Physical Technology Research Articles

Welcome to Modern Mathematical Physics: A Note from the Editorial Office

On 6 June 2024, we had the privilege of visiting Professor Chang-Pu Sun at the China Academy of Engineering Physics [...]

Resource Planning and Collapse of Buildings in Kiambu County, Kenya

Purpose: The research sought to find out the impacts of resource planning on building collapse in Kiambu County, Kenya. Therefore, this research aimed to establish the impact of resource planning on building collapse in Kiambu to be able to advise on how to contain the problem and enhance the non-collapse of such buildings. Methodology: In this study, the authors adopted a simple random sampling technique to identify construction projects in Kiambu County based on Kahneman and Tversky’s Scheduling Fallacy Theory that covers problems such as over-optimism and false resource forecasting on resource requirement necessary for efficient completion of the project. The descriptive research method was also used in the study since it included both qualitative and quantitative data. The population of the study consisted of 545, which included the employees from the physical planning department, architects, surveyors, engineers, and contractors’ builders, and finally the sample size came to 231 as estimated By Yamane, 1967. Findings: The research findings revealed that poor resource management has partly influenced building failures. Attempts by the contractors to solve the problem and eradicate the vice have been frustrated by the locals and politicians for their selfish reasons which have not been highlighted in this study. The research also pointed out that handling of resources reveals the highest mean score of 4.1773 this means that most respondents hold positive attitudes towards their practice of resource planning. Unique Contribution to Theory, Policy, and Practice: The Scheduling Fallacy Theory of 1979 by Kahneman and Tversky was utilized to examine the construction collapses of Kiambu County and the cognitive biases of underestimating time, resources, and costs in construction projects. The study suggested that strict adherence to building codes, better regulatory standards, risk evaluation, and better resource allocation are needed. Prevention was recommended through cross-sector collaboration of government, developers, contractors, and regulatory bodies alike. Empirical research into resource planning faults and their impact on structural integrity was proposed.

Physical Field Effects to Suppress Polysulfide Shuttling in Lithium-Sulfur Battery.

Lithium-sulfur batteries (LSB) with high theoretical energy density are plagued by the infamous shuttle effect of lithium polysulfide (LPS) and the sluggish sulfur reduction/evolution reaction. Extensive research is conducted on how to suppress shuttle effects, including physical structure confinement engineering, chemical adsorption strategy, and the design of sulfur redox catalysts. Recently, the rational design to mitigate shuttle effects and enhance reaction kinetics based on physical field effects has been widely studied, providing a more fundamental understanding of interactions with sulfur species. Herein, the physical field effect is focused and their methods and mechanisms of interaction are summarized systematically with LPS. Overall, the working principle of LSB system, the origin of the shuttle effect, and kinetic trouble in LSB are briefly described. Then, the mechanism and application of rational design of materials based on physical field effect concepts and the external physical field-assisted LSB are elaborated, including electrostatic force, built-in electric field, spin state regulation, strain engineering, external magnetic field, photoassisted and other physical field-assisted strategies are pivotally elaborated and discussed. Finally, the potential directions of physical field effects in enhancing the performance and weakening the shuttle effect of high-energy LSB are summarized and anticipated.

Modeling Pollutant Diffusion in the Ground Using Conformable Fractional Derivative in Spherical Coordinates with Complete Symmetry

The conformal fractional derivative (CFD) has become a hot research topic since it has a physical interpretation and is easier to grasp and apply to problems compared with other fractional derivatives. Its application to heat transfer, diffusion, diffusion-advection, and wave propagation problems can be found in the literature. Fractional diffusion equations have received great attention recently due to their applicability in physical, chemical, and biological processes and engineering. The diffusion of the pollutants within the ground, which is an important environmental problem, can be modeled with a diffusion equation. Diffusion in some porous materials or soil can be modeled more accurately with fractional derivatives or the conformal fractional derivative. In this study, the diffusion problem of a spilled pollutant leaking into the ground modeled with the conformal fractional time derivative in spherical coordinates has been solved analytically using the Fourier series for a constant mass flow rate and complete symmetry under the assumptions of homogeneous and isotropic soil, constant soil temperature, and constant permeability. The solutions have been simulated spatially and in time. A parametric analysis of the problem has been performed for several values of the CFD order. The simulation results are interpreted. It has also been suggested how to find the parameters of the soil to see whether the CFD can be used to model the soil or not. The approach described here can also be used for modeling pollution problems involving different boundary conditions.

UAV p­­­arcel Delivery System with Deep Reinforcement Learning Based Collision Avoidance and Route Optimization

Unmanned Aerial Vehicles (UAVs), or drones, have recently become a favorable solution for fast parcel delivery due to their maneuverability and advances in navigation technologies. With the limitation of battery capacity and payload of drones, it is crucial to consider both efficiency and cost while conducting the tasks. Meanwhile, UAVs should not collide with each other while traveling to customers. In this article, we propose a UAV parcel delivery system involving deep reinforcement learning (DRL) approach for collision avoidance and a genetic algorithm for route optimization. Specifically, a delivery center generates near-optimal routes, loading UAV with parcels according to demands. Each UAV takes charge of delivering packages in compliance with the assigned route while avoiding collision with each other. We utilize DRL to achieve collision avoidance without having prior knowledge about the trajectories of other UAVs. Additionally, we adopt a genetic algorithm to obtain the lowest energy cost path for each UAV. To find such an optimized path, we solve a capacitated vehicle routing problem (CVRP) with a modified cost function and extra constraints. Realistic simulations using a physics engine and software-in-the-loop (SITL) are conducted to evaluate the feasibility of the proposed methods.

Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network

AbstractFast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

Bioscience Students in Physics Courses with Higher Test Anxiety Have Lower Grades on High-Stakes Assessments and Women Report More Test Anxiety than Men

Test anxiety is beginning to be recognized as a significant factor affecting student performance in science, technology, engineering, and mathematics (STEM) courses, potentially contributing to gender inequity within these fields. Additionally, the management of test anxiety can improve self-efficacy, which is a construct that has been well studied in the physics context. In this study, we investigated the relationship between self-efficacy, test anxiety, and gender differences in performance in a two-semester-long introductory physics course sequence for bioscience students in which women outnumber men. Using validated survey data and grade information from students in a two-semester introductory physics course sequence, we compared the predictive power of self-efficacy and test anxiety on female and male students’ performance on both low- and high-stakes assessments. We found that there were gender differences disadvantaging women in self-efficacy and test anxiety in both Physics 1 and Physics 2, as well as gender differences in high-stakes outcomes in Physics 1. There were no gender differences in low-stakes assessment scores. We also found that self-efficacy and test anxiety predicted high-stakes (but not low-stakes) assessment outcomes in both Physics 1 and Physics 2. Comparison of these findings with prior studies involving physical science and engineering students shows that although women outnumber men in physics courses for bioscience students and the career goals of bioscience students are very different from the earlier researched group, most of the negative trends hold even for this new population. Thus, these findings, in a new context involving bioscience students in physics courses, are very important because they reinforce the systemic nature of women being affected more adversely by anxiety in high-stakes assessments, which is a threat to creating equitable and inclusive learning environments. An important implication is that course instructors should carefully consider how high-stakes and low-stakes assessments are used to determine grades and how to create an overall equitable, inclusive, and low-anxiety learning environment.

Preface to the Special Issue on “The 4th International Conference on Engineering Physics, MEMS-Biosensors and Applications (4ICEBA2023)”

Preface to the Special Issue on “The 4th International Conference on Engineering Physics, MEMS-Biosensors and Applications (4ICEBA2023)”

Preface: 7th Astechnova International Energy Conference (ASTECHNOVA 2023)

Abstract Astechnova International Energy Conference (ASTECHNOVA) is an international conference that is annually organized by the Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Indonesia. This 7th ASTECHNOVA was held on October 4-5, 2023. This conference provides an ideal platform for researchers, academicians, engineers, politicians, economists, energy enthusiasts, energy planners, and energy analysts, to share the recent research and development in the energy science discipline from various perspectives: New and Renewable Energies, Nuclear Technology, Energy Securities, Energy Efficiency, and Urban Infrastructure and Utilities The conference was attended by approximately 300 participants from various universities and institutions in Kiribati, India, Japan, South Korea, Malaysia, Thailand, Canada, the United States of America, Singapore, and Indonesia. The panel session of this conference includes one (1) keynote lecture and six (6) invited talks by the panel speakers from across the globe. As for the parallel session, oral presentations were delivered by the authors who submitted their research. In total, the ASTECHNOVA 2023 organizer accepted 64 paper submissions after being reviewed by distinguished experts in the field. The reviewing process has considerably reduced the number of published papers, but it also has raised the proceedings’ quality. Finally, we would like to thank all the participants, authors, panel speakers, reviewers, panel session moderators, parallel session chairs, steering committee, organizing committee, technical coordinators, and all other supporting staff for their tremendous support during this conference. Astechnova 2023 Editorial Team List of Committees are available in this Pdf.

Hydro-mechanical modeling of cohesive crack propagation of concrete lining in high internal pressure tunnels

High pressure tunnels with concrete lining have been extensively utilized in project practice. However, due to the characteristic of concrete being susceptible to cracking under tension, the lining inevitably develops cracks under high internal water pressure, posing a serious threat to the operation of tunnels. This study aims at developing a hydro-mechanical numerical model of cohesive crack propagation of concrete lining in high internal pressure tunnels. In this regard, the determination of cohesive element parameters is elucidated, the contact simulation within the software ABAQUS is improved to accurately characterize the interface between lining and surrounding rock, and the numerical calculation process in ABAQUS is realized using indirect coupled method. The simulation results align well with the physical model test and engineering monitoring data, demonstrating that the proposed method can accurately simulate the hydraulic interactions of high pressure tunnel. Additionally, a comparison with calculation models employing tie constraints to simulate the lining-surrounding rock interface is conducted. Finally, comparison with traditional continuum method reveals that while both methods exhibit consistent overall trends. It is recommended to choose the proposed method when describing the discontinuous propagation process of cracks, which cannot be simulated by the continuum analysis method.

Acquiring musculoskeletal skills with curriculum-based reinforcement learning

Efficient musculoskeletal simulators and powerful learning algorithms provide computational tools to tackle the grand challenge of understanding biological motor control. Our winning solution for the inaugural NeurIPS MyoChallenge leverages an approach mirroring human skill learning. Using a novel curriculum learning approach, we trained a recurrent neural network to control a realistic model of the human hand with 39 muscles to rotate two Baoding balls in the palm of the hand. In agreement with data from human subjects, the policy uncovers a small number of kinematic synergies, even though it is not explicitly biased toward low-dimensional solutions. However, selectively inactivating parts of the control signal, we found that more dimensions contribute to the task performance than suggested by traditional synergy analysis. Overall, our work illustrates the emerging possibilities at the interface of musculoskeletal physics engines, reinforcement learning, and neuroscience to advance our understanding of biological motor control.

Further Results for Hermite-Based Milne-Thomson Type Fubini Polynomials with Trigonometric Functions

This paper examines generating functions of r-parametric Hermite-based Milne-Thomson polynomials. Using generating function methods, the relationships among these polynomials, Fubini type polynomials, and trigonometric functions are given. Moreover, new formulas are derived by utilizing not only the generating functions of these polynomials but also associated functional equations. These formulas pertain to r-parametric Hermite-based sine-and cosine-Milne-Thomson Fubini polynomials, as well as Stirling type polynomials and numbers. Additionally, by analyzing special cases of newly obtained results, some known formulas are also derived. Furthermore, some identities involving secant and cosecant numbers are derived through the properties of trigonometric functions. Special polynomials and their generating functions are an important tool for solving some problems in many areas such as combinatorics and number theory. By introducing new formulas, this paper significantly enhances these problems-solving abilities in these areas. Consequently, these results have potential to shed light on important applications in mathematics, engineering, and mathematical physics.

Research on the high-performance computing method for the neutron diffusion spatiotemporal kinetics equation based on the convolutional neural network

Due to the uncertainty of computational results and the lack of interpretability of models in solving physical field equations in current deep learning, this paper designs a convolutional neural network that can be used to solve the neutron diffusion spatiotemporal kinetics equation in polar and cylindrical coordinate systems. This algorithm directly utilizes the macroscopic cross-section of the material without using the lattice homogenization method, replaces the finite volume method with the extended matrices, and solves the extended matrices using the convolutional kernels instead of the iterative algorithms. Taking the simplified Tsinghua High Flux Reactor (THFR) as an example, the feasibility of the algorithm is verified on the PyTorch platform and compared with the calculation results of the source iteration method running on the GPU. The calculation results show that when the number of grids in the radial and axial sections of the simplified THFR model is 804,600 and 3,576,000, respectively, and the algorithm is iterated 3000 times, the normalized power of the convolutional neural network and the source iteration method converges to 10−10, and the maximum point by point error of the neutron flux density of the above two algorithms converges to 10−5. The computational time consumed by the convolutional neural network is approximately 880.64 s and 3729.62 s, which reduces the computational time by 4.66% and 5.05% compared to the GPU parallel accelerated source iteration method, and the former consumes 43.75% less memory compared to the latter. The convolutional neural network is mainly used as the virtual physics engine for the THFR digital twin system, in addition to solving the neutron diffusion spatiotemporal kinetics equation and further improving computational speed. The algorithm directly utilizes the neutron macroscopic cross-section of the material to calculate the neutron flux density distribution without using the lattice homogenization, providing theoretical guidance and algorithm support for developing the high-precision multi-physical field coupling model.

Modeling the aspect ratio of commercial catalysts

AbstractMathematical modeling plays a diverse and crucial role in the chemical industry and academia. This manuscript describes how the physical and mechanical engineering disciplines help the commercialization of catalysts from evaluation to scale‐up and manufacturing. Forming and shaping the catalyst is the first basic step in the catalyst manufacturing industry. Part A shows some of the classic process methods that are employed for this purpose. Catalyst extrudates require proper engineering and process control of their aspect ratio. Catalyst extrudates with a high aspect ratio tend to yield a low reactor fill due to a loose packing. Catalyst extrudates with a low aspect ratio tend to yield high pressure drop. Part B introduces the tensile strength of the catalyst in order to predict the aspect ratio of catalyst extrudates in commercial plants. A force‐balance between the tensile strength of the catalyst and the impact forces due to collision experienced during manufacture leads to the dimensionless group . The group allows to introduce and quantify the severity of equipment and the severity of entire commercial plants.

Systematic derivation of [formula omitted] equations, its discretization using GTIN method and development of a switchable SP3 to [formula omitted] neutron transport solver

The Generalized Simplified Spherical Harmonics (GSPN) method has drawn recent interest owing to the fact that theoretically, it is equivalent to the PN and does not rely on any assumption other than piecewise homogeneity. Its level wise approach to PN offers an agreeable balance between the accuracy and computational efficiency making it a valuable mathematical tool for analyzing neutron transport problems pertaining to nuclear engineering and reactor physics. The lowest level approximation for N = 3, expressed as GSP3(0), offers reducing the complexity while still capturing the essential physics. In the current work, GSP3(0) equations are derived along with the interface and boundary conditions for linear anisotropic scattering. These equations are then discretized using the generalized transverse integration nodal method for a two-dimensional system of piecewise homogeneous rectangular regions. The discretization is done with an option kept for reducing the GSP3(0) formalism to the conventional SP3. Subsequently, a computer code has been developed to solve these discretized equations in the multigroup structure for vacuum, reflective or albedo boundaries. At present, this switchable SP3/GSP3(0) code has the capability of estimating k-eigenvalue and position dependent scalar neutron flux within a given two-dimensional rectangular geometry. The code has been verified by solving SP3, GSP3(0) and other benchmark problems from available literature. The results obtained from our code demonstrate that GSP3(0) is superior to the conventional SP3 and comparable to the recently proposed SDPN (N = 1,2,3) [M. Nazari, A. Zolfaghari, M. Abbasi, Prog. Nucl. Energy, 2023; 166, 104,933] in terms of accuracy as well as computation time.

Reconstruction method of high-precision longwall mining floor curved surface model driven by data points fitting of equipment

Abstract High-precision coal seam model is the basis of intelligent mining. The longwall mining face floor model, which can provide data sources for the correction of the dynamic coal seam model, is difficult to measurement directly. To address this issue, this paper proposes a high-precision coal seam surface model reconstruction and correction method based on a large number of operating data points. Firstly, the coupling model of equipment and floor is obtained based on the coupling model of plane and surface. Subsequently, the plane data points of the equipment are corrected based on the coupling model of equipment and floor to obtain the floor reconstruction points. The Catmull–Clark surface subdivision method is then used to subdivide the plane formed by the equipment data points to obtain surface subdivision points. The floor model reconstructed using surface data points is validated and corrected using the physics engine in the Unity 3d platform. Finally, the verification of the reconstruction point selection method, the coupling principle between the equipment and the floor model, the reconstruction accuracy of the base plate and the correction principle were carried out based on the equipment and floor model in the laboratory. The experimental results show the feasibility of the floor reconstruction, verification and correction method, which can provide a new idea for the reconstruction of the floor of the longwall mining face and the correction of the dynamic coal seam.

Transient NOx emission modeling of a hydrogen-diesel engine using hybrid machine learning methods

One promising approach to reduce carbon foot print of internal combustion engines (ICEs) is using alternative fuels like hydrogen, particularly by converting medium and heavy-duty diesel engines to dual-fuel hydrogen-diesel engines. To minimize elevated NOx emissions from hydrogen-fueled engine, fast and accurate emission models are essential for engine model-based control and for engine calibration and optimization using hardware-in-the-loop (HIL) setups. In this study, a fast-response NOx emissions sensor is used to measure the transient NOx emissions from a dual-fuel hydrogen-diesel engine. Subsequently, steady-state models (SSMs), quasi steady-state models (QSSMs), and transient sequential models (TSMs) in the form of black-box (BB) and gray-box (GB) models are developed for transient NOx emissions prediction. GB models utilize both information from a one dimensional (1D) physical engine model and experimental data for training, while BB models only use experimental data. SSMs are optimized artificial neural networks (ANNs) trained using steady-state data, QSSMs are optimized ANNs trained using transient data, and TSMs are time-series networks trained using transient data. Long short-term memory (LSTM) and gated recurrent unit (GRU) networks are used as the time-series deep learning networks. The results showed that the 1D physical model has the poorest performance with successive model performance improvement from SSM to QSSM and from QSSM to TSM. The developed BB TSM model in this study can predict transient NOx emissions with an R2 value greater than 0.96 at 89,000 predictions per second which makes this model suitable for real-time engine model-based control where computational efficiency is crucial. The developed GB TSM model can predict transient NOx emissions with an R2 value greater than 0.97 but it is computationally more expensive. The extra accuracy of the GB TSM models makes them the best choice for HIL setups where more computational power is available, and accuracy is more crucial.

Decoding predicted future states from the brain’s ‘physics engine’

Decoding predicted future states from the brain’s ‘physics engine’

An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.

Mesoscale simulation method for preplaced aggregate concrete based on physics engine

Preplaced aggregate concrete (PAC) has up to 60 % aggregate volume content, making mesoscale modelling difficult. A novel mesoscale simulation method based on physics engine was proposed in this study to accurately simulate the coarse aggregate skeleton of PAC. Unlike in conventional mesoscale simulations, point-to-point contacts between aggregates were formed due to virtual gravity in the physics engine. To verify this novel method, mechanical property and shrinkage behavior tests of PAC were conducted under different aggregate gradings and with different expansive agents. For the mechanical property test, the simulation results were compared with the test results in terms of the aggregate stacking state, failure mode, stress–strain curve, and mechanical property parameters. Repeatability and aggregate shape analyses were performed using finite element (FE) model. A similar comparison between the simulation and test results was also performed for the shrinkage behavior. A shrinkage coefficient reflecting the relationship between grout and PAC shrinkage was proposed. This novel mesoscale simulation method based on physics engine is particularly suitable for PAC. However, it is not limited to PAC and can be applied to the simulation of conventional concrete, thus solving the problem of traditional simulation methods that struggle to achieve a high aggregate fraction.