OpenFAST: Multidomain Simulation & Control

• OpenFAST is an open-source simulation framework that models aero-hydro-servo-elasto dynamics in wind turbines and provides discrete-action tokenization for robotics.
• Its modular design integrates physical, sensor, and control models by normalizing and tokenizing continuous signals for unified analysis.
• Applications of OpenFAST have demonstrated significant performance gains, including up to 97.5% RMS reduction in tower velocity and enhanced robotic control via transformer pre-training.

OpenFAST is a high-fidelity, open-source software tool and modeling framework designed for the analysis and simulation of physical systems, originally targeting the aero-hydro-servo-elasto-dynamics of wind turbines and subsequently supporting various robotics and embodied control research. It is used extensively in both wind energy research and robotics, enabling rigorous simulation and evaluation of advanced control, reasoning, and embodiment strategies (Fang et al., 4 May 2026, Pamososuryo et al., 2023).

1. Framework Architecture and Core Principles

OpenFAST provides an extensible modular structure enabling the integration of physical, actuation, and sensor models. In wind energy applications, it encapsulates turbine dynamics, aerodynamics, structural flexibility, servo control, hydrodynamics, and coupling between these domains. In embodied AI, its discrete-action tokenizer variant forms the backbone of a unified next-token interface, enabling the transformation of continuous robot motion into compact, model-agnostic discrete representations (Fang et al., 4 May 2026).

The framework supports native, high-dimensional control and sensor streams by normalizing, segmenting, and tokenizing continuous signals. This harmonization allows direct mapping between multimodal robot telemetry and language-model-based action inference, a key enabler of unified Vision-Language-Action (VLA) objectives.

2. Application in Wind Turbine Simulation and Control

OpenFAST has been foundational in validating novel wind turbine tower load reduction strategies, particularly under flexible, soft-soft tower design regimes. Research utilizing OpenFAST employed the NREL 5-MW reference turbine, which was adapted to shift the first side–side tower mode into sub-rated operational speed via significant reduction in wall thickness, yielding modal properties:

• Modal mass: $m_s = 3.62 \times 10^5$ kg
• Modal stiffness: $k_s = 1.7677 \times 10^5$ N/m
• Modal damping: $d_s = 2.4588 \times 10^3$ Ns/m
• First natural frequency: $\omega_{n,s} \approx 0.6963$ rad/s

ElastoDyn and AeroDyn modules are configured to expose and record tower tip deflections, velocities, and generator torques, supporting closed-loop control and detailed output channel analytics (Pamososuryo et al., 2023).

3. Discrete-Action Tokenization in Robotics: OpenFAST Tokenizer

The OpenFAST Tokenizer bridges continuous robot trajectory spaces and transformer-based VLM architectures. It processes one-second segments of robot joint or end-effector data—which may vary in sampling interval, dimension, and embodiment—performing:

1. Frequency-domain Transformation: Applies a discrete Fourier or cosine transform to a $32 \times T$ trajectory window.
2. Coefficient Quantization: Converts floating-point transforms to integer codebook indices.
3. BPE Tokenization: Feeds quantized codes to a standard Byte-Pair Encoding (BPE) vocabulary learner (size: 2048), outputting discrete “action tokens”.

A 1,000,000-sequence dataset, subsampled from major open robot trajectory sources (MolmoAct2-BimanualYAM, SO100/101, DROID, Fractal (RT-1), BC-Z, BridgeData V2), was used for training. Action vocabularies span bimanual and single-arm, joint- or end-effector–space controllers, enabling a backbone-agnostic interface (Fang et al., 4 May 2026).

Dataset	Proportion	Control Mode
MolmoAct2-BimanualYAM	30%	Absolute joint, 30 Hz
MolmoAct2-SO100/101	30%	Absolute joint, 30 Hz
MolmoAct2-DROID	30%	Absolute joint, 15 Hz
Fractal (RT-1)	3.33%	Delta end-effector
BC-Z	3.33%	Delta end-effector
BridgeData V2	3.33%	Delta end-effector

All claims and proportions are verbatim from (Fang et al., 4 May 2026).

4. Algorithms, Losses, and Transformer Integration

OpenFAST enables end-to-end learning for robotics by allowing sequence models to treat action tokens analogously to word or image tokens. During pre-training, the VLM backbone learns action token prediction via standard autoregressive cross-entropy loss $\mathcal{L}_{LM}$. Fine-tuning attaches a DiT-style flow-matching continuous-action expert, which “denoises” stochastic trajectory samples via the loss:

$$\mathcal{L}_{flow} = \mathbb{E}_{a, \epsilon, t} \| m \odot (f_\theta(x_t, t, c) - u^\star) \|_2^2,$$

where $m$ masks padding in the trajectory, $f_\theta$ is the learnable head, $x_t = (1-t)\epsilon + t a$, $k_s = 1.7677 \times 10^5$0, and $k_s = 1.7677 \times 10^5$1 denotes the context.

Per-layer key-value (KV) conditioning projects VLM transformer features (from depth $k_s = 1.7677 \times 10^5$2) into the continuous-action expert using learned adapters, facilitating hierarchical cross-modal grounding, while ensuring parameter and gradient insulation during expert-head optimization (Fang et al., 4 May 2026).

5. Evaluation, Ablation, and Performance Metrics

OpenFAST’s discrete-action tokenization is evaluated primarily via downstream, end-to-end VLA policy metrics. Empirical evidence demonstrates:

• Per-layer KV conditioning outperforms alternatives (hidden-state or per-head KV) for robot control on the LIBERO benchmark.
• Multiple flow-matching samples ($k_s = 1.7677 \times 10^5$3) per chunk improve policy success on standard robotics tasks.
• Pre-training the VLM on Molmo2-ER rather than Molmo2 yields a 6 absolute point gain in discrete-only performance.

Tokenization compresses a one-second, 32-dimensional motion window into a variable-length discrete token sequence, achieving parity with continuous-control baselines across multimodal pretraining and embodied-agent fine-tuning scenarios (Fang et al., 4 May 2026).

6. OpenFAST in Wind Turbine Control Research

OpenFAST is a core platform for the design and evaluation of advanced feedback controllers in flexible wind tower structures. Key contributions include the implementation and validation of Modulation–Demodulation Control (MDC) strategies, which employ:

• Demodulation of sensor signals (e.g., side–side tower velocity) into two orthogonal channels at 1P frequency.
• SISO LTI controllers in the demodulated domain: e.g., integrator or low-pass “damped” inverted-notches.
• Remodulation to synthesize generator torque commands ($k_s = 1.7677 \times 10^5$4) for feedback application.

Simulation with OpenFAST provides direct evidence of 20–40 dB reduction in 1P spectral density for tower deflection/velocity, and up to 97.5% RMS reduction in side–side velocity under turbulent inflow with MDC2-type controllers. The setup includes explicit parameterization of the turbine, explicit output channels, and direct code integration for controller synthesis—enabling replicable, high-fidelity, closed-loop evaluation (Pamososuryo et al., 2023).

7. Significance and Role in Multimodal Research

OpenFAST serves as a unifying interface for diverse physical control domains. In wind turbine engineering, it enables the systematic, quantitative validation of novel load-reduction and fatigue-mitigation control strategies. In robotics, OpenFAST (as an action tokenizer) is fundamental to enabling Language-Vision-Action dataset and model integration, supporting scalable pretraining and downstream policy optimization on heterogeneous robot platforms via a shared, autoregressive discrete-token interface (Fang et al., 4 May 2026, Pamososuryo et al., 2023).

A plausible implication is that such flexible tokenization and model-agnostic simulation paves the way for further unification of real-world control, simulation, and embodied AI, facilitating research that spans physical, multimodal, and algorithmic frontiers.