Browse Topic: Calibration
Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents an integrated vibro-acoustic methodology enabling early-stage prediction of loudspeaker performance in real vehicle conditions. The approach combines electromechanical characterization, a hybrid loudspeaker calibrated model valid across the audible range and coupled FEM/BEM/SEA simulations to capture the loudspeaker response in the vehicle’s cabin considering door-installation effects and cabin acoustics. The method is validated experimentally on a rear-door loudspeaker installed in a production vehicle, showing strong correlation with measured SPL. A final application case demonstrates its capability to assess the impact of alternative speaker mounting positions during the design phase.
As acoustic requirements for NVH trim components become increasingly constrained by mass, cost, and sustainability targets, traditional approaches to inner dash design based on spatially averaged Transmission Loss (TL) metrics are reaching their practical limits. In fully built vehicles, the acoustic performance of the inner dash is governed by its global insulation capability but also by strong spatial heterogeneity and its interaction with spatially distributed noise sources such as the power unit, gearbox, and tyre-road excitation. This paper presents a test-based methodology for the spatial optimisation of inner dash acoustic performance using reciprocal holography. By applying a calibrated sound power source within the vehicle cabin and measuring the reciprocal response in the engine bay and wheel-arch regions, a high-resolution spatial Transmission Loss “hologram” of the inner dash is obtained under in-situ conditions. The resulting spatial data enables the identification of localised acoustic weak points that are not observable using conventional testing methods. To bridge the gap between passive component characterisation and real-world vehicle operation, the spatial TL hologram is subsequently evaluated using representative operational source sound power data to prioritise acoustically relevant regions. This enables the transmitted acoustic energy to be evaluated under realistic driving conditions. The holographic data is then coupled with a parametric acoustic model of the inner dash system, allowing localised mass redistribution to be optimised using a genetic algorithm while respecting packaging and manufacturing constraints.
Qualification of new aerospace alloys requires extensive mechanical testing to capture anisotropy and ensure reliable performance under complex loading conditions. This process is costly and time-consuming, particularly with emerging manufacturing routes such as additive manufacturing. Advanced yield surface prediction offers a route to reduce test campaigns by linking microstructural features to macroscopic constitutive models. In this work, Digimat is employed as a multi-scale material modeling platform to generate yield surfaces of polycrystalline metals using computational homogenization. Representative volume elements (RVEs) are constructed from experimental texture and grain morphology data, and their response under multiaxial loading is simulated using a crystal plasticity framework. The computed yield loci are then fitted with phenomenological functions (e.g. Yld2000-2D), enabling calibration of anisotropic yield models from virtual testing. As a case study, an AA6016-T4 sheet with strong cube texture is modeled and validated against experimental data, including yield stresses and Lankford coefficients in multiple directions. The predictive capability of the approach is further assessed through a cup drawing simulation in Simufact, where earing behavior is accurately reproduced. These results demonstrate that digital yield surface prediction can capture anisotropic plasticity and provide reliable input to forming simulations while significantly reducing experimental requirements. This capability lays the foundation for more efficient alloy qualification, with direct impact on fatigue and damage tolerance modeling in aerospace applications.
Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in overly conservative policies that struggle with dynamic disturbances. To overcome this, we propose a novel adaptive control architecture that actively perceives and reacts to instantaneous perturbations. First, we train an optimal outer-loop policy, then replace its reliance on ground-truth disturbance data with a Residual Dynamics Predictor (RDP). The RDP estimates the external forces and moments acting on the aircraft in flight online using only the history of states and control actions. For seamless hardware transfer, we introduce a data-efficient linear calibration bridge and an online thrust correction mechanism that align the simulated latent space with reality using mere seconds of flight data. Real-world validations on a Crazyflie micro-quadrotor demonstrate that our adaptive controller significantly outperforms baselines, maintaining precise trajectory tracking under severe uncertainties including mass variations, asymmetric payloads, and dynamic slung loads.
The bird strike performance of the flight critical components of a rotorcraft is to be proved. The study investigates the bird strike performance of the cowling structure through experiments and simulations by considering a Building Block Approach. Based on this approach, bird impact tests on a rigid plate and composite panels are performed to validate Smoothed Particle Hydrodynamics method (SPH) bird model and composite material model in LS-DYNA. The composite material properties are obtained from the coupon level test results. After the composite material model is calibrated and validated, the bird strike performance of the cowling structure at critical locations is assessed. A good correlation between the experimental and numerical results was obtained at coupon, sub-component and component levels. The developed composite material modeling technique and validated bird models may be used in showing bird resistances of other airframe components of similar structure of the rotorcraft.
This paper presents an integrated simulation workflow for aircraft seat development that combines (i) structural dynamics and certification load cases, (ii) occupant comfort and living-space assessment using finite-element digital humans, and (iii) airbag folding, deployment, and calibration using a coupled gas-dynamics solver suited to early-time transients. The workflow is built around a single manufacturing-aware, as-built seat model that is reused across comfort, certification, and restraint-system studies, allowing design iterations to move upstream before design freeze. Each stage is paired with validation or industrial case examples, and the airbag-calibration process is accelerated through reduced-order modeling (ROM) of parameter identification. The result is a practical virtual-seat-development methodology that is sufficiently predictive to de-risk physical testing while remaining fast enough for concept iteration and late-stage compliance support.
Lane centering is a critical active safety feature whose effectiveness depends on robust design and validation across diverse driving conditions. This paper presents the development of a Lane Centering Controller (LCC) using a structured model-based design workflow in MATLAB and Simulink. A kinematic bicycle model was employed to simulate vehicle dynamics and evaluate an angle based steering controller integrating both feedforward and feedback control paths. The controller was tested across multiple road geometries and speeds up to 65 mph to ensure tracking consistency and stability under nominal and perturbed conditions. Perception noise models for lane curvature and curvature rate were extracted from onboard camera data under controlled conditions, revealing Gaussian characteristics. No filtering was applied, allowing direct evaluation of the controller’s inherent robustness to raw signal variability. The LCC maintained a peak lateral offset within ±0.35 m and lateral jerk within ±9 m/s3, while respecting a steering comfort limit of ±3 Nm, thereby satisfying both functional and driver comfort requirements. The MATLAB based workflow also facilitated requirement traceability and automated test case validation, enabling quantitative comparisons of control response across different speeds and curvature transitions. These results establish a clear link between simulation fidelity and control performance, providing a reference for calibration transfer in higher fidelity environments. The paper concludes with discussion on extending the algorithm to real time Hardware-in-the-Loop (HIL) and Vehicle-in-the-Loop (VIL) platforms, demonstrating scalability toward full vehicle implementation and providing a validated framework for future high speed lane centering development.
The timing of video recordings, along with the spatial positioning of objects, is a fundamental parameter for calculating the speed time history. If the task involves determining the average speed of an object moving at approximately constant speed, it may be acceptable to average the speed over several to a dozen frames, using the fps (frames per second) parameter as the basic time unit.. However, if the objective is to compute speed from individual frames, the reliability of the timing becomes crucial. Without access to DVR hardware documentation, proprietary algorithms, or software – and considering the frequent hardware modifications and software updates - the most effective way to solve the problem is through a reverse-engineering approach. This study discusses several aspects of timing analysis, including: (1) making a test recording of a calibrated LED lightboard; (2) analyzing the relationship between the lightboard time and the presentation time stamp (pts) extracted from the file metadata; (3) investigating frame skipping and frame timing errors due to frame rate changes; (4) modeling the composite motion of the rolling shutter and the lightboard LEDs; (5) identifying the DVR’s actual frame capture rate; and (6) compensating the timing of the evidentiary recording. Establishing the timing scheme of the test recording enables reliable speed analysis based on two or three adjacent frames of the evidentiary recording, as well as the determination of the velocity time history over a short segment of the recording.
This paper details comprehensive analysis modeling and analysis supporting the development of the Research Aircraft for eVTOL Enabling techNologies (RAVEN). An isolated rotor model was developed in CAMRAD II, and predictions of rotor performance and rotor aeroelastic stability were generated. The rotor stability predictions are part of assessing airworthiness of the RAVEN vehicle. The performance predictions were used to calibrate the surrogate model for the NASA Design of Rotorcraft (NDARC).
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