop spraying. By combining safety-driven design principles with swarm coordination strategies, the research aims to enhance both the reliability and performance of UAV-based agricultural operations. The work aims to integrate safety analysis into systems modeling, offering a framework adaptable to other UAV applications beyond agriculture.
This work is currently in progress.
Collaborators, Relevant Publications, & Funding
Collaborators
Cengiz Koparan, Assistant Professor of Precision Agriculture Technology, University of Arkansas
Ryker Davis, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Publications
In progress
Funding
University of Arkansas, Department of Mechanical Engineering
Human Factors
Human Factors
We assess pilot performance using flight simulators and pilot feedback to study decision-making, proficiency, and training needs. By simulating real-world challenges and gathering pilots' perspectives and insights, we inform improvements in pilot training, procedures, and certification for safer flight operations.
Gaining Perspectives using Surveys and Interviews
Gaining Perspectives using Surveys and Interviews
By surveying and interviewing pilots, engineers, and subject-matter experts, we investigate systemic factors and operational conditions influencing human performance in aviation. This approach goes beyond accident analysis to identify training gaps, procedural challenges, and design considerations, guiding improvements in pilot training, operational procedures, and human-systems integration.
In a previous study, we asked pilots about their loss of control experiences, recovery methods, and training to provide a deeper understanding of the role of human factors in these events. This research, conducted at Purdue University, led to Dr. Majumdar's PhD dissertation, 2 journal papers, 2 conference proceedings, and a FAA technical report.
Another study validated four core competencies for human factors practitioners in flight deck design through a survey of experts, highlighting the need for interdisciplinary skills to meet safety and regulatory demands. This research, in collaboration with Honeywell Aerospace, led to a peer-reviewed conference proceeding.
More work is currently in progress at the University of Arkansas.
Collaborators, Relevant Publications, & Funding
Collaborators
Tolu Olatunji, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Alexandra Kemp, Advanced Systems Engineer - Human Factors, Honeywell Aerospace Technologies
Gernot Konrad, Chief Engineer - Human Factors, Honeywell Aerospace Technologies
Thea Feyereisen, Senior Fellow - Human Factors, Advanced Technology, Honeywell Aerospace Technologies
Karen Marais, Professor of Aeronautical and Astronautical Engineering, Purdue University
Publications
Majumdar, N., and Marais, K. (2025). Lessons Learned to Prevent Loss of Control: A Review of Incidents from Pilot Archives. Proceedings of the 23rd International Symposium on Aviation Psychology, 144-149. May 2025. doi: 10.5399/osu/1188
Majumdar, N., and Marais, K. (2025). Human Factors in General Aviation Loss of Control: Survey of Pilot Experiences. Journal of Air Transportation, Vol. 33, No. 1, pp. 57-68 doi: 10.2514/1.D0432
Kemp, A., Gernot, K., Majumdar, N., and Feyereisen, T. (2025). Development of a competency-based short course syllabus for flight deck human factors practitioners. Transportation Research Procedia, 88, 176-184. doi: 10.1016/j.trpro.2025.05.022
Majumdar, N., and Marais, K. (2022). A Survey of Pilots’ Experiences of Inflight Loss of Control Incidents and Training. In AIAA AVIATION 2022 (p. 3778). Jul 2022. doi: 10.2514/6.2022-3778
Majumdar, N. (2023). State-based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots’ Perspectives (Order No. 30499136). ProQuest Dissertations & Theses Global. (2806444500). doi: 10.25394/PGS.22677526.v2
Funding
University of Arkansas, College of Engineering
University of Arkansas, The Division of Research and Innovation
University of Arkansas, Department of Mechanical Engineering
Purdue University
Assessing Pilot Proficiency using Flight Simulator
Assessing Pilot Proficiency using Flight Simulator
This research area focuses on evaluating pilot performance through advanced flight simulation, integrating human factors analysis with quantitative performance metrics. High-fidelity simulators replicate real-world flight environments, enabling controlled testing of piloting skills across various scenarios, including normal operations, abnormal events, and emergency procedures. By introducing environmental challenges, such as adverse weather or equipment failures, and using statistical methods, pilot proficiency can be assessed. The findings aim to inform targeted training interventions and certification processes for safer flight operations.
Previous work led to a poster display and an upcoming conference proceeding. More work is currently in progress.
Collaborators, Relevant Publications, & Funding
Collaborators
Tolu Olatunji, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Ryker Davis, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Nina Kondur, Summer Research Assistant, Purdue University
Publications
In progress
Funding
University of Arkansas, Honors College
University of Arkansas, College of Engineering
University of Arkansas, The Division of Research and Innovation
University of Arkansas, Department of Mechanical Engineering
Next-Generation Transportation
Next-Generation Transportation
We examine safety, benefits, and challenges to successfully integrate Advanced Air Mobility (AAM) electric, sustainable air transport for urban travel into the national airspace.
Factors for Advanced Air Mobility Implementation
Factors for Advanced Air Mobility Implementation
This research examines how people perceive and respond to Advanced Air Mobility (AAM), an emerging mode of transportation that often uses electric aircraft for efficient and sustainable urban travel. Using a methodological approach, the study explores factors such as perceived safety, benefits, and concerns, and identifies human factors based design parameters to support successful integration of AAM into the national airspace.
This work is currently in progress.
Collaborators, Relevant Publications, & Funding
Collaborators
Swapnil Saha, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Noah Bretz, Graduate Assistant, Mechanical Engineering, University of Arkansas
Samson Parker, Undergraduate Research Assistant, Mechanical Engineering, University of Arkansas
Abhishek Phadke, Assistant Professor of Engineering and Computing, Christopher Newport University
Publications
In progress
Funding
University of Arkansas, Honors College
University of Arkansas, College of Engineering
University of Arkansas, The Division of Research and Innovation
University of Arkansas, Department of Mechanical Engineering
Aviation Safety Analysis
Aviation Safety Analysis
We apply data-driven methods, human factors research, and machine learning to provide a better understanding of aviation and aerospace operations. Using formal reports, voluntary safety data, and pilot narratives, we analyze events such as instructional accidents, stall/spin incidents, inflight loss of control, and maintenance failures. Our work identifies operational risks, predicts accident severity, and informs targeted safety interventions in pilot training, maintenance practices, and UAS operations.
Instructional Aviation Accident Analysis
Instructional Aviation Accident Analysis
This study focuses on General Aviation instructional accident analysis using the National Transportation Safety Board (NTSB) accident reports. General Aviation includes non-commercial, non-military operations such as private flying, flight training, and business aviation. Nearly one-third of total active pilots are students. This work examines how instructional accidents occur and their contributing factors.
Previous work led to a BS honors thesis and an honors research symposium poster display (third prize).
More work is in progress.
Collaborators, Relevant Publications, & Funding
Collaborators
Sydney Sommers, Undergraduate Research Assistant, Mechanical Engineering, University of Arkansas
Jesse Pham, Undergraduate Research Assistant, Mechanical Engineering, University of Arkansas
Publications
Pham, J., & Majumdar, N. (2025). Solo Versus Dual: A Comparative Analysis of Instructional General Aviation Accidents. 2025 Research Poster Competition. Available online
Sommers, S. (2024). A Review and Analysis of General Aviation Instructional Accidents. Undergraduate Honors Thesis. Available online
Funding
University of Arkansas, Honors College
Analysis and Prediction of General Aviation Stall/Spin Accidents
Analysis and Prediction of General Aviation Stall/Spin Accidents
This study investigated stall and spin accidents in General Aviation fixed-wing aircraft using historical accident data. Stall/spin is the third leading cause of General Aviation fatalities, accounting for 7.28% of fatal accidents between 2013 and 2022. Using machine learning techniques, the study predicted the likelihood and severity of these accidents based on ~20 different parameters. A stall occurs when lift decreases due to disrupted airflow over the wing, while a spin is a helical descent resulting from an aggravated stall.
Previous work led to an MS thesis, a BS honors thesis, and a conference proceeding.
Collaborators, Relevant Publications, & Funding
Collaborators
Mehedi Hasan, Graduate Assistant, Mechanical Engineering, University of Arkansas
Lauren Westfall, Undergraduate Research Assistant, Mechanical Engineering, University of Arkansas
Publications
Hasan, M., Westfall, L., and Majumdar, N. (2025). Analysis of Stall and Spin Accidents Involving General Aviation Fixed-Wing Aircraft. In AIAA AVIATION 2025. Jul 2025. doi: 10.2514/6.2025-3322
Hasan, M. (2025). Analysis and Predictive Modeling of Stall and Spin Accidents in General Aviation Fixed-Wing Aircraft. Graduate MS Thesis. Available online
Westfall, L. (2025). Causes and Contributing Factors in General Aviation Fixed-wing Stall and Spin Accidents. Undergraduate Honors Thesis. Available online
Funding
University of Arkansas, Honors College
University of Arkansas, Department of Mechanical Engineering
Unmanned Air Systems (UAS) Incident Analysis
Unmanned Air Systems (UAS) Incident Analysis
This study reviewed the status of UAS safety reporting to compare the recorded information and discuss limitations of the databases. Further, using the NASA Aviation Safety Reporting System (ASRS) database and accident classification categories with unsupervised machine learning, the study identified contributing factors in UAS incidents. Using an enhanced classification framework, key categories influencing the incidents were highlighted to inform safety decisions and potential challenges in the current reporting system. Results contributed to a better understanding of operational risks in UAS systems and the effectiveness of using a voluntary reporting system such as the ASRS database.
Previous work led to a BS honors thesis, 2 honors research symposium poster displays, and a conference proceeding.
Collaborators, Relevant Publications, & Funding
Collaborators
Ryker Davis, Graduate Research Assistant, Mechanical Engineering, University of Arkansas
Kacey Haws, Undergraduate Research Assistant, Computer Science, University of Arkansas
Publications
Davis, R. and Majumdar, N. (2025). A Review of Unmanned Aircraft Vehicle Safety Reporting and Analysis of Incidents. In International Conference on Human-Computer Interaction 2025. Jun 2025. Sweden. (in press). Peer-Reviewed
Haws, K. (2025). Analyzing Unmanned Aircraft System (UAS) Incidents from NASA ASRS Data Using Unsupervised Machine Learning. Undergraduate Honors Thesis. Available online
Davis, R., & Majumdar, N. (2025). Poster: A Review of Unmanned Aircraft Systems Safety Reporting and Analysis of Incidents. 2025 Research Poster Competition. Available online
Haws, K., & Majumdar, N. (2025). Poster: Analyzing Unmanned Aircraft System (UAS) Incidents from NASA ASRS Data Using Unsupervised Machine Learning. 2025 Research Poster Competition. Available online
Funding
University of Arkansas, Honors College
University of Arkansas, Department of Mechanical Engineering
Machine Learning and Natural Language Processing for Data Analysis
Machine Learning and Natural Language Processing for Data Analysis
Our team is applying natural language processing (NLP) to analyze aviation accident narratives, which often contain details not captured in coded data. By training neural network models on thousands of reports, we aim to improve accident causation analysis and address limitations of code-only approaches.
Unsupervised and supervised machine learning techniques have been applied to inflight loss of control accidents, UAS incidents, and stall/spin accidents.
Previous work led to Dr. Majumdar's PhD dissertation, an MS thesis, a BS honors thesis, a research symposium poster display, and a conference proceeding.
More work is currently in progress.
Collaborators, Relevant Publications, & Funding
Collaborators
Mehedi Hasan, Graduate Assistant, Mechanical Engineering, University of Arkansas
Kacey Haws, Undergraduate Research Assistant, Computer Science, University of Arkansas
Karen Marais, Professor of Aeronautical and Astronautical Engineering, Purdue University
Publications
Hasan, M., Westfall, L., and Majumdar, N. (2025). Analysis of Stall and Spin Accidents Involving General Aviation Fixed-Wing Aircraft. In AIAA AVIATION 2025. Jul 2025. doi: 10.2514/6.2025-3322
Hasan, M. (2025). Analysis and Predictive Modeling of Stall and Spin Accidents in General Aviation Fixed-Wing Aircraft. Graduate MS Thesis. Available online
Haws, K. (2025). Analyzing Unmanned Aircraft System (UAS) Incidents from NASA ASRS Data Using Unsupervised Machine Learning. Undergraduate Honors Thesis. Available online
Haws, K., & Majumdar, N. (2025). Poster: Analyzing Unmanned Aircraft System (UAS) Incidents from NASA ASRS Data Using Unsupervised Machine Learning. 2025 Research Poster Competition. Available online
Majumdar, N. (2023). State-based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots’ Perspectives (Order No. 30499136). ProQuest Dissertations & Theses Global. (2806444500). doi: 10.25394/PGS.22677526.v2
Funding
Purdue University
University of Arkansas, Department of Mechanical Engineering
General Aviation Inflight Loss of Control
General Aviation Inflight Loss of Control
Using a data-driven and human factors approach, this study analyzed causes and contributing factors leading to inflight loss of control, the leading cause of fatal General Aviation accidents. The research focused on aviation accident reports using the National Transportation Safety Board (NTSB) and incidents shared by pilots. The findings helped identify specific maneuvers that may be useful for integrating into the pilot training curriculum to prevent loss of control in the future. This work was funded by the Federal Aviation Administration (FAA) at Purdue University.
Previous work led to Dr. Majumdar's PhD dissertation and MS thesis, 2 journal paper publications, 2 conference proceedings, and 2 FAA technical reports.
Collaborators, Relevant Publications, & Funding
Collaborators
Arjun Rao, Lead Engineer, Boeing
Karen Marais, Professor of Aeronautical and Astronautical Engineering, Purdue University
Publications
Majumdar, N., and Marais, K. (2025). Lessons Learned to Prevent Loss of Control: A Review of Incidents from Pilot Archives. Proceedings of the 23rd International Symposium on Aviation Psychology, 144-149. May 2025. doi: 10.5399/osu/1188
Majumdar, N., and Marais, K. (2025). Human Factors in General Aviation Loss of Control: Survey of Pilot Experiences. Journal of Air Transportation, Vol. 33, No. 1, pp. 57-68 doi: 10.2514/1.D0432
Majumdar, N., and Marais, K. (2022). A Survey of Pilots’ Experiences of Inflight Loss of Control Incidents and Training. In AIAA AVIATION 2022 (p. 3778). Jul 2022. doi: 10.2514/6.2022-3778
Majumdar, N., Marais, K., and Rao, A. (2021). Analysis of General Aviation Fixed-wing Aircraft Accidents Involving Inflight Loss of Control Using a State-based Approach. Aviation, 25(4), 283-294. doi: 10.3846/aviation.2021.15837
Majumdar, N. (2023). State-based Analysis of General Aviation Loss of Control Accidents Using Historical Data and Pilots’ Perspectives (Order No. 30499136). ProQuest Dissertations & Theses Global. (2806444500). doi: 10.25394/PGS.22677526.v2
Funding
Federal Aviation Administration (FAA), Center of Excellence (PEGASAS)
Maintenance-Related Commercial Aviation Accidents and Incidents
Maintenance-Related Commercial Aviation Accidents and Incidents
Maintenance issues in commercial aviation are one of the major causes of flight delays and cancellations. This study addressed the following questions: (1) What is the status of maintenance errors in commercial aviation accidents? (2) What issues dominate, and has that changed? Are new issues arising, for example, as composites become more prevalent? (3) What proactive and reactive methods are used to identify aviation maintenance risks, maintainer errors, and conditions? This study discusses tools to reduce maintenance errors and analyzes the NTSB reports for six maintenance-related accidents and 25 serious incidents in U.S. commercial aviation from 2009–2018.
Previous work led to a conference proceeding.
Collaborators, Relevant Publications, & Funding
Collaborators
Divya Bhargava, Instructional Assistant Professor, Texas A&M University
Tracy El Khoury, Lead Specialist, Oliver Wyman Vector
Karen Marais, Professor of Aeronautical and Astronautical Engineering, Purdue University
Vincent Duffy, Professor of Industrial Engineering, Purdue University
Publications
Majumdar, N., Bhargava, D., El Khoury, T., Marais, K., & Duffy, V. G. (2023). An Analysis and Review of Maintenance-Related Commercial Aviation Accidents and Incidents. In International Conference on Human-Computer Interaction 2023 (pp. 531-547). Lecture Notes in Computer Science, vol 14029. Springer, Cham. Jun 2023. doi: 10.1007/978-3-031-35748-0_36
Funding
Purdue University
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