Physical Principles Research Articles

On the nature of sub-THz continuum absorption in CO2 gas, its mixture with Ar, and in pure water vapor.

Detailed analysis of the unique broadband millimeter-wave (70-360 GHz) collision-induced absorption spectra in pure CO2 and in its mixture with Ar is presented. The nature of the observed continuum absorption is examined using classical trajectory simulation along with statistical physics consideration. Bimolecular continuum is decomposed in the phase space into separate contributions from the so-called free, quasibound, and true bound molecular pairs, the proportions of which greatly vary with temperature. This partitioning is supported by consideration of the second virial coefficient and excluded volume in pure CO2, Ar, and CO2-Ar. Close similarity between collision-induced absorption in the CO2 containing gases and the water vapor continuum in the subterahertz spectral range is demonstrated. This similarity suggests that the physical principles underlying both continuum absorption phenomena have much in common and, therefore, can be used for continuum modeling.

Symmetry Breaking of Three Self-Organization Rules: A General Theory for the Origin of Complexity

Complex spatiotemporal patterns in nature significantly challenge reductionism-based modern science. The lack of a paradigm beyond reductionism hinders our understanding of the emergence of complexity. The diversity of countless patterns undermines any notion of universal mechanisms. Here, however, we show that breaking the symmetry of three simple and self-organization rules gives rise to nearly all patterns in nature, such as a wide variety of Turing patterns, fractals, spiral, target and plane waves, as well as chaotic patterns. The symmetry breaking is rooted in the basic physical quantities, such as positive and negative forces, space, time and bounds. Besides reproducing the hallmarks of complexity, we discover some novel phenomena, such as abrupt percolation of Turing patterns, phase transition between fractals and chaos, chaotic edge in traveling waves, etc. Our asymmetric self-organization theory established a simple and unified framework for the origin of complexity in all fields, and unveiled a deep relationship between the first principles of physics and the complex world.

A mechanistic model to predict headspace concentration profile of carvacrol for controlled release packaging

The efficacy of an antimicrobial agent in food packaging depends largely on its concentration profile in the package headspace. In this study, a mechanistic mathematical model was developed to predict the headspace concentration profile of carvacrol, a prominent antimicrobial agent, using the physical principles that govern the movement of carvacrol molecules in a controlled release packaging system. The model and its estimated parameter values were validated using three different sets of gas chromatography measurements—this rigorous process confirms the accuracy and reliability of the model. Advancing beyond existing models, our model enhances the understanding of the dynamics between the concentration profile and its interacting processes including evaporation, adsorption, and gas transmission. It provides packaging developers with a superior alternative to the trial-and-error approach to tailor concentration profiles for optimizing antimicrobial efficacy. It leads to more efficient and better design of controlled release packaging.

Neutralized, intense-ion beams for fusion

A charge- and current-neutralized, intense-ion beam (IIB) will propagate undeflected in a magnetized plasma by the E×B , or diamagnetic, drift for a distance large compared to the beam-ion gyro-radius. This propagation occurs because of collective plasma effects when the beam-energy density is sufficient to sustain the polarization-electric field, or diamagnetic-screening currents. Our principal interest is the E×B drift, with β=Ebeam/Efield<1 . IIBs are characterized by parameters in the range: 0.1–2 MeV ion energy, 1–100 MA m−2 ion-current density, and 17–350 kW average power produced on repetitively-pulsed systems. Multi-MW average-beam power is possible with appropriate modifications to the power supply and ion-source/accelerator. IIBs can be focused geometrically, and/or magnetically, to spot sizes of the order of a few cm’s and their angular divergence is typically, ∼15 mRadians, suitable for long-range propagation in a vacuum beam-line. IIBs lose energy and momentum in the same manner as particles injected by a neutral-beam injector (NBIs), hence could be useful for fusion applications, including: heating, current drive, fueling, profile modifications, etc and such applications have yet to be thoroughly studied. Summarized here are the physical principles accounting for IIB propagation; ion-source designs used to produce the IIB; and pulsed-power methods for energizing the IIB accelerator. This technology base informs scalable metrics for the ion-source/accelerator (excluding the HV power supply and interconnects) used in a conceptual injector that provides the same ion energy and injected power (1 MeV, 17 MW) as NBIs used on ITER. A comparison indicates that the IIB injector would be much smaller in size, lower cost, and have much greater efficiency, while also providing for real-time modulation of the beam energy and intensity and the use of small-diameter injection ports that could minimize fuel contamination and magnetic-field leakage between the IIB injector and tokamak.

Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Monitoring forest carbon (C) stocks is essential to better assess their role in the global carbon balance, and to better model and predict long-term trends and inter-annual variability in atmospheric CO2 concentrations. On a national scale, national forest inventories (NFIs) can provide estimates of forest carbon stocks, but these estimates are only available in certain countries, are limited by time lags due to periodic revisits, and cannot provide spatially continuous mapping of forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on a global scale with good spatial (50–100 m) and temporal (annual) resolutions. Remote sensing has been used for several decades to monitor vegetation. However, traditional methods of monitoring AGB using optical or microwave sensors are affected by saturation effects for moderately or densely vegetated canopies, limiting their performance. Low-frequency passive microwave remote sensing is less affected by these saturation effects: saturation only occurs at AGB levels of around 400 t/ha at L-band (frequency of around 1.4 GHz). Despite its coarse spatial resolution of the order of 25 km × 25 km, this method based on the L-VOD (vegetation optical depth at L-band) index has recently established itself as an essential approach for monitoring annual variations in forest AGB on a continental scale. Thus, L-VOD has been applied to forest monitoring in many continents and biomes: in the tropics (especially in the Amazon and Congo basins), in boreal regions (Siberia, Canada), in Europe, China, Australia, etc. However, no reference study has yet been published to analyze L-VOD in detail in terms of capabilities, validation and results. This paper fills this gap by presenting the physical principles of L-VOD calculation, analyzing the performance of L-VOD for monitoring AGB and reviewing the main applications of L-VOD for tracking the carbon balance of global vegetation over the last decade (2010–2019).

Quantum work: Reconciling quantum mechanics and thermodynamics

It has been claimed that no protocol for measuring quantum work can satisfy standard physical principles, casting doubts on the compatibility between quantum mechanics, thermodynamics, and the classical limit. In this Letter, we present a solution for this incompatibility. We demonstrate that the standard formulation of these principles fails to address the classical limit properly. By proposing changes in this direction, we prove that all the essential principles can be satisfied when work is defined as a quantum observable, reconciling quantum work statistics and thermodynamics. Published by the American Physical Society 2024

A Novel Consolidation Analysis Framework: Universal Function Approximators Regularized by Physical Principles

Analytical and numerical techniques are widely used to analyse and interpret soil consolidation problems. An important limitation is the requirement for significant geotechnical knowledge and expertise to derive ‘true’ solutions. This study proposes an alternative: a universal function approximator regularized with known physical principles. The proposed approach here advances previous work to solve one-dimensional (1D) consolidation considering both self-weight and large strains, as well as two-dimensional (2D) consolidation by vertical drains. Initial and boundary conditions of the studied consolidation problems are first strongly and weakly enforced for comparison. A neural network is adopted here as the function approximator. To boost prediction accuracy, a novel strategy is proposed to adaptively sample data points for training. An estimate of epistemic uncertainty is achieved using the confidence interval of ensembled multiple outputs. The results show that the proposed approach accurately predicts the behaviours of complex consolidation processes. Results also indicate that regularization using weak physical constraints can alleviate the imbalance of back-propagated gradients of different loss terms and, in turn, achieve higher accuracy. The proposed method is generic, mesh-free, more robust and can be applied to a wide range of geotechnical problems.

Relativistic Roots of κ-Entropy

The axiomatic structure of the κ-statistcal theory is proven. In addition to the first three standard Khinchin–Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality axiom and the scaling axiom. It is shown that both the κ-entropy and its special limiting case, the classical Boltzmann–Gibbs–Shannon entropy, follow unambiguously from the above new set of five axioms. It has been emphasized that the statistical theory that can be built from κ-entropy has a validity that goes beyond physics and can be used to treat physical, natural, or artificial complex systems. The physical origin of the self-duality and scaling axioms has been investigated and traced back to the first principles of relativistic physics, i.e., the Galileo relativity principle and the Einstein principle of the constancy of the speed of light. It has been shown that the κ-formalism, which emerges from the κ-entropy, can treat both simple (few-body) and complex (statistical) systems in a unified way. Relativistic statistical mechanics based on κ-entropy is shown that preserves the main features of classical statistical mechanics (kinetic theory, molecular chaos hypothesis, maximum entropy principle, thermodynamic stability, H-theorem, and Lesche stability). The answers that the κ-statistical theory gives to the more-than-a-century-old open problems of relativistic physics, such as how thermodynamic quantities like temperature and entropy vary with the speed of the reference frame, have been emphasized.

Downscaling the ocean response to the Madden–Julian Oscillation in the Northwest Atlantic and adjacent shelf seas

AbstractSubseasonal-to-seasonal (S2S) prediction is a global effort to forecast the state of the atmosphere and ocean with lead times between two weeks and a season. This study explores the feasibility of S2S prediction of the ocean using a variety of tools including statistical analysis, a statistical-dynamical mixed layer model, and a regional, high-resolution ocean circulation model based on physical principles. Ocean predictability on S2S timescales is analyzed by compositing winter sea surface temperature (SST) anomalies in the North Atlantic with respect to the state of the Madden–Julian Oscillation (MJO). It is found that statistically significant, large-scale SST changes, particularly along the eastern seaboard of North America, can be related to the MJO. This signal is shown to be driven by anomalous air–sea heat fluxes caused by atmospheric perturbations in response to the MJO. The high-resolution model of the Gulf of Maine and Scotian Shelf is used to downscale the mean ocean response to the MJO. The model is able to capture the observed relationship between the MJO and SST in the northwest Atlantic. It is also shown that the anomalous atmospheric circulation in response to the MJO leads to anomalous upwelling on the Scotian Shelf. Overall, this study demonstrates that it is feasible, and of value, to use regional ocean models for S2S prediction.

Extracting macroscopic quantities in crowd behaviour with deep learning

Abnormal behaviours in crowded populations can pose significant threats to public safety, with the occurrence of such anomalies often corresponding to changes in macroscopic quantities of the complex system. Therefore, the automatic extraction and prediction of macroscopic quantities in pedestrian collective behaviour becomes significant. In this study, we generated pedestrian evacuation data through simulation, and calculated the average kinetic energy, entropy and order parameter of the system based on principles of statistical physics. These macroscopic quantities can characterize the changes in crowd behaviour patterns over time and can also assist in detecting abnormalities. Subsequently, we designed deep convolutional neural networks(CNNs) to estimate these macroscopic quantities directly from frame-by-frame image data. In the end, a convolutional auto-encoder(CAE) model is trained to learn the underlying physics unsupervisedly. Successful results indicate that deep learning methods can directly extract macroscopic information from crowd dynamics, aiding in analysing collective behaviour.

COMPARISON OF PLOT ANALYTICAL METHODS FOR DETERMINING EXCESSIVE WATER PRODUCTION MECHANISMS IN OIL WELLS

Watering of production wells is an important factor leading to a decrease in production profitability and an increase in costs. There are several main mechanisms of excess water inflow. These are a leak in the production casing; a violation of contact between the production casing and the rock; a formation of a bottom water cone; a raising the WOC surface to the perforation interval; a breakthrough through a layer with high permeability. Mechanisms can complement each other, forming a complex integrated system. To identify them, there are three groups of diagnostic methods — geophysical research, hydrodynamic well testing and plot analytical studies. Each of these fundamentally different groups can be supplemented by field research. Specifically in this work, we considered the latter. The analysis is carried out using the example of wells in one of the deposits of an oil field of the Arkhangelsk region. In which the causes of unwanted water production have already been studied by other methods and can be considered known. The studied reservoir is massive, with carbonate hydrophobic rocks and the reserves of the entire field are predominantly concentrated in it (97 %); the oil field (in terms of reserves) is small. There are no injection wells, operation is carried out in a natural elastic water drive mode. In the article, in total, three plot analytical methods are analyzed, which are among the main ones and have different application algorithms. Both the physical principles of their application and their effectiveness in relation to specific oil field are considered. These are the methods of K. Chan; L.I. Merkulova and A.A. Ginzburg; R. Novotny. The first studies the dependence of the water-oil ratio and its derivative on time (WOR and WOR’(t)), is the most widely known and has a large number of modifications (however, here they are not considered in detail). The second is based on the study of the so-called «water-cut characteristics» (representing the dependence of the accumulated oil production vs accumulated liquid production during the water period of operation). It has more restrictions, but is also more comprehensive. The third is based on a comparison of the permeability properties of reservoirs that change during operation.

MEMS Oscillators-Network-Based Ising Machine with Grouping Method.

Combinatorial optimization (CO) has a broad range of applications in various fields, including operations research, computer science, and artificial intelligence. However, many of these problems are classified as nondeterministic polynomial-time (NP)-complete or NP-hard problems, which are known for their computational complexity and cannot be solved in polynomial time on traditional digital computers. To address this challenge, continuous-time Ising machine solvers have been developed, utilizing different physical principles to map CO problems to ground state finding. However, most Ising machine prototypes operate at speeds comparable to digital hardware and rely on binarizing node states, resulting in increased system complexity and further limiting operating speed. To tackle these issues, a novel device-algorithm co-design method is proposed for fast sub-optimal solution finding with low hardware complexity. On the device side, a piezoelectric lithium niobate (LiNbO3) microelectromechanical system (MEMS) oscillator network-based Ising machine without second-harmonic injection locking (SHIL) is devised to solve Max-cut and graph coloring problems. The LiNbO3 oscillator operates at speeds greater than 9 GHz, making it one of the fastest oscillatory Ising machines. System-wise, an innovative grouping method is used that achieves a performance guarantee of 0.878 for Max-cut and 0.658 for graph coloring problems, which is comparable to Ising machines that utilize binarization.

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power

Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. Both models had good prediction accuracy despite using less input variables than traditional models and using more challenging prediction conditions by enforcing rapid speed changes during cycling. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

Fostering scientific methods in simulations through symbolic regressions

Two-dimensional computer and tablet PC physics simulations have proved to be effective in helping students understand the fundamental principles of physics and related natural processes. However, the current approach to using these simulations tends to follow a repetitive cognitive and procedural pathway, regardless of the specific physical concepts being explored or software environment being used. This approach involves manipulating the simulation interface and collecting data through interaction with controls, widgets, or other contextual elements. Students then attempt to determine how these experimental measurements align with established laws, interactions, or mechanisms, as the teacher might have previously explained. We believe that this approach, while appropriate for education, obscures scientific processes, mainly related to the hypothetico-deductive model. To address this issue, we have developed a simple and adaptable computer environment that makes use of genetic algorithms (GAs) and symbolic regression to derive many of the basic laws of nature from the data collected by students using the popular physics education technology (PhET) simulations environment. Our proposal enables learners to observe how the order and relationships of mathematical tokens are routinely refined as new data points are added to the simulation setting. This iterative distillation technique can also be augmented with the interplay of dimensional analysis. In contrast with other more sophisticated artificial intelligence patterns, GA fit into the realm of grey box machine learning models. These type of evolutionary algorithms achieve the sought results by evolving mathematical models on each stage in an understandable way, which mimics the way scientific breakthroughs are accomplished (over the course of generations of researchers and based of prior knowledge). By implementing this innovative approach, we can provide students with a more authentic empirical experience that fosters a deeper understanding of the principles of science and scientific discovery. Field tests with students supporting this claim have also been carried out.

Learning-based model predictive secondary frequency control of PV-ESS-EV microgrid

Microgrids comprising photovoltaic (PV), energy-storage systems and electric vehicles have received increasing attention. However, the increase in renewable energy sources, such as PVs, will introduce uncertainty factors to the frequency response, resulting in frequency fluctuations that can reduce power quality. Hence, we propose an outstanding learning-based control approach that combines a model of physical principles with a Gaussian Process (GP) learning approach. The learning model corrects the physical principles model and estimates its uncertainty. Simulations are performed by comparing the proposed controller with conventional model-predictive controllers. The results indicate that the learning-based controller proposed can adjust the frequency with less overshooting and a lower root mean square error compared to the conventional MPC control method.

Разработка и исследование подхода к обработке сигналов цифровых измерительных трансформаторов тока и напряжения

During the development of secondary systems of stations and substations, including relay protection, automation, control, electricity metering, etc., qualitative changes have recently occurred due to the introduction of digital technologies. The introduction of digital technologies is supported by innovative development programs such as “Digital Transformation 2030”. The introduction of digital technologies is associated with the use of digital data transmission channels according to uniform standards. Projects of “digital substations” are being actively implemented with the transmission of digital information at all levels, including the primary equipment, the transmission of information both from current and voltage measuring transformers, and from switching devices. Currently, digital instrument transformers are actively being developed and implemented, in which information about measured currents and voltages is presented in digital code. New approaches to measure primary currents and voltages, and transmit information pose new challenges, and open prospects for improving signal processing methods used for relay protection purposes. Thus, the relevant aim of the study is the development and research of approaches to digital processing of signals of digital instrument current and voltage transformers. To solve the problems posed within the framework of this study, analytical and numerical solution methods have been used. Numerical methods are applied using software products such as MathCAD and the Python programming language to generate signals and display them on graphs. To substantiate the reliability, verification of the results obtained by different methods is carried out. The authors have formulated an approach to digital processing of signals obtained using current sensors of a digital instrument transformer. The authors have studied the approach to determine synchronized current vector values by processing information received from primary current converters. The characteristics of the algorithm for obtaining a vector have been analyzed and the dependences of errors on the influence of distorting factors have been plotted. The proposed approach is a development of the existing approach to obtain a vector by getting information from current sensors operating on different physical principles. The results show the advantages of the proposed method. The proposed digital signal processing algorithm is more technically expensive since it contains at least two metrologically calibrated measuring channels instead of one. However, all other things being equal, it allows to increase the accuracy and speed of the digital processing algorithms taken as a prototype. The results obtained can be used to formulate algorithms for measuring relay protection devices.

A Comprehensive Review of Indentation of Gels and Soft Biological Materials

Abstract Indentation measurement has emerged as a crucial technique for elucidating the mechanical properties of soft hydrated materials. These materials, encompassing gels, cells, and biological tissues, possess pivotal mechanical characteristics crucial for a myriad of applications across engineering and biological realms. From engineering endeavors to biological processes linked to both normal physiological activity and pathological conditions, understanding the mechanical behavior of soft hydrated materials is paramount. The indentation method is particularly suitable for accessing the mechanical properties of these materials as it offers the ability to conduct assessments in liquid environment across diverse length and time scales with minimal sample preparation. Nonetheless, understanding the physical principles underpinning indentation testing and the corresponding contact mechanics theories, making judicious choices regarding indentation testing methods and associated experimental parameters, and accurately interpreting the experimental results are challenging tasks. In this review, we delve into the methodology and applications of indentation in assessing the mechanical properties of soft hydrated materials, spanning elastic, viscoelastic, poroelastic, coupled viscoporoelastic, adhesion properties, and fracture toughness. Each category is accomplished by the theoretical models elucidating underlying physics, followed by ensuring discussions on experimental setup requirements. Furthermore, we consolidate recent advancements in indentation measurements for soft hydrated materials highlighting its multifaceted applications. Looking forward we offer insights into the future trajectory of the indentation method on soft hydrated materials and the potential applications. This comprehensive review aims to furnish readers with a profound understanding of indentation techniques and a pragmatic roadmap of characterizing the mechanical properties of soft hydrated materials.

Physics-informed deep-learning applications to experimental fluid mechanics

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

Carbonic Anhydrases: Different Active Sites, Same Metal Selectivity Rules.

Carbonic anhydrases are mononuclear metalloenzymes catalyzing the reversible hydration of carbon dioxide in organisms belonging to all three domains of life. Although the mechanism of the catalytic reaction is similar, different families of carbonic anhydrases do not have a common ancestor nor do they exhibit significant resemblance in the amino acid sequence or the structure and composition of the metal-binding sites. Little is known about the physical principles determining the metal affinity and selectivity of the catalytic centers, and how well the native metal is protected from being dislodged by other metal species from the local environment. Here, we endeavor to shed light on these issues by studying (via a combination of density functional theory calculations and polarizable continuum model computations) the thermodynamic outcome of the competition between the native metal cation and its noncognate competitor in various metal-binding sites. Typical representatives of the competing cations from the cellular environments of the respective classes of carbonic anhydrases are considered. The calculations reveal how the Gibbs energy of the metal competition changes when varying the metal type, structure, composition, and solvent exposure of the active center. Physical principles governing metal competition in different carbonic anhydrase metal-binding sites are delineated.

Programming viscoelastic properties in a complexation gel composite by utilizing entropy-driven topologically frustrated dynamical state

Hydrogel composites in an aqueous media with viscoelastic properties and elastic modulus that can be precisely tailored are desirable to mimic many biological tissues ranging from mucus, vitreous humor, and nucleus pulposus as well as build up biosensors. Without altering the chemistry, tuning the physical interactions and structures to govern the viscoelastic properties of the hydrogels is indispensable for their applications but quite limited. Here we design a complexation gel composite and utilize the physical principle of topologically frustrated dynamical state to tune the correlated structures between the guest polycation chains and negatively charged host gels. We precisely quantify the mesh size of the host gel and guest chain size. By designing various topologically correlated structures, a viscoelastic moduli map can be built up, ranging from tough to ultrasoft, and from elastic-like with low damping properties to viscous-like with high damping properties. We also tune the swelling ratio by using entropy effect and discover an Entropy-driven Topologically Isovolumetric Point. Our findings provide essential physics to understand the relationship between entropy-driven correlated structures and their viscoelastic properties of the complexation hydrogel composites and will have diverse applications in tissue engineering and soft biomaterials.