KINETICS & CONTROL  ·  COLORADO SPRINGS, CO

Engineering
complex systems
for a changing world.

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.

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[ 01 ]   SCALE & REACH

Fifteen years advancing the state of the art in experimental aerodynamics, machine learning, and dynamic control.

15+ Years bridging experimental fluid dynamics, controls, and data-driven modeling.
12+ Years as prime contractor to the U.S. Air Force Academy Aeronautics Research Center.
50+ Peer-reviewed publications across AIAA, JFM, J. Fluids & Structures, IFAC, and IJFC.
20+ Cadet, student, and post-doctoral researchers mentored into authored publications.

Trusted by

  • USAFA
  • AFOSR
  • AFRL
  • NASA
  • DARPA
  • ONR
  • NSF
  • Lockheed Martin
  • Atargis Energy
  • Autodesk
  • UCCS
  • Notre Dame
[ 02 ]   PRACTICE

Three connected pillars,
one integrated firm.

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.

01 Foundation End-to-end wind tunnel testing

Wind tunnel testing,
delivered end‑to‑end.

We take a test campaign from the napkin to the archival report. Design of experiments, model design and instrumentation, hardware fabrication, tunnel execution, data acquisition, uncertainty quantification, and the publishable record — all under one roof.

Subsonic, supersonic, and water tunnel experience. Cyber-physical and hardware-in-the-loop test architectures when the question is dynamic.

FORCE / MOMENT BALANCE PRESSURE TAPS · SPIV PLANE SCHLIEREN / DIC RT DAQ · LABVIEW CONTRACTION TEST SECTION DIFFUSER DESIGN · BUILD · TEST · REPORT
01 / DESIGN

Test plan & model

DoE, CAD, structural & aerodynamic sizing, fabrication path, instrumentation layout.

02 / INSTRUMENT

Measurement stack

Force/moment, pressure rake, SPIV/PTV, PSP, DIC, schlieren, high-speed video, IR.

03 / EXECUTE

Real-time DAQ

LabVIEW RT, FPGA-paced acquisition, in-run validation, automated angularity correction.

04 / DELIVER

Archival output

Reduced datasets, uncertainty budgets, validated reports, journal-quality publications.

02 Intelligence ML / AI integrated with engineering systems

Modern ML/AI,
fluent in real engineering.

We integrate neural networks, time-series models, and operator-theoretic methods into the instruments, control loops, and decision pipelines our clients already operate. The goal is not a demo — it is a deployable capability sitting next to a transducer.

Examples from our shipping work: real-time wave-elevation prediction with LSTM + Space-Time POD, neural-network surrogates for unsteady aerodynamic data, and adaptive controller structure discovery via linear-genetic programming.

SENSOR ARRAY RT MEASUREMENT UPSTREAM FIELD CFD SNAPSHOTS FLIGHT-TEST LOG STPOD SPACE-TIME POD SVD basis LSTM / FEEDFORWARD NEURAL NET PyTorch · multi-horizon MULTI-HORIZON PREDICTION + CONTROL COMMAND · > 1 kHz SENSOR → REDUCTION → INFERENCE → ACTION
01 / SURROGATES

Neural surrogates

Feedforward, LSTM, and GRU models replacing expensive simulation or measurement chains.

02 / REDUCTION

POD · DMD · SINDy

Space-time POD and Koopman observables built for control-readiness, not just visualization.

03 / TOOLING

Engineering apps

Streamlit, PyTorch, and hyperparameter-sweep pipelines for in-house engineering teams.

04 / DEPLOY

Real-time inference

Model export to LabVIEW RT and FPGA targets — embedded at the edge, not in the cloud.

03 Authority Data-driven & hybrid control

Controllers that earn
their place on the hardware.

We design 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 energy-conversion applications. Synthesized, validated, and signed off in-house.

PLANT Aero / Hydro / FSI STATE ESTIMATOR ROM + ML residual SYSTEM ID DMD / SINDy / Koopman CONTROLLER MRAC · adaptive · LGP CLOSED-LOOP · PHYSICS + DATA · REAL HARDWARE
01 / IDENTIFY

System identification

Black-box, grey-box, and physics-informed identification from rich experimental data.

02 / REDUCE

ROM-based control

Controllers synthesized from low-dimensional models — fast, interpretable, and stable.

03 / HYBRIDIZE

Physics + ML

Hybrid controllers using physics priors with ML residuals — robust and data-efficient.

04 / DEPLOY

Real-time hardware

LabVIEW RT, FPGA, and embedded targets — synthesis through hardware-in-the-loop.

[ 03 ]   UNDERLYING DISCIPLINES

The technical bench
under the practice.

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.

Full capability detail
001

Experimental Aerodynamics

Subsonic, supersonic, and water-tunnel campaigns — instrumentation through analysis.

SPIV / PTVPSPDICSchlieren
002

Computational Fluid Dynamics

RANS, hybrid RANS/LES, and DES of complex geometries on DoD HPCMP supercomputing.

RANS / DES / LESDoD HPCMPMesh design
003

Machine Learning & Surrogates

PyTorch-based feedforward, LSTM, and GRU models for engineering inference and prediction.

PyTorchLSTM / GRUStreamlit
004

Reduced-Order Modeling

POD, DMD, SINDy, and Koopman-operator methods for control-ready model extraction.

POD / DMDSINDyKoopman
005

Fluid–Structure Interaction

Aero-elastic phenomena: dynamic stall, leading-edge vortex stabilization, gust, flutter.

Aero-elasticityGust responseDynamic stall
006

Closed-Loop Control

System ID, state estimation, and adaptive/MRAC controllers above 1 kHz on real hardware.

System IDMRACLabVIEW RT / FPGA
007

Active Flow Control

Synthetic jets, fluidic oscillators, and plasma actuators across the subsonic-to-transonic regime.

Synthetic jetsOscillatorsPlasma
008

Technical Advisory

Independent review for program offices, R&D leadership, and acquisition teams.

Program reviewRFP shapingR&D strategy
[ 04 ]   SELECTED APPLICATIONS

Where the practice
has been proven.

Representative engagements across defense, aerospace, and ocean energy — illustrating the end-to-end model. Many programs are not listed publicly.

All applications
SUBSONIC CONFIGURATION TEST
PILLAR 01WIND TUNNEL · END-TO-END

Subsonic Wind-Tunnel Testing for a High-Speed Flight Vehicle

Multi-entry test campaign on a scaled flight-vehicle model — Mach sweeps, angle-of-attack and sideslip databases, configuration deltas, and in-ground-effect.

VERTICAL STAB REMOVED TAILLESS STABILITY — BIO-INSPIRED EMPENNAGE
PILLAR 01 · 03STABILITY & CONTROL

Tailless Aircraft Stability with a Bio-Inspired Rotating Empennage

Removed the vertical stabilizer from an F-16 baseline and assessed flight stability with a rotating-empennage control concept — characterizing yaw authority for stealth-driven configurations.

UPSTREAM SENSORS PREDICTED η(t+τ) LSTM + STPOD REAL-TIME WAVE ESTIMATION · MULTI-HORIZON
PILLAR 02ML / AI INTEGRATION

Real-Time Wave Elevation Prediction with LSTM + STPOD

A PyTorch+Streamlit estimation tool predicting wave elevation seconds in advance from upstream sensor measurements — deployed alongside a wave-energy converter control loop.

TunnelSense ControlActuate CYBER-PHYSICAL FLUID DYNAMICS
PILLAR 01 · 03HARDWARE-IN-THE-LOOP

Closed-Loop Cyber-Physical Wind-Tunnel Testing

A wind-tunnel methodology emulating integrated aerodynamic forces in real time — replicating in-flight dynamics on a stationary model.

DATA-DRIVEN FLOW CONTROL
PILLAR 02 · 03REDUCED-ORDER CONTROL

Data-Driven Closed-Loop Flow Control

Low-dimensional, data-driven models of transient flow phenomena enabling adaptive feedback control — published foundational work in J. Fluid Mechanics.

AERO-OPTICS · DIRECTED ENERGY
PILLAR 01 · 02WAKE CHARACTERIZATION

Aero-Optical Wake Characterization

Coupled experimental–computational study of optical aberration through a turbulent wake — advancing closed-loop adaptive optics for in-flight beam control.

[ 05 ]   INDUSTRIES

One technical fabric,
across many sectors.

The same toolkit — wind-tunnel measurement, ML/AI integration, data-driven control — cuts across the markets we serve. Eight focus areas.

/ 01

Defense AerospaceTailless · Stealth · Combat air

/ 02

High-Speed FlightSubsonic / transonic configuration

/ 03

Government R&DAFOSR · DARPA · ONR · NASA

/ 04

Higher EducationCadet research · Graduate mentorship

/ 05

Ocean RenewablesWave energy · Hydrokinetics · ML

/ 06

Autonomous SystemsUAS · Bio-inspired · Sensing

/ 07

Directed EnergyAero-optics · Beam control

/ 08

Industrial FlowDiagnostics · Process optimization

[ 06 ]   INSIGHTS

Engineering thought,
at journal depth.

A selection of recent research and editorial — covering data-driven flow control, cyber-physical methods, and machine learning in the experimental loop.

All insights
η(t + τ) — multi-horizon prediction
ML / AISTREAMLIT TOOL

Wave Estimation with LSTM + Space-Time POD

Field notes from a production PyTorch + Streamlit estimation tool: multi-horizon wave elevation prediction, STPOD preprocessing, and exporting to a real-time controller.

log(E_modes)
JOURNALJFM · V610

Low-dimensional modelling of a transient cylinder wake using double POD

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.

α° — angle of attack
JOURNALJ. FLUIDS & STRUCT. · V67

Cyber-physical flexible wing for aeroelastic investigation of stall and classical flutter

The cornerstone paper on the cyber-physical wind-tunnel methodology — a stationary model emulating in-flight aerodynamic loading in real time, collapsing flight-test feedback into the laboratory.

[ 07 ]   PRACTICE

Built for engineers who refuse the easy problem.

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.

Get in touch
01Technical excellence over volume of outputFirst principles
02We publish what we ship — peer-reviewedAIAA · JFM · JFS
03Direct mentorship; no proxy layersPI / CEO access
04Mission-driven work, mission-driven clientsUSAFA · NASA · DARPA
05Cross-discipline by default — fluids · ML · controlIntegrated practice
[ NEXT ]

Let's solve
what's next.

A defined scope, an early concept, or a problem you're not yet sure how to frame — we're one call away.

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