Bureau Veritas is a company specialized in testing, inspection and certification founded in 1828. It operates in a variety of sectors, including Building & Infrastructure (27% of revenue), Agri-food & Commodities (23% of revenue), Marine & Offshore (7% of revenue), Industry (22% of revenue), Certification (7% of revenue) and Consumer Products (14% of revenue). Bureau Veritas is present in 140 countries through a network of over 1,500 offices and laboratories, and more than 78,000 employees. Bureau Veritas generated €5.1 billion in revenue in 2019. Didier Michaud-Daniel has been the CEO of Bureau Veritas since March 2012.
A benchmark study of 10 different numerical methods for ship motion and load assessment is presented. Pitch motions and midship vertical bending moments are compared to model test results for a containership at zero speed in head regular waves. The wave steepness is varied from 2.1% to 10.5%. The model tests show that pitch and the vertical bending moment (VBM) display nonlinear behavior even for low-steepness waves. It is demonstrated that computational fluid dynamics (CFD) methods can reproduce the ship responses with good accuracy, even in very steep waves, involving green water and parts of the ship going in and out of water. Weakly nonlinear potential-theory methods tend to overestimate the pitch motions and the sagging moments as the wave steepness increases. For the vertical bending moment in steep waves, the 3D panel methods did not give significantly better results than those obtained with the nonlinear strip theories.
Designing floating wind turbine systems requires integrated load assessments (ILA) using fully coupled hydro-servo-aero-elastic models. In most cases, floater hydrodynamics are represented using potential-flow models for mooring system design and motion estimation, while the floater itself is typically assumed to behave as a rigid body. However, this assumption can significantly affect tower eigenfrequency calculations, especially for large floaters. In this study, we investigate these effects using in situ sensor data from the Zefyros 2.3 MW spar wind turbine. We present a methodology to accurately determine the tower's eigenfrequencies. A rigid-floater model without added mass leads to an average error of 65 % for the first tower mode relative to measurements. Including hydrostatic added mass reduces the error to 40 %. Further incorporating floater flexibility decreases the error to 4.3 %, and accounting for blade flexibility lowers it to just 2.8 %. These discrepancies highlight the importance of refining the hydro-servo-aero-elastic model to align with eigenfrequencies derived from finite-element hydro-structural analyses. We present potential model adjustments, assess their impacts, and demonstrate the updated validation process.
The phase-velocity method is widely used to generate focused waves for studying extreme waves and their interactions with marine structures. However, this method utilizes only the steady-state components of generated wave trains, while the wavefront, which contains complex transient components, is ignored. In this paper, we propose a new method for generating focused waves based on the wavefront velocity, defined as the velocity of the maximum wave in the wavefront zone. Unlike the phase-velocity method, our approach accounts for both the steady-state and wavefront components of transient waves. Consequently, the maximum waves of transient wave components are superimposed to form focused waves. We derive an analytical transient solution for focused waves using the wavefront-velocity method, enabling rapid investigation of the evolution of this type of focused waves. Furthermore, we implement the wavefront-velocity method in a numerical wave flume to generate focused waves. Compared with the phase-velocity method, the wavefront-velocity method produces a larger focused-wave amplitude, a shorter focusing time, and reduced long-wave reflections from the ends of wave flumes. It provides an alternative approach for generating extreme waves, thereby holding significant value for investigating the interactions between extreme waves and marine structures.
Fibrocytes (CD34⁺/CD45⁺) support ECM-renewal and wound remodeling yet are rarely targeted by cosmetic-grade actives [1–3]. I present a compact discovery funnel that links (i) information-preserving library triage with ADME/drug-likeness filters, (ii) ΔSASA-aware end-point energetics layered on MM/GB(P)SA, (iii) curvature- and pose-constrained pharmacophoric ODEs, and (iv) a GHZ-calibrated hybrid quantum learner (QAOA⊗PQC). Phytochemical libraries from ZINC/DrugBank/PubChem are pared with SwissTargetPrediction/SwissADME plus QED and synthetic-accessibility to retain chemotype diversity while enforcing developability [11,22–24,29,31–33]. Binding assessment augments docking and MM/GBSA with a linear non-polar term, ΔGₙₚ=γΔSASA, restoring hydrophobic burial often underweighted by end-point methods [12–15,67–71]. Pose evolution is regularized by pharmacophore constraints in concave, hydrated pockets and integrated with SO(3)-respecting moves before MD-refinement [12–15,39,42,49–54,72]. Quantum training is gated by a digitized-adiabatic GHZ-suite: prepared GHZ-states, von Neumann entropy, and entanglement-witness tests set acceptance thresholds prior to learning [125,127,131–132,146]. The downstream QAOA⊗PQC regressor/classifier uses SPSA on Qiskit/Aer, with cross-entropy benchmarking as a hardware–software check [109–111,136,140–200]. Applied to >12,000 phytochemicals against MMP-1, neutrophil elastase, and the elastin receptor complex (EBP/NEU1/PPCA) [4–8,154,169–172], the funnel yielded ~1,200 diverse seeds, 220 redocked candidates, and 64 MD/MM-PBSA–refined leads. A botanical triad (Ginkgo biloba, Punica granatum, Morus alba) achieved in-silico potencies of 0.9–3.4 μM (MMP-1) and 1.5–5.8 μM (elastase), with Bliss/Chou–Talalay synergy CSI=1.37±0.09 [115–116]. On held-out tests, the hybrid quantum–physics model gave the lowest error (MSE≈0.52; parity R²≈0.99), surpassing ridge, calibrated physics-only, raw MM-PBSA, and docking-only baselines [29–31,70–71,101,140–200]. Chemotype-bucket analysis showed ~22%±3% lower weighted MSE versus non-hybrid comparators, and adding Stemaflex™ peptide–polyphenol cofactors further reduced error, aligning with ERC-linked biology and fibrocyte-relevant network improvements [11,31,70–71,119–121,124,154,159–160,169–172].
Concern for the environmental and operational loads from waves, wind, current, ice, slamming, sloshing, green water, weight distribution, and other operational factors. Consideration shall be given to deterministic and statistical load predictions based on model experiments, full-scale measurements and theoretical methods. Uncertainties in load estimations shall be highlighted. The committee is encouraged to cooperate with the corresponding ITTC committee.
