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The influence of thermal-load components (convection, collimated and uncollimated components of infrared radiation) in the process of production of PET packaging on the heating of PET preforms has been assessed. It has been established that the collimated component of infrared radiation ensures most (up to 70%) of the thermal energy in the process of heating of a PET preform.
Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.
Preferential attachment networks with power law exponent τ>3 are known to exhibit a phase transition. There is a value ρ{sub c}>0 such that, for small edge densities ρ≤ρ{sub c} every component of the graph comprises an asymptotically vanishing proportion of vertices, while for large edge densities ρ>ρ{sub c} there is a unique giant component comprising an asymptotically positive proportion of vertices. In this paper we study the decay in the size of the giant component as the critical edge density is approached from above. We show that the size decays very rapidly, like exp (−c/√(ρ−ρ{sub c})) for an explicit constant c>0 depending on the model implementation. This result is in contrast to the behaviour of the class of rank-one models of scale-free networks, including the configuration model, where the decay is polynomial. Our proofs rely on the local neighbourhood approximations of Dereich and Mörters (Ann Probab 41(1):329–384, 2013) and recent progress in the theory of branching random walks (Gantert et al. in Ann Inst Henri Poincaré Probab Stat 47(1):111–129, 2011).
Signal partitioning or signal segmentation allows to perform data classification, prediction of signals’ behavior, and profound interpretation of obtained data. In accordance with the signal model some distribution characteristics of the window variance noise component are investigated. It was shown that when all true underlying signal components remain unchanged, the window variance noise component is gamma distributed. Applying window variance to multicomponent signal (i.e., pharmacokinetic curve), it was shown that the window variance allows to split visually the signal record into phases due to prevalent processes.
The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.
The interfacial reaction between high-Mn–high-Al steel and CaO-SiO{sub 2}-type mold flux was investigated, with particular emphasis on the reduction of additive oxide components by Al. Typical additive oxide components (B{sub 2}O{sub 3}, Na{sub 2}O) could be reduced by Al, thereby losing their initial role in the flux. It is proposed to consider the chemical stability of the additive components in the flux in addition to the physico-chemical properties of the flux when developing the mold flux chemistry.
Ti6Al4V is a widely used, multi-component alloy in additive manufacturing, during which the fluid flow in the molten pool significantly affects the solidified dendrites. To predict and further control the microstructure, modeling and simulating the microstructure evolution play a critical role. In this study, a newly developed, efficient, quantitative multi-component phase-field (PF) model is coupled with a lattice Boltzmann (LB) model to simulate Ti6Al4V solidified dendrite evolution under fluid flow. The accuracy and convergence behavior of the model is validated by the Gibbs–Thomson relation at the dendrite tip. Single and multiple two-dimensional (2D) equiaxed dendrite evolution cases under forced flow were simulated. Results show that the dendrite pattern is influenced remarkably by the fluid flow. Underlying mechanisms of the asymmetrical evolution are revealed by discussing the interaction among the flow, composition distribution and dendrite morphology, quantitatively. The dendrite kinetics are also derived, which ascertains the relationship between tip velocity and undercooling and inlet velocity and is the foundation for larger-scale simulation. We believe that the coupled quantitative multi-component PF–LB framework employed in this study helps in investigating the solidified dendrite morphology evolution in a deep and quantitate manner.
The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.
In this paper an improved sub-component fatigue testing method is proposed, in which structural optimization is used to obtain specimens in which fatigue failure is precipitated in the designated area away from the boundaries, i.e., load application and fixture points. This is achieved by optimizing the nonlinear beam taper and the dynamic excitation. The major requirement for accurate material characterization through sub-component tests concerns unbiased stress states in the gauge section. However, empiricism shows that many sub-component high-cycle fatigue testing methods suffer from failure in the boundaries rather than the gauge section, which causes bias. The common practice for reinforcing those regions only shifts the issue into new areas of local discontinuities where failure is still caused remotely from the gauge section. An experimental proof of concept demonstrates that optimization of the beam taper can be used to obtain unbiased fatigue test data.
Interest in microbial communities for bioprocessing has surged in recent years based on the potential to optimize multiple tasks simultaneously and to enhance process productivity and stability. The presence and magnitude of these desirable system properties often result from interactions between functionally distinct community members. The importance of interactions, while appreciated by some disciplines for decades, has gained interest recently due to the development of ‘omics techniques, polymicrobial culturing approaches, and computational methods which has made the systems-level analysis of interacting components more tractable. This review defines and categorizes natural and engineered system components, interactions, and emergent properties, as well as presents three ecological theories relevant to microbial communities. Case studies are interpreted to illustrate components, interactions, emergent properties and agreement with theoretical concepts. A general foundation is laid to facilitate interpretation of current systems and to aid in future design of microbial systems for the next generation of bioprocesses.
Highlights: • Fourteen putative PP2C-A-like phosphatases were identified in Brassica rapa. • BrHAB2a was verified as a putative negative regulator of ABA signaling. • The inhibition of BrHAB2a by ABA and its receptors was successfully recapitulated. Abscisic acid (ABA) signaling is a vital physiological step that is used by many land plants to fight against environmental stress. As components of the linear ABA signaling pathway, clade A protein phosphatases type 2C (PP2C-As) are mainly inhibited by PYRABACTIN RESISTANCE1/PYR1-LIKE/REGULATORY COMPONENTS OF ABA RECEPTORS (PYLs)-type receptors upon their binding to ABA. Here, we show that the genome of Brassica rapa encodes 14 putative clade A PP2C-like proteins (BrPP2C-As). Two of these BrPP2C-As, Bra025964 and Bra016595, show high similarity to the HAB2 (Homology to ABI2) protein in Arabidopsis. RNAseq data reveal that nearly all BrPP2C-As, like BrHAB2a (Bra025964) and BrHAB2b (Bra016595), were highly expressed in at least one tissue. Overexpression of BrHAB2a conferred ABA insensitivity to Arabidopsis thaliana seedlings. Furthermore, the phosphatase activity of BrHAB2a could be inhibited by AtPYL1 or BrPYL1 in the presence of ABA. Overall, these results suggest that BrHAB2a is a functional PP2C-A like protein phosphotase and a key component of ABA signaling in Brassica rapa.
Through-section residual stresses in an asymmetric, thin-walled tapered bearing component were non-destructively studied via neutron diffraction after soft machining, heat treatment and hard turning. Two-dimensional area maps revealed non-uniform distributions of stress fields in both the thinnest and thickest sections of the component, indicating that the entire processing sequence plays a vital role — not only in distortion, but in the final stress state as well. Here, a computational model was developed to further understand the extent of the distortion induced in bearing components as a result of process variables in the manufacturing sequence.
A new method of using chemical kinetic reaction modeling to predict the Research Octane Number (RON) and Motor Octane Number (MON) of single component fuels and fuel mixtures is described and illustrated via comparisons between computed and experimental values obtained using the well-established ASTM test procedures in a Cooperative Fuels Research (CFR) engine. Comparisons include predictions of RON and MON for a large variety of neat fuels, studies determining the RON and MON of mixtures of primary reference fuels (PRF) and toluene, and studies of RON and MON for mixtures of single-component and multiple-component gasoline surrogate mixtures with ethanol. Advantages in costs, time, and experimental complexity of the kinetic modeling approach compared to the existing engine test procedures are discussed.
Based on our latest detailed chemical reaction mechanism, C3MechV4.0, we have developed two reduced reaction mechanisms—C3MechLite and C3MechCore—targeting C 0 –C 3 chemical species including NH 3 . C3MechLite (61 species), contains a number of species comparable to GRI-Mech (53 species), that can accurately predict the combustion characteristics of hydrogen, carbon monoxide, ammonia, methane, natural gas, nitrogen oxides, and their mixtures for a wide range of conditions. C3MechCore (118 species) targets a more comprehensive range of C 0 –C 3 fuels, including ammonia, methanol, ethanol, and dimethyl ether. Both mechanisms demonstrate predictive accuracy comparable to C3MechV4.0 for the combustion characteristics of the target fuels. C3MechLite is designed with a component library structure, enabling further reduction in mechanism size depending on the fuel(s) of interest for 2D/3D numerical simulations. Various combinations of component libraries were validated, and the average prediction error remains within 1 % compared to C3MechLite. Furthermore, the mechanism was applied to 3D LES simulations of H 2 lifted flames and was confirmed to reproduce flame characteristics with high accuracy. C3MechLite and its component library structure enable high-fidelity and computationally efficient chemical kinetic mechanisms, paving the way for application in more complex combustion simulations.
In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.
Purpose: Students applying to radiation oncology residency programs complete 1 or more radiation oncology clerkships. This study assesses student experiences and perspectives during radiation oncology clerkships. The impact of didactic components and number of clerkship experiences in relation to confidence in clinical competency and preparation to function as a first-year radiation oncology resident are evaluated. Methods and Materials: An anonymous, Internet-based survey was sent via direct e-mail to all applicants to a single radiation oncology residency program during the 2012-2013 academic year. The survey was composed of 3 main sections including questions regarding baseline demographic information and prior radiation oncology experience, rotation experiences, and ideal clerkship curriculum content. Results: The survey response rate was 37% (70 of 188). Respondents reported 191 unique clerkship experiences. Of the respondents, 27% (19 of 70) completed at least 1 clerkship with a didactic component geared towards their level of training. Completing a clerkship with a didactic component was significantly associated with a respondent's confidence to function as a first-year radiation oncology resident (Wilcoxon rank–sum P=.03). However, the total number of clerkships completed did not correlate with confidence to pursue radiation oncology as a specialty (Spearman ρ P=.48) or confidence to function as a first year resident (Spearman ρ P=.43). Conclusions: Based on responses to this survey, rotating students perceive that the majority of radiation oncology clerkships do not have formal didactic curricula. Survey respondents who completed a clerkship with a didactic curriculum reported feeling more prepared to function as a radiation oncology resident. However, completing an increasing number of clerkships does not appear to improve confidence in the decision to pursue radiation oncology as a career or to function as a radiation oncology resident. These results support further development of structured didactic curricula for the radiation oncology clerkship.
In this paper, we introduce a diffuse interface model to simulate multi-component two-phase flow with partial miscibility based on a realistic equation of state (e.g. Peng–Robinson equation of state). Because of partial miscibility, thermodynamic relations are used to model not only interfacial properties but also bulk properties, including density, composition, pressure, and realistic viscosity. As far as we know, this effort is the first time to use diffuse interface modeling based on equation of state for modeling of multi-component two-phase flow with partial miscibility. In numerical simulation, the key issue is to resolve the high contrast of scales from the microscopic interface composition to macroscale bulk fluid motion since the interface has a nanoscale thickness only. To efficiently solve this challenging problem, we develop a multi-scale simulation method. At the microscopic scale, we deduce a reduced interfacial equation under reasonable assumptions, and then we propose a formulation of capillary pressure, which is consistent with macroscale flow equations. Moreover, we show that Young–Laplace equation is an approximation of this capillarity formulation, and this formulation is also consistent with the concept of Tolman length, which is a correction of Young–Laplace equation. At the macroscopical scale, the interfaces are treated as discontinuous surfaces separating two phases of fluids. Our approach differs from conventional sharp-interface two-phase flow model in that we use the capillary pressure directly instead of a combination of surface tension and Young–Laplace equation because capillarity can be calculated from our proposed capillarity formulation. A compatible condition is also derived for the pressure in flow equations. Furthermore, based on the proposed capillarity formulation, we design an efficient numerical method for directly computing the capillary pressure between two fluids composed of multiple components. Finally, numerical tests are carried out to verify the effectiveness of the proposed multi-scale method.
We consider a thermodynamic consistent multi-component model in multi-dimensions that is a generalization of the classical two-phase flow model of Baer and Nunziato. The exchange of mass, momentum and energy between the phases is described by additional source terms. Typically these terms are handled by relaxation procedures. Available relaxation procedures suffer from efficiency and robustness resulting in very costly computations that in general only allow for one-dimensional computations. Therefore we focus on the development of new efficient and robust numerical methods for relaxation processes. We derive exact procedures to determine mechanical and thermal equilibrium states. Further we introduce a novel iterative method to treat the mass transfer for a three component mixture. All new procedures can be extended to an arbitrary number of inert ideal gases. We prove existence, uniqueness and physical admissibility of the resulting states and convergence of our new procedures. Efficiency and robustness of the procedures are verified by means of numerical computations in one and two space dimensions. - Highlights: • We develop novel relaxation procedures for a generalized, thermodynamically consistent Baer–Nunziato type model. • Exact procedures for mechanical and thermal relaxation procedures avoid artificial parameters. • Existence, uniqueness and physical admissibility of the equilibrium states are proven for special mixtures. • A novel iterative method for mass transfer is introduced for a three component mixture providing a unique and admissible equilibrium state.