DFT is vital for materials discovery, and at the base of extensive molecular and materials databases, chemical reaction predictions, and machine learning potentials. The widespread use of DFT in chemistry and materials science aims for "chemical accuracy," but this is limited by the unknown exchange and correlation (XC) functional. A meta-GGA, the restored regularized strongly constrained and appropriately normed, r r2SCAN XC functional, fulfils 17 exact constraints of the XC energy. r2SCAN still appears inadequate at predicting material properties of strongly correlated compounds. Inaccuracies of r2SCAN arise from functional and density-driven errors, linked to the self-interaction error. We introduce a new method, r2SCANY@r2SCANX, for simulating transition metal oxides accurately. r2SCANY@r2SCANX utilizes different fractions of exact exchange: X to set the electronic density, and Y to set the energy density functional approximation. r2SCANY@r2SCANX addresses functional-driven and density-driven inaccuracies. Using just one or two universal parameters, r2SCANY@r2SCANX enhances the r2SCAN predictions of the properties of 18 correlated oxides, outperforming the highly parameterized DFT+U method. The O2 overbinding in r2SCAN ( 0.3 eV/O2) reduces to just 0.03 eV/O_2 with any X in r2SCAN10@r2SCANX. Uncertainties for oxide oxidation energies and magnetic moments are reduced by r2SCAN10@r2SCAN50, minimizing r2SCAN density-driven errors. The computationally efficient r2SCAN10@r2SCAN is nearly as accurate as the hybrid r2SCAN10 for oxidation energies. Thus, accurate energy differences can be achieved by rate-limiting self-consistent iterations and geometry optimizations with the efficient r2SCAN. Subsequently, expensive hybrid functionals can be applied in a fast-to-execute single post-self-consistent calculation, as in r2SCAN10@r2SCAN, which is 10 to 300 times faster than r2SCAN10.
Spatial consistency is a fundamental property of the visual world and a key requirement for models that aim to understand physical reality. Despite recent advances, multimodal large language models (MLLMs) often struggle to reason about 3D geometry across multiple views. Rather than asking models to describe scene attributes, we introduce a more challenging task: given two views of the same scene, identify the object that violates 3D motion consistency. We propose a simple and scalable method for generating realistic, spatially inconsistent image pairs from multi-view scenes, enabling systematic evaluation of this capability. Our results show that state-of-the-art MLLMs significantly underperform human observers and exhibit substantial variability across different scene attributes, revealing a fragile and incomplete understanding of 3D structure. We hope our findings underscore the need for approaches that develop a more deeply grounded understanding of the physical world.
Decisions on the lifetime extension of wind turbines require evaluating the remaining useful life of major load-carrying components by making a comparison to the design lifetime. This work focuses on the lifetime assessment of two fundamentally different components: a structural component in the form of the tower and rotating components in the form of the main bearings. A method is presented that combines high-frequency SCADA, accelerometers, tower bottom and blade root strain gauge bridges, and limited design information for continued estimates of the component loads and their subsequent fatigue damage accumulations. The work is applied to a highly instrumented DTU research turbine, a Vestas V52 model, where strain gauges in the blade root and in the tower bottom are calibrated for nearly 10 years using continual calibration methods without the need for operator input. The lifetime estimates of the tower bottom and front and rear main bearings were found to be 2952, 282, and 566 years, respectively, reflecting the low average wind speed of the turbine site compared to the wind turbine design wind class IA. Secondly, it was investigated whether virtual load sensors can replace tower strain gauges. Consistent tower bottom strain signal estimates and long-term damage accumulation were achieved with ±5 % lifetime variability once SCADA, nacelle accelerometers, and blade root strain gauges were combined for the deployment of a long short-term memory (LSTM) neural network. A systematic underprediction of the accumulated damage of the tower bottom was observed for the virtual load sensors with a reduced set of inputs, and a correction method was proposed. Finally, the impact of environmental conditions, including turbulence intensity and shear exponent of the incoming wind, on the main bearing lifetime was investigated based on load measurements. A simple drivetrain thermal model was used to evaluate the modified lifetime L10 m of the main bearings. Fatigue loads in the locating main bearing are driven by the peak of the turbine thrust curve, with higher loads observed at rated wind speed. An effect of longer main bearing lifetime with higher turbulence intensity was observed at rated wind speed and can be explained by the turbulence averaging of the thrust loads.
Abstract Life extension starts with integrity management. As floating production platforms in the Gulf of America age, the industry has a challenge to prolong the end of their service life while maintaining safe production and adding shareholder value. Industry generated integrity management documents have been created to provide a standardized life extension process, while allowing flexibility on how risks are managed based on system characteristics. In the past few years, Shell has been working to extend the life of several production platforms and subsea riser systems in the Gulf of America. This paper will share our learnings from standardizing the life extension process and key technical and engineering solutions applied in the determination and approval of longer service life for our floating production systems including topsides, hull, moorings, risers, and foundations. Shell has utilized a combination of direct and indirect assessments to quantify the condition of equipment for life extension. This paper will demonstrate that life extension starts well before the actual life extension process is initiated, in having proper and detailed asset integrity management plan. A consistent and deliberate execution of asset integrity management over the producing life of an asset is essential in reducing the overall scope and cost of activities required for extending the life of the asset. It also streamlines the assessment and approvals by third party Certified Verification Agents (CVA) and the regulatory body (BSEE). This paper will benefit other operators, CVAs, and the industry at large as we continue the journey of standardizing the life extension process in Gulf of America.
