jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingli Ren | Pan Yang
Source: Strategic Study of CAE (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Lifecycle Data Management of Nuclear Power Plant: Framework System and Development Suggestions The amount of nuclear power plant data has grown exponentially and has become an important asset as a result of the digital and intelligent transformation of the nuclear industry. However, there is a lack of lifecycle data management systems for nuclear power plants. This paper summarizes the status of research on nuclear power plant lifecycle data management in China; analyzes the demands and challenges of nuclear power plant lifecycle data management; and constructs a data management framework around the lifecycle nodes and business flows of nuclear power plants. The major tasks in establishing a nuclear power plant lifecycle data management system include (1) promoting the comprehensive digitization of the nuclear power industry and unifying the nuclear power plant lifecycle data standards; (2) classifying lifecycle data according to data types and designing data models; and (3) formulating scientific data quality control policies and data quality measurement standards to ensure the security of the lifecycle data of nuclear power plants. Furthermore, the following recommendations are made. (1) A data integration platform should be established to catalog the items, business data, and three-dimensional simulation data of nuclear power plants to promote the interconnection of data across business systems. (2) Blockchain technology should be applied to nuclear power plant lifecycle data management to ensure the authenticity and traceability of the data. (3) Digital twins and model-based system engineering should be applied in nuclear power plant lifecycle data management to improve the level of intelligent development. (4) A green policy should be established for nuclear power plant data management to realize the low-carbon, efficient, and sustainable development of data centers. Nuclear power plant ; Systems engineering ; Application lifecycle management ; Nuclear power ; Computer science ; Engineering ; Physics ; Nuclear physics ; Operating system Graphite, nuclear technology, radiation studies
Authors: Bing Dong | Kang Yang | Wei Zhang | Junlian Yin | Dezhong Wang
Source: Frontiers in Energy Research (article, 2022-03-21)
Field: Materials Science — Materials Chemistry

An Improved Method for PWR Fuel Failure Detection Using Cascade-forward Neural Network With Decision Tree When a fuel rod is damaged, determining the degree of fuel failure makes sense. The operators can decide whether to continue operating the reactor or shut it down based on the severity of the fuel failure. The isotopic ratio of two radioactive fission products (FPs) is a typical technique for evaluating the degree of fuel failure, although this is not applicable in the case of little fuel failure but large tramp uranium mass. The feedforward neural network (FFNN) has been used to identify fuel failures in order to overcome the shortcomings of the isotopic ratio method, although there is still inadequacy in the ability to distinguish between an intact fuel rod and a defective fuel rod with a small defect. In this study, we propose a cascade-forward neural network with a decision tree for fuel failure detection that performs well at classifying the degree of fuel failure and, in particular, at differentiating between an intact fuel rod and a defective fuel rod with a small size defect. The input of the neural network is the specific activity of FPs measured in the coolant. The degree of fuel failure is determined by the neural network’s output, which is labeled using one-hot encoding. The training set is constructed using the Booth-type diffusion model and the first-order kinetic model. The performance of the improved neural network is demonstrated. It is shown that the improved method is more accurate and responsive than the previous neural network when recognizing the onset of fuel failure. Finally, the most important nuclides are determined through the sensitivity analysis, and the neural network is simplified according to the importance of nuclides and the limitation of the radioactive detector in practical application. Artificial neural network ; Feed forward ; Nuclear fuel ; Burnup ; Feedforward neural network ; Computer science ; Cascade ; Nuclear engineering ; Nuclear reactor core ; Degree (music) ; Engineering ; Artificial intelligence ; Control engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Bo Pu | Xiaoming Wang | Weibing Li | Jun Feng
Source: Applied Sciences (article, 2022-01-05)
Field: Materials Science — Materials Chemistry

Analytical Model Formulation of Steel Plate Reinforced Concrete Walls against Hard Projectile Impact Steel plate reinforced concrete (SC) walls can effectively resist projectile impact by preventing the rear concrete fragments flying away, thus attracting much attention in defence technology. This work numerically and analytically investigated the hard projectile perforation of steel plate reinforced concrete walls. Impact resistance theories, including cavity expansion analysis as well as the petaling theory of thin steel plates were used to describe the cratering, tunneling and plugging phases of SC walls perforation. Numerical modeling of SC walls perforation was performed to estimate projectile residual velocity and target destructive form, which were validated against the test results. An analytical model for SC wall perforation was established to describe the penetration resistance featuring five stages, i.e., cratering, tunneling and plugging, petaling with plugging and solely petaling. Analytical model predictions matched numerical results well with respect to projectile deceleration evolution as well as residual velocity. From a structural absorbed energy perspective, the effect of front concrete panel and rear steel plate thickness combinations was also studied and analyzed. Finally, equivalent concrete slab thickness was derived with respect to the ballistic limit of SC walls, which may be helpful in the design of a protective strategy. Projectile ; Ballistic limit ; Perforation ; Materials science ; Structural engineering ; Slab ; Residual ; Reinforced concrete ; Composite material ; Engineering ; Mathematics ; Metallurgy High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Geophysical Methods and Applications
Authors: Raquel Martínez | A. Arroyo | Alberto Pigazo | M. Mañana | Eduardo Bayona | Francisco J. Azcondo | Sergio Bustamante | Alberto Laso
Source: Sensors (article, 2022-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Acoustic Noise-Based Detection of Ferroresonance Events in Isolated Neutral Power Systems with Inductive Voltage Transformers Power-quality events and operation transients in power systems (PS) with isolated neutral can saturate inductive voltage transformers (IVT), which, when interacting with the overhead and underground cable capacitances, can cause ferroresonance events in the local PS. This abnormal operating mode can partially or totally damage the transformers and switchgears within the affected PS. Distribution system operators (DSO) can minimize these effects by detecting ferroresonance events accurately and fast enough and changing the mode of operation accordingly. Direct detection methods, i.e., based on voltage measurements, are reliable, but the massive deployment of this solution is relatively expensive; i.e., power quality analyzers cost thousands of USD. Alternatively, indirect detection methods are also available, e.g., IVT vibration measurements with accelerometers costing hundreds of USD, but their reliability depends on the installation method used. This manuscript proposes using the acoustic noise caused by magnetostriction forces within the IVT core during ferroresonance events to detect their occurrence. Compared to other indirect methods, electret condenser microphones with preamplifying stage cost less than USD 10 and are less sensitive to the installation procedure. The proposed method is validated experimentally, and its performance compared to IVT vibration measurements one by using the same detection methodology. Ferroresonance in electricity networks ; Transformer ; Voltage ; Vibration ; Engineering ; Electrical engineering ; Electronic engineering ; Computer science ; Acoustics ; Physics Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Jochen Vleugels | Lore Veelaert | Thomas Peeters | Toon Huysmans | Femke Danckaers | Stijn Verwulgen
Source: Applied Sciences (article, 2022-10-10)
Field: Materials Science — Polymers and Plastics

Predicting User’s Measurements without Manual Measuring: A Case on Sports Garment Applications As sports garments are stretchable, different sizing tables are used than for retail clothing. However, customers measuring themselves leads to errors and unsatisfaction, since these customized branded garments cannot be returned. Using fitting sets avoids this, but this is not always feasible, especially in an online retail environment. Therefore, this research aims to use descriptive measures—parameters that do not require manual measuring because they are readily known by heart by almost any customer—to predict users’ body measurements, which can, thus, be used by customers to determine the size of their sports garment from a sizing chart. To validate if these input measures are sufficient to predict the correct size, three prediction methods are used and compared with baseline manual measurements. The methods are: (i) clothing size predictions from shape models with descriptive measures as inputs, (ii) clothing size predictions from a regression analysis, and (iii) clothing size predictions from a shape model based on extensive 3D scanned measurements as input. The conclusion is that a regression algorithm with, as input variables, the straightforward demographics of age, gender, stature, and weight is more accurate than the algorithm with the same inputs but with a shape model behind it. Moreover, chest and hip circumferences have an intraclass correlation coefficient rating above 0.9 and are, thus, suited for online retail of stretchable garments, such as cycling clothes. As validated by end-users, the regression predictions are shown to agree with preferred garment sizes of the participants, within the natural variation of personal preferences. Clothing ; Sizing ; Intraclass correlation ; Computer science ; Statistics ; Regression analysis ; Descriptive statistics ; Mathematics ; Econometrics ; Reproducibility ; Geography Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Thermoregulation and physiological responses
Authors: David Poole | Jorge L. Gálvez Vallejo | Mark S. Gordon
Source: Journal of Chemical Theory and Computation (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

A Task-Based Approach to Parallel Restricted Hartree–Fock Calculations In recent years, parallelism via multithreading has become extremely important to the optimization of high-performance electronic structure theory codes. Such multithreading is generally achieved via OpenMP constructs, using a fork-join threading model to enable thread-level data parallelism within the code. An alternative approach to multithreading is <i>task-based parallelism</i>, which displays multiple benefits relative to fork-join thread parallelism. A novel Restricted Hartree-Fock (RHF) algorithm, utilizing task-based parallelism to achieve optimal performance, was developed and implemented into the JuliaChem electronic structure theory software package. The new RHF algorithm utilizes a unique method of shell quartet batch creation, enabling construction and distribution of fine-grained shell quartet batches in a load-balanced manner using the Julia task construct. These shell quartet batches are then distributed statically across message-passing interface (MPI) ranks and dynamically across threads within an MPI rank, requiring no explicit inter-rank or interthread synchronization to do so. Compared to the hybrid MPI/OpenMP RHF algorithm present in the GAMESS software package, the task-based algorithm demonstrates speedups of up to ∼40% for systems in the S22(3) test set of molecules, with system sizes up to ∼1000 basis functions. The JuliaChem algorithm demonstrates the viability of both the task-based parallelism model and the Julia programming language for construction of performant electronic structure theory codes targeting systems of a size of chemical interest. Computer science ; Thread (computing) ; Multithreading ; Parallel computing ; Task parallelism ; Implicit parallelism ; Parallelism (grammar) ; Data parallelism ; Programming language Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Surface Chemistry and Catalysis
Authors: C. Ricardo Constante-Amores | Jalel Chergui | Seungwon Shin | Damir Jurić | J. R. Castrejón-Pita | Alfonso A. Castrejón‐Pita
Source: Journal of Fluid Mechanics (article, 2022-09-30)
Field: Materials Science — Surfaces, Coatings and Films

Role of surfactant-induced Marangoni stresses in retracting liquid sheets In this work, we study the effect of insoluble surfactants on the three-dimensional rim-driven retraction dynamics of thin water sheets in air. We employ an interface-tracking/level-set method to ensure the full coupling between the surfactant-induced Marangoni stresses, interfacial diffusion and inertia. Our findings are contrasted with the (Newtonian) dynamics of a liquid sheet edge, finding that the surfactant concentration can delay, or effectively prevent, the breakup of the rim. Our simulations use the fastest growing Rayleigh–Plateau instability to drive droplet detachment from the fluid sheet (rim). The results of this work unravel the significant role of Marangoni stresses in the retracting sheet dynamics at large elasticity numbers. We study the sensitivity of the dynamics to the elasticity number and the rigidification of the interface. Marangoni effect ; Pulmonary surfactant ; Materials science ; Breakup ; Inertia ; Rheology ; Mechanics ; Marangoni number ; Elasticity (physics) ; Newtonian fluid ; Instability ; Surface tension ; Composite material ; Convection ; Thermodynamics ; Physics ; Classical mechanics Surface Modification and Superhydrophobicity ; Fluid Dynamics and Heat Transfer ; Fluid Dynamics and Thin Films
Authors: Dwaraknath Gnaneshwar | Bharath Ramsundar | Dhairya Gandhi | Rachel C. Kurchin | Venkatasubramanian Viswanathan
Source: arXiv (Cornell University) (preprint, 2022-03-07)
Field: Materials Science — Materials Chemistry

Score-Based Generative Models for Molecule Generation Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial training and architectural constraints, respectively. Score-based generative models sidestep these challenges by modelling the gradient of the log probability density using a score function approximation, as opposed to modelling the density function directly, and sampling from it using annealed Langevin Dynamics. We believe that score-based generative models could open up new opportunities in molecule generation due to their architectural flexibility, such as replacing the score function with an SE(3) equivariant model. In this work, we lay the foundations by testing the efficacy of score-based models for molecule generation. We train a Transformer-based score function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5 million samples from the ZINC dataset and use the Moses benchmarking framework to evaluate the generated samples on a suite of metrics. Generative grammar ; Computer science ; Benchmarking ; Generative model ; Artificial intelligence ; Function (biology) ; Transformer ; Score ; Machine learning ; Engineering Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Scientific Computing and Data Management
Authors: Vladimir Kulagin | Yu. Gasparyan | N. N. Degtyarenko
Source: Fusion Engineering and Design (article, 2022-09-23)
Field: Materials Science — Materials Chemistry

Numerical estimation of the atomic fraction during laser-induced desorption of hydrogen from tungsten and beryllium Beryllium ; Tungsten ; Desorption ; Hydrogen ; Materials science ; Analytical Chemistry (journal) ; Atomic physics ; Laser ; Thermal desorption ; Chemistry ; Adsorption ; Nuclear physics ; Physical chemistry ; Physics ; Optics Fusion materials and technologies ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Fouzia Chang | Najma Memon | Saima Q. Memon | Abdul Sattar Chang
Source: International Journal of Environmental Science and Technology (article, 2022-01-05)
Field: Chemistry — Organic Chemistry

Selective adsorption of emerging contaminants from aqueous solution using Cu-based composite by solvothermal Adsorption ; Aqueous solution ; Chemistry ; Langmuir adsorption model ; Nuclear chemistry ; Copper ; Diclofenac ; Organic chemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Xintong Li | Zhida Liu | Yihan Liu | Suyogya Karki | Xiaoqin Li | Deji Akinwande | Jean Anne C. Incorvia
Source: ACS Applied Electronic Materials (article, 2022-08-09)
Field: Materials Science — Materials Chemistry

All-Electrical Control and Temperature Dependence of the Spin and Valley Hall Effect in Monolayer WSe<sub>2</sub> Transistors Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K, and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with a reflection measurement and a four-port electrical measurement is used to interpret the KR data. A lower-bound spin/valley lifetime is predicted to be of 40 ns and a mean free path of 240 nm below 90 K, 2 orders of magnitude higher than a previous work that uses similar methods. The spin/valley polarization on the edge is calculated to be ∼4% at 45 K. These results are important steps toward practical use of the SVHE. Valleytronics ; Condensed matter physics ; Spintronics ; Tungsten diselenide ; Spin Hall effect ; Spin (aerodynamics) ; Monolayer ; Spin polarization ; Van der Pauw method ; van der Waals force ; Physics ; Hall effect ; Materials science ; Chemistry ; Ferromagnetism ; Electrical resistivity and conductivity ; Nanotechnology ; Transition metal 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Saber Rostamzadeh | Mustafa Sarısaman
Source: New Journal of Physics (article, 2022-08-01)
Field: Materials Science — Materials Chemistry

Charge–pseudospin coupled diffusion in semi-Dirac graphene: pseudospin assisted valley transport Abstract Modifying the hexagonal lattices of graphene enables the repositioning and merging of the Dirac cones which proves to be a key element in the use of these materials for alternative electronic applications such as valleytronics. Here we study the nonequilibrium transport of carriers within a system containing two Dirac cones in both standard graphene and semi-Dirac graphene. In the latter, the lattice modifications cause the relativistic and parabolic dispersion bands to coexist, furnishing the Fermi surface with a rich pseudospin texture and a versatile Dirac cones separation. We construct a kinetic theory to investigate the carrier diffusion and uncover that the pseudospin index contributes to the particle current and, like the real spin, can induce a magnetoelectric effect, and argue that the pseudospin–charge coupling can be utilized to design a pseudospin filter. We explore the charge dynamics inside a quasi-one-dimensional conductor using the drift-diffusion model and detect the pseudospin accumulation at the sample boundaries. We find that, while, for graphene, the accumulation contributes to an extra voltage drop between the sample interfaces, the semi-Dirac system presents a similar accumulation that is strikingly equipped with valley polarization, signifying an essential tool for the control of valley manipulation and chirality transport using the pseudospin. Physics ; Graphene ; Charge (physics) ; Condensed matter physics ; Dirac (video compression format) ; Diffusion ; Dirac equation ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Authors: Zihang Li | Dajun Ren | Zhaobo Wang | Shan Jiang | Shuqin Zhang | Xiaoqing Zhang | Wangsheng Chen
Source: Water Science & Technology (article, 2022-06-13)
Field: Chemistry — Inorganic Chemistry

Adsorption and removal of direct red 31 by Cu-MOF: optimization by response surface Cu(PABA) is a Cu-based MOF material assembled from Cu<sup>2+</sup> and the organic ligand p-aminobenzoic acid (PABA). Cu (PABA) was synthesized by a solvothermal method, characterized and applied to the adsorption of direct red 31 dye (DR-31). The effects of pH, DR-31 concentration and temperature on the adsorption performance of Cu(PABA) were investigated. The adsorption kinetics were analyzed by pseudo-first-order, pseudo-second-order and intra-particle diffusion models, and the adsorption equilibrium data was fitted by Langmuir and Freundlich isotherm models. The pseudo-first-order kinetics and Langmuir model satisfactorily described the adsorption kinetics and adsorption equilibrium, respectively. The maximum adsorption capacity of Cu(PABA) for DR-31 dye at room temperature was 1,244.8 mg/g, as calculated using the Langmuir adsorption isotherm model. By response surface methodology (RSM), the optimal adsorption was found at pH value of 10.9, DR-31 dye concentration of 216.6 mg/L, and temperature of 27 °C, and the removal rate was as high as 99.4%. Therefore, Cu(PABA) can be used as an efficient adsorbent for removing DR-31 dye from aqueous solution. Adsorption ; Freundlich equation ; Chemistry ; Langmuir adsorption model ; Langmuir ; Kinetics ; Aqueous solution ; Diffusion ; Response surface methodology ; Inorganic chemistry ; Nuclear chemistry ; Chromatography ; Physical chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions