Core Design Research Articles

Comparative study on bending behavior and damage analysis of 3D-printed sandwich core designs with bio-inspired reinforcements

In this study, novel sandwich core designs with bio-inspired reinforcements were proposed and their bending behaviors were comparatively examined. The geometrical shapes of alligator osteoderm and chambered nautilus shell were utilized as bio-inspired reinforcements for sandwich core structures. Sandwich core structures were produced through the additive manufacturing method. Experimental tests and finite element analysis were performed to determine the bending performances of the proposed sandwich core structures. The load-carrying capacity, deformation ability, damage-tolerant capability, energy absorption, and damage mechanisms of the proposed sandwich core structures were comparatively investigated through experimental and numerical methods. The orthotropic material model and Hashin’s damage criterion were used in the numerical model of 3D-printed sandwich core structures to consider the effect of the filament raster orientation on the elastic and damage behavior of the sandwich core structures. Compared to the classical honeycomb sandwich core structure, while bio-inspired reinforcements improved the load-carrying capacity and damage-tolerant capability of sandwich core structures, they reduced the energy absorption ability of sandwich core structures due to reducing the vertical deformation ability of sandwich core structures. Bio-inspired reinforcements significantly affected the stress distribution and damage behavior of the sandwich core structures. They reduced von Mises stress level at the outer cell edges of the sandwich core structures and caused reinforcement damage instead of outer cell damage.

Practical Design of a Breed-and-Burn Lead-Cooled Fast Reactor with Rotational Fuel Shuffling Strategy

The purpose of this study is to demonstrate a practical core design for a lead-cooled, nitride fueled, rotational fuel shuffling breed-and-burn (RFBB) fast reactor. The core design is based on the Westinghouse Lead Fast Reactor (WH-LFR) and uses natural uranium nitride fuel with a sodium bond encased in oxide dispersion-strengthened steel cladding. Simulations confirmed the potential of the reactor to maintain criticality at the equilibrium state, with a reactivity swing of less than 200 pcm at every cycle interval and an average discharge burnup of 235 MWd/kg heavy metals (HM) for a 1050 effective full-power day refueling interval. Power profiles were maintained stable at the equilibrium state, while the cladding of the discharged fuel incurred over 650 displacements per atom over its entire residency in the core. From a nonproliferation perspective, the plutonium vector for the discharge fuel aligns with reactor-grade fuel standards, with over a 70% concentration of 239Pu and over 22% 240Pu, reducing the risk of weaponization. The adopted control rod system has been shown to offer sufficient negative reactivity of over 19 $ to bring the reactor into a subcritical state. Challenges such as the susceptibility of neutron balance to material thickness and neutron leakage have been addressed, emphasizing the necessity for meticulous design improvements. A steady-state thermohydraulic analysis confirmed the heat removal capacity from the hottest channel, ensuring operational safety. This study confirmed the feasibility of the RFBB strategy for a lead-cooled nitride-fueled fast reactor and sets a precedent for future research in enhancing fuel utilization and safety in nuclear reactors.

KNN-GNN: A powerful graph neural network enhanced by aggregating K-nearest neighbors in common subspace

It has been proven that graph neural networks (GNNs) are effective for a variety of graph learning-based applications. Typical GNNs iteratively aggregate messages from immediate neighbors under the homophily assumption. However, there are a larger number of heterophilous networks in our daily life, where the ability of these GNNs is limited. Recently, some GNN models have been proposed to handle networks with heterophily via some key designs like aggregating higher-order neighbors and combining immediate representations. But ”noise” information transmitted from different-order neighbors will be injected into the representations of nodes. In this paper, we propose a new GNN model, called KNN-GNN, to effectively perform the node classification task for networks with various homophily levels. The main idea of KNN-GNN is to learn a comprehensive and accurate representation for each node by integrating not only the local information from its neighborhood but also the non-local information held by its similar nodes decentralized in the network. Specifically, the local information of a node is generated from itself and its 1-hop neighbor. Then, we project all nodes into a common subspace, where similar nodes are desired to be close to each other. The non-local information of a node is gathered by aggregating its K-Nearest Neighbors searched in the common subspace. We evaluate the performance of KNN-GNN on both real and synthetic datasets including networks with diverse homophily levels. The results demonstrate that KNN-GNN outstrips the state-of-the-art baselines. Moreover, the ablation experiments show that the core designs in KNN-GNN play a critical role in node representation learning.

Single‐Walled Carbon Nanotube‐Based Optical Nano/Biosensors for Biomedical Applications: Role in Bioimaging, Disease Diagnosis, and Biomarkers Detection

AbstractThe convergence of advanced nanotechnology with disease diagnosis has ushered in a transformative era in healthcare, empowering early and accurate detection of diseases and paving the way for timely interventions, improved treatment outcomes, and enhanced patient well‐being. The development of novel materials is frequently the impetus behind significant advancements in sensor technology. Among them, single‐walled carbon nanotubes (SWCNTSs) have emerged as promising nanomaterials for developing biosensors. Their unique optical, electrical, and biocompatibility properties make them promising candidates for enhancing the sensitivity and real‐time monitoring capabilities of biosensors, as well as for enabling various bioimaging techniques. Recent studies have demonstrated the utility of SWCNTS‐based biosensors in the real‐time monitoring of biological analytes, such as nitric oxide and hydrogen peroxide (H2O2), with potential implications for disease understanding and therapeutic response assessment. Moreover, SWCNTSs have shown promise in bioimaging applications, including fluorescence, Raman spectroscopy, and photoluminescence imaging of biological samples. This article delves into the core principles, design strategies, and operational mechanisms that underpin SWCNTS‐bioimaging techniques‐based biosensors. It emphasizes on their unique properties and versatile functionalization of carbon nanotubes, laying the foundation for their integration into biosensor platforms and applications aimed at diagnosing a wide spectrum of diseases including infectious diseases, cancer, neurological disorders, and metabolic conditions.

The influence of industrial designers and developmental technologists on design-driven innovation and related mechanisms

ABSTRACT Design-driven innovation (DDI) is a concept that promotes product innovation by giving new meaning to existing products. However, there are divergent views on who starts the DDI process and what kinds of activities are carried out within it. Therefore, this study analyzed four cases that succeeded in de-maturing the Japanese electric fan market to confirm whether there are differences in DDI processes, outcomes of new products, and post-launch spillover effects on new businesses between DDI originating from industrial designers (IDs) and that originating from developmental technologists (DTs). This study also aimed to elucidate the mechanisms by which these differences may arise. Our results showed that the difference in the way of thinking caused by the different knowledge and skills possessed by IDs and DTs affects the selection of core design concepts and product architecture. This was a fundamental factor that caused differences in DDI processes, the outcomes of new products, and the ripple effect on new businesses.

Femtosecond laser writing of robust waveguides in optical fibers with enhanced photosensitivity

We report the femtosecond laser writing of meter-long optical waveguides inscribed through the coating of specifically designed optical fibers. In order to improve the material photosensitivity and to ensure non-guiding optical fibers for subsequent laser processing of the waveguiding core, a depressed refractive index core design is implemented by co-doping a large portion of the optical fiber with germanium oxide and fluorine. The enhanced photosensitivity provided by further deuterium loading these fibers allows laser-writing of large refractive index contrast waveguides over wide cross sections. To mitigate the formation of photoinduced color centers causing high propagation losses in the photo-written waveguides, thermal annealing up to 400°C is performed on polyimide-coated laser-written fibers. Although the refractive index contrast decreases, the propagation losses are drastically reduced down to 0.08 dB/cm at 900nm allowing a robust single-mode guiding from visible to near infrared. Our results pave the way towards the development of a new generation of optical fibers and photonic components with arbitrarily complex designs.

Research on magnetic field characteristics of amorphous alloy and grain‐oriented silicon steel hybrid magnetic circuit iron core

AbstractAmorphous alloy and grain‐oriented silicon steel are commonly employed as soft magnetic materials for the preparation of transformer cores, but both materials have their advantages and disadvantages. Using only silicon steel or amorphous alloy for the preparation of the core makes it difficult to achieve both high design density and low no‐load loss characteristics. In order to achieve the magnetic performance advantages of both amorphous alloy and silicon steel in transformer core development and application, the authors design and develop a kind of amorphous alloy and grain‐oriented silicon steel hybrid magnetic circuit core to explore the amorphous alloy and grain‐oriented silicon steel at different ratios under the magnetic properties of matching law. Based on the amorphous alloy and grain‐oriented silicon steel core magnetic performance comparison characteristics, the authors establish a core magnetic circuit model and design a hybrid magnetic circuit core structure with different material ratios. The hybrid magnetic circuit core model is simulated and analysed to obtain the magnetic density distribution of the hybrid magnetic circuit core. A number of hybrid magnetic circuit core experimental models are produced, and the use of a magnetic property measurement platform is made for the hybrid magnetic circuit core measurements, to reveal the magnetic performance matching law of the hybrid magnetic circuit core, providing a good solution for power distribution in the hybrid magnetic circuit.

Model authenticity in learning mathematical experimentation: how students perceive and learn from scientist and peer models

The implementation of video modeling examples of mathematical hands-on experimentation may provide students with authentic and, at the same time, not too cognitively overwhelming experiences. However, the effectiveness of video modeling examples can be influenced by different characteristics of the observed models. On the one hand, based on the model-observer similarity hypothesis, it is likely that the observation of peers is particularly conducive to learning. On the other hand, from an authentic learning perspective, the presence of experts is considered to constitute a core design element of authentic learning settings which may foster motivational and cognitive learning outcomes. Against the background of these contradictory assumptions, the present study investigates the effects of observing models with different degrees of authenticity on students’ perceived authenticity, their situational interest, and their knowledge acquisition. We conducted an experimental study with 105 10th graders who observed either peer or scientist models performing a mathematical hands-on experiment in a video recording. As expected, the results show that students perceived the scientist models as more authentic than the peer models. Furthermore, we found neither a direct effect of condition nor an indirect effect mediated by students’ perceived authenticity of the observed models on students’ situational interest and knowledge acquisition. With this study, we contribute to the literature on the conditions and effects of authentic learning.

Experimental and Seepage Analysis of Gabion Retaining Wall Structure for Preventing Overtopping in Reservoir Dams

Recently, heavy rains caused by climate change have resulted in dam failures due to overtopping. This study presents a design method aiming to prevent overtopping failures by applying gabion retaining walls at the dam crest. Simulations, experiments, and measurements were conducted to evaluate the effectiveness of this design. The design framework aims to establish a system in which gabion retaining walls prevent overtopping when water levels exceed the crest of the dam, efficiently draining seepage water into the dam body through vertical filters. Research findings indicate that implementing dam crest core and geomembrane design effectively prevents seepage and saturation of the downstream slope during overtopping events. Notably, the reservoir dam operates in a stable manner, as seepage water passing through the dam body is directed solely to the toe drain. Overall, this design approach suggests its potential as a practical solution by significantly reducing hazards resulting from heavy rainfall.

Magnetic-fluid-infiltrated bilaterally polished photonic crystal fiber with butterfly core for LSPR based magnetic field sensing via water based Fe3O4 magnetic fluid

In this manuscript, a sensor is devised employing photonic crystal fiber with localized surface plasmon resonance (PCF-LSPR), emphasizing the manipulation of refractive index (RI) through magnetic fluid (MF). The sensor's air holes adopt a hexagonal arrangement, forming a butterfly core design, and the transmission channels for the effective confinement of optical field energy relies significantly on the regions surrounding the central air hole in both directions. MF serves as the sensing medium, and the top and bottom polished surfaces are coated with gold and titanium dioxide. The sensor undergoes analysis using the finite element method, scrutinizing its model characteristics, structural parameters, and sensing performance. The results indicate a wavelength sensitivity of up to 45,600 nm/RIU and a maximum figure of merit (FOM) of 434 RIU−1. Within the range of magnetic field 30–150 Oe, the highest magnetic field sensitivity records 3350 pm/Oe. Over the temperature range of 27.4–114 °C, the temperature sensitivity measures only 310 pm/°C. A maximum sensor resolution of 2.19×10−6RIU is achieved for x−pol. The linear relationship between the resonant wavelength and the magnetic field yields R2=0.9945, for degree (2) for x−pol. The proposed sensor exhibits notable advantages, including a structure which is very stable, high sensitivity, ease of integration, and resilience to electromagnetic interference. Additionally, it excels in detecting weak magnetic fields. Its potential applications span from industrial production, military technology, to medical equipment.

Computational Investigation of the Mechanical Response of a Bioinspired Nacre-like Nanocomposite under Three-Point Bending

Natural biological nanocomposites, like nacre, demonstrate extraordinary fracture toughness, surpassing their base materials, attributed to their intricate staggered hierarchical architectures integrating hard and soft phases. The enhancement of toughness in these composites is often linked to the crack-deflection mechanism. Leveraging the core design principles that enhance durability, resilience, and robustness in organic materials, this paper describes the use of computational modeling and simulation to perform a three-point bending test on a 3D staggered nanocomposite intentionally crafted to mimic the detailed microstructure of nacre. We adopted a previously proposed interfacial zone model that conceptualizes the “relatively soft” layer as an interface between the “hard” mineral tablets and the microstructure’s interlayer spaces to examine how the microstructure and interface characteristics affect the mechanical responses and failure mechanisms. By comparing the model’s predictions with experimental data on natural nacre, the simulations unveil the mechanisms of tablet separation through adjacent layer sliding and crack deflection across interfacial zones. This study offers a robust numerical method for investigating the fracture toughening mechanisms and damage evolution and contributes to a deeper understanding of the complex interplays within biomimetic materials.

Liquid deuterium cold source concept for the NIST neutron source

The National Institute of Standards and Technology (NIST) Center for Neutron Research (NCNR) houses an aging reactor that serves more than 40 % of all cold neutron research needs in the U.S. First critical in 1967, the National Bureau of Standards Reactor (NBSR) is now more than 50 years old. NCNR engineers have initiated a design effort for a replacement reactor, namely the NIST Neutron Source or NNS. The NNS is conceived as a 20-MWth light-water cooled and moderated, and heavy-water reflected compact core design. The NNS will include two liquid deuterium cold sources to moderate neutrons to effective temperatures well below that of thermal neutrons. These cold neutron sources will require new cryogenic infrastructure to operate. This report describes a preliminary proposed design concept for the NNS cold sources and associated ancillary infrastructure.

DAM SRAM CORE: An Efficient High-Speed and Low-Power CIM SRAM CORE Design for Feature Extraction Convolutional Layers in Binary Neural Networks.

This article proposes a novel design for an in-memory computing SRAM, the DAM SRAM CORE, which integrates storage and computational functionality within a unified 11T SRAM cell and enables the performance of large-scale parallel Multiply-Accumulate (MAC) operations within the SRAM array. This design not only improves the area efficiency of the individual cells but also realizes a compact layout. A key highlight of this design is its employment of a dynamic aXNOR-based computation mode, which significantly reduces the consumption of both dynamic and static power during the computational process within the array. Additionally, the design innovatively incorporates a self-stabilizing voltage gradient quantization circuit, which enhances the computational accuracy of the overall system. The 64 × 64 bit DAM SRAM CORE in-memory computing core was fabricated using the 55 nm CMOS logic process and validated via simulations. The experimental results show that this core can deliver 5-bit output results with 1-bit input feature data and 1-bit weight data, while maintaining a static power consumption of 0.48 mW/mm2 and a computational power consumption of 11.367 mW/mm2. This showcases its excellent low-power characteristics. Furthermore, the core achieves a data throughput of 109.75 GOPS and exhibits an impressive energy efficiency of 21.95 TOPS/W, which robustly validate the effectiveness and advanced nature of the proposed in-memory computing core design.

METAL: Methodology for liquid metal fast reactor core economic design and fuel loading pattern optimization

The design of Liquid Metal-cooled Fast Reactor (LMFR) cores encompass two levels of design. The first is the physical core design, which is concerned with the design of the fuel assembly geometry in the context of a full core. The second is the fuel loading design, which is an in-core fuel management problem concerned with the design of fuel enrichment and core loading pattern. In this paper, an optimized fuel design search is developed to improve core loading patterns. As part of the implementation, the multiphysics simulation suite LUPINE has been enhanced to allow for fuel shuffles and an in-core fuel management optimization methodology has been added with the objective of reducing the fuel cost. The design methodology is referred to as Methodology for Economical opTimization of Applied LMFR (METAL), and this methodology is demonstrated using the popular Super Power Reactor Innovative Small Modular (SPRISM) sodium-cooled fast reactor (SFR) as a reference core. One challenge currently facing the deployment of LMFRs is the availability of TRansUranium (TRU) and high assay low enriched uranium (HALEU) driver fuel. The two forms of fuel are expensive and not widely available, which poses a challenge on the startup of LMFR cores. To address the availability of driver fuel, and to lower fuel costs, low enriched uranium (LEU) blankets are investigated as replacements to the natural uranium or depleted uranium blankets typically used. The advantage of this solution is the wide availability of LEU and its ability to lower the amount of TRU or HALEU driver fuel needed. The design objectives, constraints, and optimization algorithm for a SFR are identified. Then, METAL is applied to design a SFR core fueled by TRU driver fuel assemblies and LEU blanket assemblies. To demonstrate the advantage of introducing LEU blankets, METAL is also used to design another SFR core following the same objectives and constraints, but using naturally enriched blankets. By comparing the two cores, it was found that the introduction of LEU blankets results in a ∼28% reduction in the driver fuel mass requirements. Upon development of a levelized fuel cycle cost (LFCC) model, the 28% reduction in driver fuel mass corresponds to a 10% decrease in the LFCC. Additional advantages of using LEU blankets include reducing the core conversion ratio (CR), and improving the assembly radial power peaking factor (RPPF), which enhances the safety and non-proliferation performances of the SFR core.

Function-Level Compilation Provenance Identification with Multi-Faceted Neural Feature Distillation and Fusion

In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary code. To this end, we present MulCPI, short for Multi-representation Fusion-based Compilation Provenance Identification, which integrates the features collected from multiple distinct intermediate representations of the binary code for better discernment of the fine-grained function-level compilation details. In particular, we devise a novel graph-oriented neural encoder improved upon the gated graph neural network by subtly introducing an attention mechanism into the neighborhood nodes’ information aggregation computation, in order to better distill the more informative features from the attributed control flow graph. By further integrating the features collected from the normalized assembly sequence with an advanced Transformer encoder, MulCPI is capable of capturing a more comprehensive set of features manifesting the multi-faceted lexical, syntactic, and structural insights of the binary code. Extensive evaluation on a public dataset comprising 854,858 unique functions demonstrates that MulCPI exceeds the performance of current leading methods in identifying the compiler family, optimization level, compiler version, and the combination of compilation settings. It achieves average accuracy rates of 99.3%, 96.4%, 90.7%, and 85.3% on these tasks, respectively. Additionally, an ablation study highlights the significance of MulCPI’s core designs, validating the efficiency of the proposed attention-enhanced gated graph neural network encoder and the advantages of incorporating multiple code representations.

The Damietta Server: a comprehensive protein design toolkit.

The growing importance of protein design to various research disciplines motivates the development of integrative computational platforms that enhance the accessibility and interoperability of different design tools. To this end, we describe a web-based toolkit that builds on the Damietta protein design engine, which deploys a tensorized energy calculation framework. The Damietta Server seamlessly integrates different design tools, in addition to other tools such as message-passing neural networks and molecular dynamics routines, allowing the user to perform multiple operations on structural models and forward them across tools. The toolkit can be used for tasks such as core or interface design, symmetric design, mutagenic scanning, or conformational sampling, through an intuitive user interface. With the envisioned integration of more tools, the Damietta Server will provide a central resource for protein design and analysis, benefiting basic and applied biomedical research communities.The toolkit isavailable with no loginrequirement through https://damietta.de/.

Considerations for the static and seismic design of pumped storage reservoirs in soft soils and seismic environments

AbstractFor recent hydroelectric pumped storage projects, the construction of embankment dam structures on alluvial deposits consisting of exceptionally soft soil and in areas with high seismicity was required. This contribution shares experiences and challenges from the static and seismic design of embankment dam structures and ground treatment options. Finite element‐based static and seismic analyses using the Hardening Soil Small Strain model (HSS) showed that preconsolidation has clear benefits compared to stone columns for ground improvement purposes. Regardless of general advantages of surface sealing systems for frequent and rapid impoundment level changes, it was found that clay core design options perform very well under seismic loading conditions and even better than surface‐sealed structures. Seismic ground and structure response analyses involving soft soil considering shear strain‐dependent stiffness degradation revealed that deamplification is expected in contrast to the rule‐of‐thumb amplification approaches enshrined in many seismic design guidelines and codes. It was found that this effect increases with seismic magnitudes and that saturation of peak ground acceleration (PGA) takes place. This results in relatively moderate structure excitations even under significant dynamic excitation.

Detecting irradiation defects in materials: A machine learning approach to analyze helium bubble images

Additive manufacturing technology has received significant attention in the field of nuclear materials due to its potential to improve the radiation resistance of materials and components. In our research, we propose to use 304 L stainless steel fabricated by Selective Laser Melting (SLM) as a replacement for traditional casting due to its superior performance and optimized manufacturing technique. During testing of its radiation resistance, we found that analyzing helium bubbles captured by transmission electron microscopy (TEM) is crucial for understanding and predicting material behavior under irradiation. However, this task typically involves manual counting, which is both time-consuming and prone to errors due to the fatigue of human annotators. To address this challenge, we present a novel machine learning model for automatic helium bubbles detection and counting through images. Our model leverages a fusion of traditional computer vision techniques with cutting-edge machine learning methods to achieve superior detection of helium bubbles in nuclear materials. This approach is based on two core designs: a novel generative model to generate training data and a gradient convergence layer to limit the range of parameters. Experiments show that our model outperforms previous state-of-the-art unsupervised detectors, and achieves highly accurate helium bubble segmentation in a few-shot scenario, with an F1 score typically exceeding 90 % and an average size error less than 0.1 nm. Our model achieves over 100 times more efficiency than manual counting, which takes approximately 20 s per image. Our work demonstrates a successful collaboration between the fields of material characterization and artificial intelligence (AI). By leveraging the respective strengths of material science and machine learning, we have achieved surprising results that could have a significant impact on the development of new materials and components for nuclear applications.

Design and Synthesis of Polystyrene-Occluded Reactive Core–Shell Particles of Natural Rubber to Balance Stiffness and Toughness in Poly(lactic acid)

Design and Synthesis of Polystyrene-Occluded Reactive Core–Shell Particles of Natural Rubber to Balance Stiffness and Toughness in Poly(lactic acid)

Impact of fuel temperature on nuclear core design calculations

The operation of a nuclear power plant relies on precalculated nuclear design predictions based on core calculations of various reactor states. The fuel temperature is a crucial factor in determining the reactor fuel behavior, but assessing the temperature variation in a fuel pellet taking into account neutron transport is challenging. Detailed simulation of the temperature behavior within the fuel pellet can be obtained by coupling of Monte Carlo neutron transport codes with thermal-hydraulics solvers. However, this approach is not practical for standard nuclear design calculations, and computationally cheaper and faster methods must be used. In nuclear core simulators, a concept of a single "effective temperature" that yields the same neutron response as in the case of the actual temperature shape is mainly applied. This paper evaluates various fuel temperature models used in nuclear core simulation calculations, ultimately recommending a new effective temperature model that considers the burnup correction.