KC Engineering · Kinetics & Control · Colorado Springs, CO
We deliver end-to-end wind tunnel test campaigns, embed modern machine learning into experimental and engineering systems, and design data-driven and hybrid controllers for mission-critical aerospace, defense, and energy clients.
12+ yrs
Prime contractor, USAFA Aeronautics Research Center
Ph.D.
Electrical & Computer Engineering, University of Wyoming, 2010
JFM 2008
Double POD for transient wakes, J. Fluid Mechanics 610
NASA Orion
Crew-module damping derivatives, J. Spacecraft & Rockets 2023
18 papers
Listed with authors and DOIs: AIAA, JFM, JFS, IFAC, IJFC
[ 01 ] Scale & reach
U.S. Air Force Academy
Hermeus
Atargis Energy
AFOSR
NASA
U.S. Air Force Academy
[ 02 ] Practice
Modern engineering problems live where wind-tunnel evidence, real-time data, and feedback control intersect. KC Engineering is built to deliver across all three, as a single accountable capability.
We treat a test campaign as a single accountable program, not a procurement sequence. The firm owns design of experiments, model design and instrumentation, fabrication, tunnel execution, data acquisition, reduction, uncertainty quantification, and archival reporting.
Operational experience across subsonic, supersonic, and water tunnels. Closed-loop and cyber-physical test architectures when the question is dynamic rather than static.
A / Design
Test plan & DoE
Objective-driven design of experiments, screening sweeps, factor sensitivity, and budget-aware test matrices.
B / Model
Model & instrumentation
CAD, aerodynamic and structural sizing, fabrication path, sensor selection, and integrated DAQ layout.
C / Execute
Tunnel execution
Subsonic, transonic, supersonic, and water-tunnel runs. SPIV, PSP, DIC, schlieren, force/moment, pressure.
D / Report
Reduction & reporting
Validated reductions, uncertainty budgets, archival reports, journal-grade artifacts, deliverable packaging.
We bring neural networks, time-series models, and operator-theoretic methods into the instruments, control loops, and decision pipelines our clients already operate. The goal is a deployable, edge-ready capability, not a notebook demo.
Production examples: a PyTorch + Streamlit wave-elevation estimator using LSTM and Space-Time POD for an ocean-energy client, neural-network surrogates for unsteady aerodynamic measurement, and controller-structure discovery via linear-genetic programming.
A / Surrogates
Neural surrogates
Feedforward, LSTM, and GRU surrogates replacing expensive measurement or simulation chains in production.
B / Reduction
POD · DMD · SINDy
Space-time POD, dynamic mode decomposition, and sparse identification, built for control-readiness.
C / Tooling
Engineering apps
Streamlit, PyTorch, and hyperparameter-sweep pipelines delivered to in-house engineering teams.
D / Deploy
Real-time inference
Model export to LabVIEW RT and FPGA targets, embedded at the edge, never blocking on the cloud.
Controllers that respect the physics and exploit the data: adaptive, model-reference, reduced-order, and hybrid physics+ML architectures, identified from real measurements and deployed onto real hardware at real time scales.
Demonstrated across flow control, aero-elastic stabilization, vortex-shedding regulation, and ocean wave-energy. Synthesized, validated, and signed off in-house.
A / Identify
System identification
Black-box, grey-box, and physics-informed identification from rich experimental data.
B / Reduce
ROM-based control
Controllers synthesized from low-dimensional models: fast, interpretable, and stable.
C / Hybridize
Physics + ML
Hybrid controllers using physics priors with ML residuals: robust and data-efficient.
D / Deploy
Real-time hardware
LabVIEW RT, FPGA, and embedded targets, from synthesis through hardware-in-the-loop.
[ 03 ] Underlying disciplines
The pillars sit on a deep technical bench across fluid mechanics, structures, measurement, and computation. A summary view of what we bring to every engagement.
Illustrative visualization · generic planform · move pointer to orbit
[ 04 ] Selected applications
Representative engagements across defense, aerospace, and ocean energy, illustrating the end-to-end model. Many programs are not listed publicly.
[ 05 ] Insights
A selection of recent research and editorial covering data-driven flow control, cyber-physical methods, and machine learning in the experimental loop.
2026
A production PyTorch + Streamlit estimation tool for ocean wave-energy applications. Multi-horizon prediction of wave elevation from upstream sensor measurements; STPOD preprocessing; Airy / Stokes / JONSWAP / Bretschneider spectra; export to LabVIEW real-time targets.
Client tool, listed for completeness; no public DOI.
2016
Casey Fagley, Jürgen Seidel, Thomas McLaughlin
Journal of Fluids and Structures 67 34-47 (2016) · doi:10.1016/j.jfluidstructs.2016.07.021
The cornerstone paper on the cyber-physical wind-tunnel methodology: emulating in-flight aerodynamic loads on a stationary model and collapsing flight-test feedback into the laboratory. J. Fluid Structures 67:34-47.
2008
Stefan G. Siegel, Jürgen Seidel, Casey Fagley, D. M. Luchtenburg, Kelly Cohen, Thomas McLaughlin
Journal of Fluid Mechanics 610 1-42 (2008) · doi:10.1017/s0022112008002115
The foundational coordinate-transformation paper anchoring the firm’s reduced-order-modeling practice: captures the transient non-linear cylinder wake in a minimum number of modes. J. Fluid Mechanics 610:1-42.
[ 06 ] Practice
We stay small, deeply technical, and unencumbered by org chart. The work we take on demands first-principles thinking, real lab time, real code, and a publishable standard of proof.
