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Abstract Measurements of the star formation efficiency (SFE) of giant molecular clouds (GMCs) in the Milky Way generally show a large scatter, which could be intrinsic or observational. We use magnetohydrodynamic simulations of GMCs (including feedback) to forward-model the relationship between the true GMC SFE and observational proxies. We show that individual GMCs trace broad ranges of observed SFE throughout collapse, star formation, and disruption. Low measured SFEs ($${\ll} 1\hbox{ per cent}$$) are ‘real’ but correspond to early stages; the true ‘per-freefall’ SFE where most stars actually form can be much larger. Very high ($${\gg} 10\hbox{ per cent}$$) values are often artificially enhanced by rapid gas dispersal. Simulations including stellar feedback reproduce observed GMC-scale SFEs, but simulations without feedback produce 20× larger SFEs. Radiative feedback dominates among mechanisms simulated. An anticorrelation of SFE with cloud mass is shown to be an observational artefact. We also explore individual dense ‘clumps’ within GMCs and show that (with feedback) their bulk properties agree well with observations. Predicted SFEs within the dense clumps are ∼2× larger than observed, possibly indicating physics other than feedback from massive (main-sequence) stars is needed to regulate their collapse.
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This content will become publicly available on November 1, 2025
Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation
This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.
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- PAR ID:
- 10555236
- Publisher / Repository:
- NSF PAR
- Date Published:
- Journal Name:
- Materials
- Volume:
- 17
- Issue:
- 21
- ISSN:
- 1996-1944
- Page Range / eLocation ID:
- 5339
- Subject(s) / Keyword(s):
- architected composites additive manufacturing artificial intelligence optimization
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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