Intelligent Designs for Demanding Operations
Numeric Engineering Inc. designs and deploys instrumented, edge-intelligent systems for Disrupted, Degraded, Intermittent, and Low-Bandwidth (DDIL) environments. It integrates physical instrumentation, physics-based models, edge computing, and artificial intelligence to support operational decision-making, structural monitoring, and autonomous functions in harsh, communications-constrained settings.
Texas Board of Professional Engineers Firm Registration No. 20229
Designs and deploys integrated measurement systems for offshore and other high-consequence assets, including sensor specification, deployment, and measurement architecture design.
Deploys oceanographic and meteorological sensors including ADCPs, wave radar, and wind monitoring devices.
Integrates GNSS, motion reference units, inclinometers, and accelerometers for dynamic asset tracking.
Implements load-cell and tension-link measurement systems for operational assets.
Deploys hardwired and wireless instrumentation for process and pressure monitoring.
Applies strain-gauge instrumentation where conventional load cells are impractical.
Establishes real-time measurement, validation, synchronization, and edge-processing architectures to deliver trusted engineering data.
Acquires multi-channel data across analog, digital, and network-based devices with precise synchronization.
Conditions, filters, validates, and logs incoming signals close to the asset.
Interfaces field data with SCADA, PI systems, and higher-level applications.
Processes and buffers real-time data locally to ensure continuity under degraded connectivity.
Secures data transmission to on-premise or cloud infrastructure.
Performs engineering analysis and develops deployable models for structural response, cumulative fatigue, and dynamic operating conditions.
Analyzes stress, displacement, and cumulative fatigue damage using physics-based models.
Develops computational representations derived from high-fidelity simulations for real-time execution.
Builds physics-based twins for real-time operational load and stability monitoring.
Generates synthetic data from finite element models to train predictive algorithms.
Utilizes video-based and indirect measurement methods to track parameters in operational environments where direct physical instrumentation is impractical.
Applies patented video-based tracking to estimate six degrees-of-freedom motion.
Integrates machine learning, nonlinear optimization, and control methods to provide real-time state estimation, predictive analysis, and autonomous functions.
Estimates and predicts asset states under limited bandwidth and degraded communications.
Deploys real-time operational intelligence functions for mission-critical applications.
Oil & Gas · Marine · Ports & Harbors · Industrial · Defense · Aerospace
Offshore · Oil & Gas · Client: Offshore Operators
Fuses instrumentation, real-time data acquisition, physics-based models, and AI into a containerized, edge-resident system. Deployed across 10 deepwater assets to deliver sustained autonomous intelligence in harsh, communications-constrained environments.
Marine · Client: Floating Production Facility (FPF) Operators
Implemented a physics-based digital twin to assist offshore personnel in maintaining safe operating load conditions on floating production facilities by processing real-time sensor data to determine optimum ballast tank levels.
Defense
Developed and deployed a patented computer vision tracking system capable of estimating six degrees-of-freedom motion in operational environments, combining calibrated visual references, geometric reconstruction, and model-based interpretation.
Industrial
Combined platform motion tracking with high-fidelity physics-based models to estimate cumulative fatigue damage in critical structural components, leveraging finite element analysis synthetic data to train predictive models.