Knowledge Of Physics Research Articles

Evaluating direct vessel injection accident-event progression of AP1000 and key figures of merit to support the design and development of water-cooled small modular reactors

The passive safety systems (PSSs) within water-cooled reactors are meticulously engineered to function autonomously, requiring no external power source or manual intervention. They depend exclusively on inherent natural forces and the fundamental principles of reactor physics, such as gravity, natural convection, and phase changes, to manage, alleviate, and avert the release of radioactive materials into the environment during accident scenarios like a loss-of-coolant accident (LOCA). PSSs are already integrated into such operating commercial reactors as the Advanced Pressurized Reactor-1000 MWe (AP1000) and the Water-Water Energetic Reactor-1200 MWe (WWER-1200) are adopted in most of the upcoming small modular reactor (SMR) designs. Examples of water-cooled SMR PSSs are the passive emergency core-cooling system (ECCS), passive containment cooling system (PCCS), and passive decay-heat removal system, the designs of which vary based on reactor system-design requirements. However, understanding the accident-event progression and phases of a LOCA is pivotal for adopting a specific PSS for a new SMR design. This study covers the accident-event progression for direct vessel injection (DVI) small-break loss-of-coolant accident (SB-LOCA), associated physics phenomena, knowledge gaps, and important figures of merit (FOMs) that may need to be evaluated and assessed to validate thermal-hydraulics models with an available experimental dataset to support new SMR design and development.

Problem-Based Learning versus Traditional Learning in Physics Education for Engineering Program Students

Problem-based learning (PBL) is an educational method which involves learning by solving real problems. However, applying a PBL approach to engineering subjects in some cases became a challenging issue. Thus, it is important to determine the best ways in which engineering students can learn physics more effectively and solve problems relevant to their daily lives. The main aim of this study was to compare the final assessment grades in a physics course for students in the engineering program using PBL and traditional learning (TL) methods. The advantages and disadvantages of PBL and TL methods from the practical experience of lecturers are also presented. The study presented data of 460 first-year engineering students taking the physics course at Kaunas University of Technology. The information presented in this study is based on the insights, observations, and individual experiences of the authors as teaching staff. The results indicated that the application of the PBL method allows for enhancement in the teamwork, presentation, and critical thinking skills in physics. However, the TL method promotes the individual learning skills and wider theoretical knowledge in physics of students and is more suitable when the exam only includes closed-ended questions.

Preface: DIDFYZ 2023

Searching for more effective ways of educating the young generation in physics means not only reforming the content and methods from the point of view of us creators and teachers, but mainly understanding the object of education, i.e. a young person - a student leaving school. At the same time, social changes are reflected in the structure of society’s needs for education, in the needs of young people, their approach to their environment, to fulfilling their needs and their attitude to global issues. The ability of society to respond to these requirements is determined mainly by the state and level of education. The advancement of knowledge, social relations and the economy depends on it. The main task of educational systems is to convey new knowledge in science to young people, but also to shape their personality. In physics, this role is particularly important, because knowledge of physics determines knowledge in other areas as well. We have been dealing with the issue of the transfer of physical knowledge within the framework of physics teaching theory, or in other words physics didactics, for more than half a century. Over the past 50 years, we have marked a qualitative shift in the theory and practice of this young scientific discipline. The organizers of the DIDFYZ conferences aimed not only to participate in these changes, but to be their initiators and creators. Regular biennial meetings became a platform for the creation of new ideas in the theory of teaching physics, which gradually grew into the internationally recognized DIDFYZ conference.The idea of organizing regular international meetings of physicists who deal with education in physics arose in the seventies of the last century. The Department of Physics of the Constantine the Philosopher University in Nitra took on the organizational role of the aforementioned conference on current problems of education in physics. It first organized a conference called DIDFYZ in 1977 in Bojnice. Other conferences were held in Nitra, in Rackova dolina in the Western Tatras, and since 2019 the venue of conference is held in Terchová (Vrátna dolina) in the beautiful mountain environment of Malá Fatra National Park.The topics of the DIDFYZ conferences over the past 46 years have always had the ambition to deal with the fundamental problems of physics education. They defined urgent problems that need to be solved at a given time (introduction of new teaching technologies, new physical discoveries and the ability to interpret them, problems of worldview, new trends in experiments, personal development, development of skills).The 23rd DIDFYZ 2023 conference entitled “New approaches in physics education for generation Z” set itself the goal of reflecting the characteristics of the educated generation referred to as “Generation Z” and “Alpha” in physics education, in the content, activities and methods used in teaching physics.Our pupils can be understood as consumers of communicated physical knowledge. Our task is to think in more detail about the characteristics of the generation we are addressing when creating physical education. Identifying which generations we are addressing means at the same time knowing how to address them in order to win them over and motivate them to learn about physics. In today’s education, the generations known as “Generation Z” and “Generation Alpha” face us. The correct grasp of their education is the gateway to the successful creation of the future.The organizers and participants of the conference believe that their efforts and the results presented at the conference will not only bring new knowledge about the possibilities of improving the teaching of physics, but will also contribute to a fundamental change in the quality and level of education and, in particular, to an increase in society’s interest in the problems of education and the educational system.Ľubomír Zelenický, Ľubomíra Valovičová, and Anton Trník EditorsList of Committees, International Scientific Committee, Organizing Committee are available in this Pdf.

The importance of context and framing: Supporting undergraduate students apply physics knowledge in biology

The importance of context and framing: Supporting undergraduate students apply physics knowledge in biology

Does generation Z (and Alpha) need physics as a separate school subject?

Physics teachers and learners collected surveys focused on their belief of teaching and learning physics at secondary and high schools. In the context of the idea of STEM education, physics cannot be replaced by another subject - science - to a sufficient extent. All the interviewed respondents agreed on this. The learning environment is now different, the position of the teacher is different too. But the aim of education is to prepare learners for the life in the 21st century and physics plays an important role. Pupils have to acquire the necessary competences, and skills for their application on the labor market at the time of the so-called fourth industrial revolution. This aim of education is essential but not negligible is their well being at schools. It is known that a crucial trend among young people is the incidence of depression and early school leaving. The outcome of our investigation has shown that in physics a lot of basic cognitive skills are taught. Physics is also a platform for buiding skills needed in the 21st century – communication, creativity, critical thinking. Physics is an essential part of STEM education. The results of the surveys were incorporated into the subjects of the pregraduate physics teacher training. The basic trend in teaching physics is quality versus quantity, the use of physics knowledge in everyday life. The way how physics teachers deal with these new objectives, which methods were chosen will be discussed in this paper.

Simulation of the motion of a pendulum

The project's objective is to simulate movement in the study and analysis of motion simulation problems and to propose simulation algorithms based on numerical calculation methods. It has many practical application values. In engineering and science, it is often necessary to simulate and analyze the motion of the fold to study the motor and dynamic properties of the fold, providing the theoretical basis and solutions to practical problems. With the support of modern computers and numerical computing technology, the problem of motion simulation has become a popular research direction. Many scholars and engineers have proposed different numerical calculation methods and simulated algorithms to simulate the motion process of the fold and analyze its motion laws. This article introduces the basic knowledge of physics and the formulas of motion, as well as some important concepts and theories related to motion. The motion simulation algorithm was then analyzed and discussed in detail. Subsequently, numerical calculations were prepared using MATLAB software, and simulated experiments were conducted using examples to analyze dynamic changes. Finally, the prospects for the future direction of research are presented. Therefore, if the initial speed is the same, the width and length of the time will increase.

Applying Physics Knowledge and STEAM Education in High School: Connecting Traditional Vietnamese Culture Through the Moon-Shaped Lute Production Project

<p style="text-align: justify;">This article explores the integration of physics knowledge and science, technology, engineering, arts, and mathematics (STEAM) education in high schools through a unique project that connects traditional Vietnamese culture with the production of moon-shaped lutes. The moon-shaped lute, known as “Đàn Nguyệt” in Vietnamese, holds great cultural significance in Vietnam. By incorporating physics principles into the lute production process, students develop a deeper understanding of its construction, sound production, and structural stability. They explore concepts like material selection, sound propagation, and ergonomic design to enhance the instrument’s craftsmanship and playability. This interdisciplinary approach not only fosters scientific and technical skills but also preserves and promotes traditional Vietnamese culture. Engaging students in projects that combine art, science, and culture highlights the power of STEAM education in nurturing well-rounded individuals who have a strong connection to their heritage. The integration of physics knowledge and cultural elements in this project demonstrates the value of interdisciplinary learning and its impact on students’ appreciation for their cultural identity.</p>

Physics-informed deep learning for scattered full wavefield reconstruction from a sparse set of sensor data for impact diagnosis in structural health monitoring

This paper presents a physics-informed deep learning framework for the reconstruction of full scattered spatiotemporal Lamb wavefields (video images) in plate-like structures from a sparse set of time-series sensor data. The reconstructed scattered wavefield contains a wealth of information about the wave propagation phenomenon including any interactions of the propagating wave with damage in the structure. This information is paramount for damage diagnosis as is demonstrated in this paper via impact diagnosis—a key structural health monitoring application. A physics-informed neural network (PINN) that encodes the underlying elastodynamic field equations into the learning/training process in the neural network is proposed for this purpose. This prior wavefield physics knowledge embedded in the loss function acts as a regularization agent for the minimization problem in the neural network training, thereby enabling the extrapolation of a sparse set of one-dimensional time-series signals into two-dimensional scattered wavefield. The wavefield reconstruction capabilities of the proposed supervised forward PINN framework are first verified both numerically and experimentally for a stiffened aluminum panel under a couple of narrowband ultrasonic-frequency excitations, and the results confirm its robustness to low spatial resolution and substantial noise in the measured sensor data. The PINN requires far fewer sensors for scattered wavefield reconstruction, thereby permitting for a higher sensor spacing or lower spatial sampling. To this end, it is shown that a sensor spacing of 5λ generates good wavefield reconstruction accuracy, which is a 10-fold increase over the Nyquist–Shannon sampling limit (λ/2). Two sets of experiments are then conducted on a long-stiffened aluminum panel to validate the proposed framework via low-velocity impact diagnosis in the near-ultrasonic frequency range. The first set of experiments, with the known excitation force incorporated into the PINN, allows the wavefields to be accurately reconstructed with the sensor spacing up to 5λ as expected. The second set of experiments assumes unknown impact force history—a classical case of impact diagnosis where the impact force history is not known a priori. It is shown that the wavefield reconstruction through PINN still provides good accuracy albeit with a less generous sensor spacing of 2λ. A convolutional neural network long short-term memory (CNN-LSTM) model then solves the mathematically inverse problem of inferring the impact location and impact force history by analyzing the reconstructed impact generated wavefield. The impact location is predicted well with 93% accuracy, and the impact force history is reconstructed with 90% accuracy, further validating the proposed framework.

Velocity Profile Geometries and Granular Temperature Distributions in Very Dense Granular Flows

AbstractUnderstanding the motion of particles in very dense granular flows is crucial for comprehending the dynamics of many geological phenomena, and advancing our knowledge of granular material physics. We conduct transparent ring shear experiments to directly observe the granular motion under relatively high‐pressure conditions, and find that the granular velocity non‐linearly decays, forming an approximately 7‐particle‐diameter‐thick localized shear band. A fitting curve underlying non‐local physics can be used to well predict velocity profile geometries that are almost independent of normal stress and shear velocity. Moreover, experimental results show monotonically decreasing granular kinetic temperature, which may be caused by energy dissipation due to more inelastic contacts under high confining pressures. The variation of granular temperature will significantly influence the local yield stress and rheological properties, which may lead to inhomogeneous fluidity of the material and thus to shear localization in very dense granular flows.

Physics-guided deep learning for damage detection in CFRP composite structures

Structural health monitoring of carbon fiber reinforced plastics composite structures based on deep learning usually builds on black box models for damage detection. However, the lack of physics knowledge limits its application into real-world problems where structural property or environmental condition varies. To overcome this limitation, we propose a physics-guided deep learning framework to integrate physics into data-driven models. This physics-guided convolutional neural network leverages structural degradation trend and physical consistency by combining the output of the physical model with the observed feature in a hybrid model. This hybrid model uses an additional branch to observe the information of stiffness degradation, which is the input into the physical model to describe the damage growth in structures by establishing a relationship with the power spectral density change in the guided wave signals. Additionally, the physics-based loss function is designed as part of the learning objective to ensure the model outputs satisfy the existing physics and maintain physical consistency. Experiment results show that our method demonstrates great generalizability that by training on data of only one particular CFRP composite structure, the PGCNN model can expand its great performance on all other CFRP layups in the NASA-published CFRP Dataset with over 90% accuracy.

A review and reformulation of macroscopic realism: resolving its deficiencies using the framework of generalized probabilistic theories

The notion of macrorealism was introduced by Leggett and Garg in an attempt to capture our intuitive conception of the macroscopic world, which seems difficult to reconcile with our knowledge of quantum physics. By now, numerous experimental witnesses have been proposed as methods of falsifying macrorealism. In this work, I critically review and analyze both the definition of macrorealism and the various proposed tests thereof, identifying a number of problems with these (and revisiting key criticisms raised by other authors). I then show that all these problems can be resolved by reformulating macrorealism within the framework of generalized probabilistic theories. In particular, I argue that a theory should be considered to be macrorealist if and only if it describes every macroscopic system by a strictly classical (i.e., simplicial) generalized probabilistic theory. This approach brings significant clarity and precision to our understanding of macrorealism, and provides us with a host of new tools – both conceptual and technical – for studying macrorealism. I leverage this approach i) to clarify in what sense macrorealism is a notion of classicality, ii) to propose a new test of macrorealism that is maximally informative and theory-independent (unlike all prior tests of macrorealism), and iii) to show that every proof of generalized contextuality on a macroscopic system implies the failure of macrorealism.

Phy-Taylor: Partially Physics-Knowledge-Enhanced Deep Neural Networks via NN Editing.

Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-knowledge-enhanced DNN framework called Phy-Taylor, accelerating learning-compliant representations with physics knowledge. The Phy-Taylor framework makes two key contributions; it introduces a new architectural physics-compatible neural network (PhN) and features a novel compliance mechanism, which we call physics-guided neural network (NN) editing. The PhN aims to directly capture nonlinear physical quantities, such as kinetic energy, electrical power, and aerodynamic drag force. To do so, the PhN augments NN layers with two key components: 1) monomials of the Taylor series for capturing physical quantities and 2) a suppressor for mitigating the influence of noise. The NN editing mechanism further modifies network links and activation functions consistently with physics knowledge. As an extension, we also propose a self-correcting Phy-Taylor framework for safety-critical control of autonomous systems, which introduces two additional capabilities: 1) safety relationship learning and 2) automatic output correction when safety violations occur. Through experiments, we show that Phy-Taylor features considerably fewer parameters and a remarkably accelerated training process while offering enhanced model robustness and accuracy.

Do students have sufficient knowledge of physics to take marine biology courses?

Physics and biology are two branches of science that have many connections. In marine biology courses, students must have a good basic concept of physics. The purpose of this study was to determine whether students’ physics knowledge is sufficient to follow marine biology lectures. This study is a quantitative descriptive study. The study was conducted in May 2024 in the Biology Education study program at Raja Ali Haji Maritime University involving 27 students in their third year of study. Data collection was carried out by testing and analyzing descriptively. The results of the study showed that students did not have sufficient knowledge of physics to be able to follow marine biology courses. This is indicated by the problems found by students in solving questions related to the Effect of the position of the earth, moon, and sun on sea tides, the relationship between salinity and density of seawater, the specific heat of seawater, and the impact of global warming on increasing temperature and rising sea levels. Therefore, action is needed to strengthen the basic concepts of physics of biology students so that when taking biology lectures related to physics, students will be able to understand them well.

Constraint-Aware Learning for Fractional Flow Reserve Pullback Curve Estimation from Invasive Coronary Imaging.

Estimation of the fractional flow reserve (FFR) pullback curve from invasive coronary imaging is important for the intraoperative guidance of coronary intervention. Machine/deep learning has been proven effective in FFR pullback curve estimation. However, the existing methods suffer from inadequate incorporation of intrinsic geometry associations and physics knowledge. In this paper, we propose a constraint-aware learning framework to improve the estimation of the FFR pullback curve from invasive coronary imaging. It incorporates both geometrical and physical constraints to approximate the relationships between the geometric structure and FFR values along the coronary artery centerline. Our method also leverages the power of synthetic data in model training to reduce the collection costs of clinical data. Moreover, to bridge the domain gap between synthetic and real data distributions when testing on real-world imaging data, we also employ a diffusion-driven test-time data adaptation method that preserves the knowledge learned in synthetic data. Specifically, this method learns a diffusion model of the synthetic data distribution and then projects real data to the synthetic data distribution at test time. Extensive experimental studies on a synthetic dataset and a real-world dataset of 382 patients covering three imaging modalities have shown the better performance of our method for FFR estimation of stenotic coronary arteries, compared with other machine/deep learning-based FFR estimation models and computational fluid dynamics-based model. The results also provide high agreement and correlation between the FFR predictions of our method and the invasively measured FFR values. The plausibility of FFR predictions along the coronary artery centerline is also validated.

Towards a better comprehension of reactive transport coupling experimental and numerical approaches

In this work we focus on further understanding reactive transport in carbonate rocks, in particular limestones characterized by a bimodal pore size distribution. To this end, we performed injection experiments with CO2-saturated water on a sample of Euville limestone and monitored the experiments with a medical CT scanner. Microscanner imaging was performed before and after alteration. Experiments showed that permeability increased by nearly two decades due to the alteration process. This increase could be attributed to the formation of a preferential dissolution path visualized on the CT images. Microscanner images show that preferential dissolution areas are characterized by the presence of numerous enlarged macropores. The preferential dissolution path created therefore retains a porous structure and does not correspond to a wormhole-type channel. To provide further knowledge of the small-scale physics of reactive transport, we performed Lattice-Boltzmann simulations of flow in a numerically generated model 2D porous medium having geometrical and topological features designed to approach Euville limestone. We showed that the fluid velocity increased in nearly percolating paths of macropores. Considering the experiments, this means that the CO2-saturated water starts to enter high-velocity zones earlier than low-velocity zones, inducing an earlier onset of the alteration process and a more pronounced local dissolution. However, numerical results showed that the alteration of non-connected macropores leads to an increase of permeability much smaller than the experimentally observed one. To explain this fact we used effective medium modelling that permits predicting the variation in permeability as a function of the fraction of macropores and consequently as a function of alteration. It proved that as long as there is no alteration-induced percolating path consisting of macropores, the increase in permeability is relatively low as shown by the Lattice-Boltzmann simulations. An increase in permeability of several orders of magnitude is only observed when the macroporosity is close to the percolation threshold. This fact is in accordance with the experimentally observed results.

An Approach Inspired by Quantum Mechanics for the Modeling of Large Power Systems

The ever increasing complexity of power systems mandates a further improvement of the numerical methods used for their simulation. We propose a new approach that is inspired by an exact analogy with quantum mechanics, but requires no specific knowledge of quantum physics. This approach allows in particular to use local methods commonly applied in quantum mechanics, such as Lanczos' algorithm. It is applied to computing the load flow and extended to simple dynamic studies where the nodes are described by swing equations. The first set of results are for an ideal 2D-lattice grid to illustrate the method and its main features. Then a contingency analysis by the proposed approach is performed for various realistic power systems, including a model of the European network. This demonstrates the interest of the new approach and its efficiency when compared with state-of-the-art methods, especially for large power system.

Dynamically orthogonal narrow-angle parabolic equations for stochastic underwater sound propagation. Part I: Theory and schemes.

Robust informative acoustic predictions require precise knowledge of ocean physics, bathymetry, seabed, and acoustic parameters. However, in realistic applications, this information is uncertain due to sparse and heterogeneous measurements and complex ocean physics. Efficient techniques are thus needed to quantify these uncertainties and predict the stochastic acoustic wave fields. In this work, we derive and implement new stochastic differential equations that predict the acoustic pressure fields and their probability distributions. We start from the stochastic acoustic parabolic equation (PE) and employ the instantaneously-optimal Dynamically Orthogonal (DO) equations theory. We derive stochastic DO-PEs that dynamically reduce and march the dominant multi-dimensional uncertainties respecting the nonlinear governing equations and non-Gaussian statistics. We develop the dynamical reduced-order DO-PEs theory for the Narrow-Angle parabolic equation and implement numerical schemes for discretizing and integrating the stochastic acoustic fields.

Developing a New Web Service for Experimental Nuclear Reaction Database (EXFOR) Using RESTful API and JSON

Efficient data mining from the Experimental Nuclear Reaction Database (EXFOR) has a potential for utilization of modern computational analysis techniques to find trends, shortcomings and hidden patterns in the database, which in turn helps improve our knowledge of nuclear physics. To facilitate the data mining, we have developed two EXFOR parsing computer programs (EXFOR Parser) to convert the data in the database stored in the EXFOR format into the widely adopted JSON format. The converted JSON data are used for further processing to extract individual physical observables and generate tabulated data (x, y, dx, dy) where all units of measurement are standardized. Furthermore, we have developed REST APIs and an open web system for easy access and quick visualizations of these converted datasets.

Stephen Hawking's Warning on Contacting Aliens: A Physics Perspective on the Intelligence Trap.

The search for extraterrestrial intelligence is a fascinating and important endeavor, but it raises significant ethical and safety concerns. In the search for extraterrestrial intelligence, scientists use knowledge of physics to identify potential communication methods and signals that may be used by extraterrestrial civilizations. One of the most notable scientists to highlight these concerns was the late physicist Stephen Hawking, who cautioned that actively attempting to communicate with extraterrestrial civilizations could harm humanity. While it is true that we cannot predict the intentions of any potential extraterrestrial civilizations, some scientists argue that the potential advantages of seeking contact outweigh the potential risks. Deciding to initiate contact with extraterrestrial civilizations is a complex issue that requires balancing scientific curiosity with concerns for our own safety. The "Intelligence Trap" is a concept in psychology that suggests that highly intelligent people are more susceptible to cognitive biases and flawed thinking than less intelligent people. It can be argued that Hawking's warnings may be an example of the so-called intelligence trap, as some evidence from the field of physics suggests. Nonetheless, Hawking emphasized that it is crucial for scientists and policymakers to carefully weigh the potential risks and benefits of such efforts and proceed with caution.

Physics knowledge-based transfer learning between buildings for seismic response prediction

The recent advance in deep learning has attracted considerable interest for employing the state-of-the-art methods to solve engineering problems. However, the applicability of machine learning based models is hindered by the high cost of big data acquisition and task-specific difficulties. This paper presents a framework of physics knowledge-based transfer learning (Phy-KTL) neural networks that integrates the powerful learning capacity of physics-informed neural networks (PINNs) and the flexible transferability of model-based transfer learning technique to enhance structural seismic response prediction in the context of limited labelled datasets. The leverage of physics knowledge (represented by Runge-Kutta solver) allows the neural networks to better capture the structural nonlinear pattern. The use of model-based transfer learning improves the model generality by transferring features extracted from the source building to target buildings. The effectiveness of Phy-KTL in predicting seismic responses between target buildings is numerically validated as compared with Data-driven neural networks, PINNs, and Data-based transfer learning (Data-KTL). A practical application, which uses Phy-KTL to transfer features extracted from the numerical model to the physical building tested on the shaking table, validates that Phy-KTL is robust and effective to improve seismic response prediction of target buildings with limited labelled data.