Complex modeling for the quantification of nanoscale disorder using genetic algorithms, density functional theory and line-profile analysis
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We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.
Characterizing in situ stress is essential in Enhanced Geothermal System (EGS) for both risk assessment and operation design. Standard stress measurement techniques such as diagnostic fracture injection tests (DFITs) require creating hydraulic fractures and apply analytical methods such as the G -function analysis to estimate the stress. These classical approaches rely on several assumptions, including but not limited to: (i) the induced fracture is planar and persistent, and (ii) the fracture plane is normal to the minimum horizontal stress. However, these assumptions may not hold in EGS environments like Utah FORGE, where thermo-hydro-mechanical (THM) coupling, wellbore deviation, and rock heterogeneity can alter the local stress condition and consequently lead to fracture complexities in both near-well and far-field regions.
Gaseous benzene-iodine and diethyl ether-iodine spectra, extinction coefficients, formation constants and enthalpies for charge transfer complexes of gaseous benzene-iodine and diethyl ether-iodine
The present investigation is concerned with an eight-day episode, during which a series of mesoscale convective complexes (MCC) developed and moved across the country, producing heavy rain and some flooding over an extensive region. An overview of the considered period from August 3 to August 10, 1977 is presented, and the evolution of the August 4 storm is examined. The structure of the mature MCC is discussed, taking into account the August 4-5 storm, a comparative case involving the August 3-4 storm, and an evaluation of the observed phenomena. It is concluded that MCCs are basically tropical in nature and that their dynamics are dominated by buoyant accelerations. It was found that the MCCs developed a warm-core, divergent anticyclonic flow pattern in the upper troposphere which was not present prior to the development of convection. A similar structure is observed in tropical cloud clusters.
Complex refractive index and dielectric permittivity studies of presently used Space Shuttle tile materials at millimeter wavelengths reveal these tiles to exhibit similar absorption characteristics to those of fused silica materials. This absorption is mainly related to the water content in the specimen. A strong birefringence is observed at least in one of these fibrous refractory composite materials.
Electronic parts (integrated circuits) have grown in complexity such that determining all failure modes and risks from single particle event testing is impossible. In this presentation, the authors will present why this is so and provide some realism on what this means. Its all about understanding actual risks and not making assumptions.
The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. Furthermore, the effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.
The Recipient will assist with the field validation of one or more risk assessment toolsets through: 1) collaborative research; 2) sharing of relevant datasets, information, and technical insights from field efforts; and 3) other modeling and simulation toolsets as they are developed. Collaborative research could include, but is not limited to: 1) demonstrating use of field data for improved leakage pathway characterization, uncertainty reduction, and risk management and uncertainty workflows; 2) testing and validation of a specific National Risk Assessment Partnership (NRAP) tool using site specific data; and 3) improved modeling or reduced-order modeling of geophysical monitoring.
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