About
Derek Hollenbeck earned his Ph.D. in Mechanical Engineering in 2023 at the University of California, Merced. Derek has research interests in digital twins, fluid mechanics, controls, dynamics, fractional calculus, and cyber physical systems using small unmanned aerial systems (sUAS) with applications in leak detection, localization and quantification of methane emissions. He is passionate about working on difficult problems through the lense of smart sensing that intersect real-world applications and foundational research concepts.
Experience
- Lead the development and implementation of technologies for making measurements of methane emissions via multi-rotor sUAS, manned aircraft, and satellite;
- Mentored undergraduate team in the development of a relatively low-cost methane emission sUAS measurement platform;
- Lead and co-developed a bio-gas engine system with a team of undergraduates for utilizing unpurified bio-gas for electrical power generation.
- Oversee the daily operations of the Center for Methane Emission Research and Innovation, including managing research projects, supervising graduate students and research staff, and coordinating collaborative activities;
- Researched and developed localization algorithms using Digital Twins for point source emissions in an Oil and Gas setting.
Projects
A UAV platform using a digital twin and observability Gramian to optimally sense and estimate methane emission sources.
- Tools: sUAS, Python, Gaussian Plume Model, Tikhonov Regularization
- Develops a low-cost sUAS for mobile methane sensing and source estimation.
- Uses a Gaussian plume model as a digital twin for real-time simulation of methane dispersion.
- Implements an optimal sensing path based on the observability Gramian to maximize estimation accuracy.
- Estimates emission source location and strength using a Source Receptor Matrix (SRM) method.
A cognitive digital twin that monitors battery health (SOC, RUL) to ensure safe landings for long-endurance VTOL drones.
- Tools: Digital Twin, Python, VTOL sUAS, Coulomb Counting
- Develops a Cognitive Battery Monitoring System (CBMS) for a hybrid VTOL fixed-wing sUAS.
- Employs a Digital Twin model to behavior-match battery discharge curves.
- Estimates battery State of Charge (SOC) and Remaining Useful Life (RUL) in real-time.
- Provides early warning recommendations for safe landings on long-endurance missions.
Publications
Google Scholar ORCID- S. Giri, D. Hollenbeck, R. Krzysiak and Y. Chen, “PHANTOM: Physics-informed Hyperspectral Adversarial Network for Transformer-Optimized Methane Detection”, Submitted, American Control Conference (ACC 2026).
- D. Hollenbeck, R. Krzysiak, et al., “Developing An Optimal Mobile Measurement sUAS using Digital Twins and the Observability Gramian”, International Conference on Control, Mechatronics and Automation (ICCMA 2025).
- R. Krzysiak, et al., “Modeling and Control of a Prescribed Fire with UAVs as Sensors and Actuators,” International Conference on Control, Mechatronics and Automation (ICCMA 2025).
- S. Giri, R. Krzysiak, et al., “Aviris-Ng-Like Smart Virtual Remote Sensing via Spectra-Aware Physics Informed Gans”, ASME 2025 International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference (IDETC/CIE 2025).
- D. Hollenbeck, D. An, R. Krzysiak et al., “Towards Cognitive Battery Monitoring on Hybrid VTOL Fixed-Wing sUAS with Maximized Safe Endurance”, International Conference on Control, Mechatronics and Automation (ICCMA 2023).
- D. An, R. Krzysiak, et al., “Long Endurance Site-Specific Management of Biochar Applications Using Unmanned Aircraft Vehicle and Unmanned Ground Vehicle”, IFAC-PapersOnLine, 56.2 (2023): 8908-8913.
- D. An, R. Krzysiak, et al., “Battery-health-aware UAV mission planning using a cognitive battery management system”, IEEE International Conference on Unmanned Aircraft Systems (ICUAS 2023).
Skills
CAE Software
Fusion 360
Autodesk
Paraview
OpenFOAM
Languages and Databases
MATLAB
Simulink
Python
Unmanned Systems
Ardupilot
MissionPlanner
QGroundControl
Education
University of California, Merced
Merced, USA
Degree: Ph.D. in Mechanical Engineering
GPA: 3.98
Completed: May 2023
- Smart sensing with Digital Twins
- Detection, localization, and quantification of methane emissions with sUAS
- Emphasis with fluids, dynamics, and controls
Research Focus: