KC Engineering/Capabilities

An integrated
technical capability.

Three pillars, one practice. End-to-end wind tunnel testing. Modern ML/AI woven into engineering systems. Data-driven and hybrid control on real hardware. Each is delivered by senior engineering — never sub-contracted.

01 Foundation End-to-end wind tunnel testing

Wind tunnel testing,
delivered end-to-end.

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.

DESIGN · BUILD · TEST · REPORT
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.

Anchored in
2022 Experiments & Computation on Vortical Interactions on Multi-Swept Wing Configurations AIAA AVIATION 2022 Nonlinear Rate-Dependent Flight Damping Derivatives of the Orion Crew Module J. SPACECRAFT & ROCKETS 2016 Cyber-Physical Flexible Wing for Aeroelastic Investigation J. FLUIDS & STRUCT. · V67 2017 Reduced-Order Model from Structural Kinematic Measurements in Stall Flutter J. FLUIDS & STRUCT. · V71
02 Intelligence ML / AI integrated with engineering systems

Modern ML / AI,
fluent in real engineering.

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.

STPOD LSTM / FEEDFORWARD NEURAL NET MULTI-HORIZON PREDICTION SENSOR → REDUCTION → INFERENCE → ACTION
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.

Anchored in
2026 Wave Estimation Tool v1.1 — LSTM + Space-Time POD PRODUCTION TOOL 2017 Reduced-Order Model from Structural Kinematic Measurements in Stall Flutter J. FLUIDS & STRUCT. · V71 2022 Leading-Edge Vortex Dynamics on Finite-Aspect-Ratio Swept Wings J. FLUIDS & STRUCT. 2008 Low-Dimensional Modelling of a Transient Cylinder Wake using Double POD J. FLUID MECH. · V610
03 Authority Data-driven & hybrid control

Controllers that earn
their place on the hardware.

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.

PLANT ESTIMATOR SYSTEM ID CONTROLLER CLOSED-LOOP · PHYSICS + DATA · REAL HARDWARE
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 — synthesis through hardware-in-the-loop.

Anchored in
2014 Closed-Loop Flow Control of a Forebody at High Incidence Angle AIAA J. · 52(7) 2018 Feedback Flow Control — A Heuristic Approach AIAA JOURNAL 2017 Robust LQR Control for Stall Flutter Suppression — Polytopic Approach IFAC-PAPERSONLINE 50(1) 2022 Cycloidal WEC Under Real-Time Dynamic Control — 3D Radiation Model INT. MARINE ENERGY J.
[ 04 ]   UNDERLYING DISCIPLINES

The technical bench
under the practice.

Eight foundational disciplines — every engagement draws from this bench.

001

Experimental Aerodynamics

Subsonic, supersonic, and water-tunnel campaigns — SPIV/PTV, PSP, DIC, force & moment, pressure rake, high-speed schlieren.

SPIV / PTVPSPDICSchlieren
002

Computational Fluid Dynamics

Industrial RANS, hybrid RANS/LES, and DES — CAD-to-mesh through validated unsteady physics extraction on DoD HPCMP resources.

RANS / DES / LESDoD HPCMPMesh design
003

Machine Learning & Surrogates

PyTorch-based feedforward, LSTM, and GRU models for engineering inference. Streamlit tooling, hyper-sweeps, export pipelines.

PyTorchLSTM / GRUStreamlit
004

Reduced-Order Modeling

POD, DMD, SINDy, and Koopman-operator methods. Control-ready, low-dimensional representations of dominant nonlinear physics.

POD / DMDSINDyKoopman
005

Fluid–Structure Interaction

Dynamic stall hysteresis, leading-edge vortex stabilization, gust response, flutter mitigation for flexible aerostructures.

Aero-elasticityGust responseDynamic stall
006

Closed-Loop Control

System ID, state estimation, and feedback synthesis on real-time hardware — PID, MRAC, adaptive, and linear-genetic above 1 kHz.

System IDMRACLabVIEW RT / FPGA
007

Active Flow Control

Synthetic jets, fluidic oscillators, plasma, and high-rate blowing — separation, lift, drag, and shock manipulation.

Synthetic jetsOscillatorsPlasma
008

Technical Advisory

Independent technical review for program offices, R&D leadership, and acquisition teams. De-risk and surface the right questions.

Program reviewRFP shapingR&D strategy
[ 05 ]   ENGAGEMENT MODES

How we work
with clients.

Three modes of engagement that map cleanly onto how real technical programs run.

A — DISCOVERY

Scoped technical study

Fixed-scope investigation. Trade studies, feasibility analyses, design-of-experiments, ML proof-of-capability, or sensitivity sweeps.

TYPICAL 6 — 16 WEEKS

B — PROGRAM

Cooperative R&D

Multi-year cooperative agreements as prime or sub. Wind-tunnel campaigns, hardware, controller deployment, archival record.

TYPICAL 12 — 48 MONTHS

C — ADVISORY

Strategic advisory

On-retainer advisory for program offices and R&D leadership. Independent review, proposal evaluation, on-call subject-matter access.

RETAINER · MONTHLY

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