Physical Principles Research Articles

Dynamic Physics-Guided Deep Learning for Long-Term Production Forecasting in Unconventional Reservoirs

Summary Neural network predictive models are popular for production forecasting in unconventional reservoirs due to their ability to learn complex relationships between well properties and production responses from extensive field data. The intricate flow behavior in hydraulically fractured unconventional reservoirs, which remains poorly understood, makes these statistical models particularly useful. Various neural network variants have been developed for production prediction in these reservoirs, each offering predictive capability of varying levels of granularity, accuracy, and robustness against noisy and incomplete data. Neural network predictive models that integrate physical principles are especially useful for subsurface systems, as they provide predictions that adhere to physical laws. This work introduces a new dynamic physics-guided deep learning (DPGDL) model that incorporates physical functions into neural networks and employs residual learning to compensate for the imperfect description of the physics, under variable data support. The new formulation allows for dynamic residual correction, avoids unintended bias due to less-than-ideal input data, and provides robust long-term predictions. The DPGDL model improves upon a static formulation by utilizing a masked loss function to enable learning from wells with varying production lengths and by improving the results when partially-observed timesteps are present. In addition, a sequence-to-sequence residual model has been developed to correct additional biases in the long-term predictions from the physics-constrained neural networks. Several synthetic data sets with increasing complexity as well as a field data set from the Bakken are used to demonstrate the performance of the new DPGDL model.

A preliminary study of polymer optical fiber’s knittability for smart wear applications

PurposeThe purpose of this research is to ascertain the feasibility of fabricating polymer optical fibers (POFs) based textile structures by knitting with Polymethylmethacrylate (PMMA) based optical fibers for textile sensor application. It has long been established that by using the principles of physics, POFs have the capability to function as sensors, detecting strain, temperature and other variables. However, POF applications such as strain and pressure sensing using knitting techniques has since not been very successful due to a number of reasons. Commercially available PMMA-based optical fibers tend to be fragile and susceptible to breakages when subjected to stress during the knitting processes. Also light transmitted within these fibers is prone to leakage due to the curvature that results when optical fibers are interlaced or interlooped within fabric structures.Design/methodology/approachUsing Stoll’s multi-gauge CMS 350 HP knitting machine, five fabric structures namely, 1 × 4 float knit structure, tunnel inlay knit structure, 3:1 fleece fabric and 2:1 fleece fabric structure respectively were used to knit sensor samples. The samples were subsequently tested for length of illumination and sensitivity relative to applied pressure.FindingsThe results of this preliminary study establish that embedding plastic optical fibers into a knitted structure during the fabric formation process for soft strain sensor application possible. The best illumination performance was recorded for tunnel inlay structure which had an average of 94 cm course length of POF being illuminated. Sensor sensitivity experiments also establish that the relative spectral intensity of the fiber is sensitive to both light and pressure. Problems encountered and recommendations for further research have also been discussed and proffered.Research limitations/implicationsDue to resource limitations, an innovative technique (use of precision weight set) was used to apply pressure to the sensors. Consequently, information regarding the extent of corresponding sensor deformation has not been used in this initial analysis.Practical implicationsBecause the fundamental step toward finding a solution to any engineering problem is the acquisition of reliable data, and considering the fact that most of the popular technologies used for soft textile sensors are still bedeviled with the problem of signal instability and noise, the success of this application thus has the tendency to promote the wide spread adoption of POF sensors for smart apparel applications.Originality/valueAs far as research on soft strain sensors is concerned, to the best of the authors’ knowledge, this is the first study to have attempted to knit deformable sensors using commercially available POFs.

Recent Progress in the Physical Principles of Dynamic Ground Self-Righting.

Animals and robots must self-right on the ground after overturning. Biology research has described various strategies and motor patterns in many species. Robotics research has devised many strategies. However, we do not well understand the physical principles of how the need to generate mechanical energy to overcome the potential energy barrier governs behavioral strategies and 3D body rotations given the morphology. Here, I review progress on this which I led studying cockroaches self-righting on level, flat, solid, low-friction ground, by integrating biology experiments, robotic modeling, and physics modeling. Animal experiments using three species (Madagascar hissing, American, and discoid cockroaches) found that ground self-righting is strenuous and often requires multiple attempts to succeed. Two species (American and discoid cockroaches) often self-right dynamically, using kinetic energy to overcome the barrier. All three species use and often stochastically transition across diverse strategies. In these strategies, propelling motions are often accompanied by perturbing motions. All three species often display complex yet stereotyped body rotation. They all roll more in successful attempts than in failed ones, which lowers the barrier, as revealed by a simplistic 3D potential energy landscape of a rigid body self-righting. Experiments of an initial robot self-righting via rotation about a fixed axis revealed that the longer and faster appendages push, the more mechanical energy can be gained to overcome the barrier. However, the cockroaches rarely achieve this. To further understand the physical principles of strenuous ground self-righting, we focused on the discoid cockroach's leg-assisted winged self-righting. In this strategy, wings propel against the ground to pitch the body up but are unable to overcome the highest pitch barrier. Meanwhile, legs flail in the air to perturb the body sideways to self-right via rolling. Experiments using a refined robot and an evolving 3D potential energy landscape revealed that, although wing propelling cannot generate sufficient kinetic energy to overcome the highest pitch barrier, it reduces the barrier to allow small kinetic energy from the perturbing legs to probabilistically overcome the barrier to self-right via rolling. Thus, only by combining propelling and perturbing can self-righting be achieved when it is so strenuous; this physical constraint leads to the stereotyped body rotation. Finally, multi-body dynamics simulation and template modeling revealed that the animal's substantial randomness in wing and leg motions helps it, by chance, to find good coordination, which accumulates more mechanical energy to overcome the barrier, thus increasing the likelihood of self-righting.

Imaging the Lung in ARDS: A Primer.

Despite periodic changes in the clinical definition of ARDS, imaging of the lung remains a central component of its diagnostic identification. Several imaging modalities are available to the clinician to establish a diagnosis of the syndrome, monitor its clinical course, or assess the impact of treatment and management strategies. Each imaging modality provides unique insight into ARDS from structural and/or functional perspectives. This review will highlight several methods for lung imaging in ARDS, emphasizing basic operational and physical principles for the respiratory therapist. Advantages and disadvantages of each modality will be discussed in the context of their utility for clinical management and decision-making.

Modeling and Comparison Study of Industrial AC-Arcs

Electric arcs are a necessary heat source in many industrial processes that take place in Submerged Arc Furnaces (SAFs). Arcs exhibit non-linear electrical characteristics and behave in a complex manner. Therefore, an improved understanding of their behavior enables better control of furnace operation. Modeling of industrial arcs is a multiphysics process that involves simultaneously solving several coupled physical phenomena, such as electromagnetics, fluid dynamics, and heat transfer, including a radiative heat transfer from the plasma arc. Coupling fluid dynamics and electromagnetics is known as Magnetohydrodynamics (MHD). For practical applications, however, there are also simpler approaches to arc modeling, either based on simplified physical principles or empirical behavior. In this paper, a combined Cassie–Mayr model (CMM) and a channel arc model (CAM) are implemented and coupled with a submerged arc furnace electrical circuit model. The complete circuit model parameters such as resistances and inductances are estimated using modeling of a full size furnace, and then, actual measurements from a SAF are used to validate the models by comparing current and voltage waveform. Both models are then used to estimate harmonic distortion in a SAF for different arc current ratios, which should help operators to estimate the arc current in real time thus be able to lower and raise the electrode to keep operating conditions constant.

Fundamental causal bounds of quantum random access memories

Our study evaluates the limitations and potentials of Quantum Random Access Memory (QRAM) within the principles of quantum physics and relativity. QRAM is crucial for advancing quantum algorithms in fields like linear algebra and machine learning, purported to efficiently manage large data sets with O(logN)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\\mathcal{O}}}}(\\log N)$$\\end{document} circuit depth. However, its scalability is questioned when considering the relativistic constraints on qubits interacting locally. Utilizing relativistic quantum field theory and Lieb–Robinson bounds, we delve into the causality-based limits of QRAM. Our investigation introduces a feasible QRAM model in hybrid quantum acoustic systems, capable of supporting a significant number of logical qubits across different dimensions-up to ~107 in 1D, ~1015 to ~1020 in 2D, and ~1024 in 3D, within practical operation parameters. This analysis suggests that relativistic causality principles could universally influence quantum computing hardware, underscoring the need for innovative quantum memory solutions to navigate these foundational barriers, thereby enhancing future quantum computing endeavors in data science.

The myth of high-resolution liquid phase biological electron microscopy.

Cryo-electron microscopy (cryo-EM) has transformed structural biology over the past 12 years, with it now being routine rather than exceptional to reach a near-atomic level of resolution for proteins and macromolecular complexes. Samples are immobilized by vitrification and this sample can be maintained at liquid nitrogen temperatures in the vacuum of the electron microscope with negligible sublimation. Due to the low electron doses needed to avoid radiation damage, averaging over tens of thousands to hundreds of thousands of particle images is used to achieve a high signal-to-noise ratio. An alternative approach has been proposed where samples are at room temperature in the liquid state, maintained in the vacuum of the electron microscope by thin film enclosures that are relatively transparent to electrons while preventing evaporation of the liquid. A paper has argued that using this liquid-phase approach, higher resolution (3.2 Å) can be achieved than using cryo-EM (3.4 Å) when imaging and reconstructing adeno-associated virus particles. I show here that these assertions are untrue, and that basic principles in mathematics and physics would need to be violated to achieve the stated resolution in the liquid state. Thus, high resolution liquid phase EM of macromolecules remains science fiction.

Tidal heating as a discriminator for horizons in equatorial eccentric extreme mass ratio inspirals

Tidal heating in a binary black hole system is driven by the absorption of energy and angular momentum by the black hole’s horizon. Previous works have shown that this phenomenon becomes particularly significant during the late stages of an extreme mass ratio inspiral (EMRI) into a rapidly spinning massive black hole, a key focus for future low-frequency gravitational-wave observations by (for instance) the Laser Interferometer Space Antenna mission. Past analyses have largely focused on quasicircular inspiral geometry, with some of the most detailed studies looking at equatorial cases. Though useful for illustrating the physical principles, this limit is not very realistic astrophysically, since the population of EMRI events is expected to arise from compact objects scattered onto relativistic orbits in galactic centers through many-body events. In this work, we extend those results by studying the importance of tidal heating in equatorial EMRIs with generic eccentricities. Our results suggest that accurate modeling of tidal heating is crucial to prevent significant dephasing and systematic errors in EMRI parameter estimation. We examine a phenomenological model for EMRIs around exotic compact objects by parametrizing deviations from the black hole (BH) picture in terms of the fraction of radiation absorbed compared to the BH case. Based on a mismatch calculation, we find that reflectivities as small as |R|2∼O(10−5) are distinguishable from the BH case, irrespective of the value of the eccentricity. We stress, however, that this finding should be corroborated by future parameter estimation studies. Published by the American Physical Society 2024

Non-contrast MR angiography: physical principles and clinical applications in chest, abdomen and pelvis imaging.

This review article focuses on the advancements in non-contrast magnetic resonance angiography (NC-MRA) and its increasing importance in body imaging, especially for patients with renal complications, pregnant women, and children. It highlights the relevance of NC-MRA in chest, abdominal, and pelvis imaging and details various bright-blood NC-MRA techniques like cardiac-gated 3D Fast Spin Echo (FSE), balanced Steady-State Free Precession (bSSFP), Arterial Spin Labeling (ASL), and 4D flow methods. The article explains the operational principles of these techniques, their clinical applications, and their advantages over traditional contrast-enhanced methods. Special attention is given to the utility of these techniques in diverse imaging scenarios, including liver, renal, and pelvic imaging. The article underscores the growing importance of NC-MRA in medical diagnostics, offering insights into current practices and potential future developments. This comprehensive review is a valuable resource for radiologists and clinicians, emphasizing NC-MRA's role in enhancing patient care and diagnostic accuracy across various medical conditions.

The balance issue of the proportion between new energy and traditional thermal power: An important issue under today's low-carbon goal in developing countries

The stability of the new power system depends on the balance between controllable flexible resources and uncontrollable uncertain resources. For many developing countries and regions lacking advanced flexible resources, thermal power is the main force in solving the problem of new energy consumption. Finding the balance between new energy and traditional thermal power is the key for today's low-carbon goal. To solve this problem, this paper calculates the power range and climbing rate range of thermal power based on physical principles and actual data, and characterizes the characteristics of wind power, photovoltaic power, load and net load based on the five proposed volatility indicators. Then, a logically clear method for determining the required number of thermal power units based on the volatility indicators of new energy from the mechanism and principle perspective is proposed. Finally, under the premise of meeting the power balance and flexibility balance of the power system, the reasonable ratios between the installed capacity of new energy and the number of thermal power units are calculated in four seasons. The results indicate that the judgment system can provide guidance for the energy structure of developing regions.

Inverse mapping of quantum properties to structures for chemical space of small organic molecules

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the inverse mapping in chemical space remain elusive. We address this challenge by demonstrating the possibility of parametrizing a chemical space with a finite set of QM properties. Our proof-of-concept implementation achieves an approximate property-to-structure mapping, the QIM model (which stands for “Quantum Inverse Mapping”), by forcing a variational auto-encoder with a property encoder to obtain a common internal representation for both structures and properties. After validating this mapping for small drug-like molecules, we illustrate its capabilities with an explainability study as well as by the generation of de novo molecular structures with targeted properties and transition pathways between conformational isomers. Our findings thus provide a proof-of-principle demonstration aiming to enable the inverse property-to-structure design in diverse chemical spaces.

The Physics of Preference: Unravelling Imprecision of Human Preferences through Magnetisation Dynamics

Paradoxical decision-making behaviours such as preference reversal often arise from imprecise or noisy human preferences. Harnessing the physical principle of magnetisation reversal in ferromagnetic nanostructures, we developed a model that closely reflects human decision-making dynamics. Tested against a spectrum of psychological data, our model adeptly captures the complexities inherent in individual choices. This blend of physics and psychology paves the way for fresh perspectives on understanding the imprecision of human decision-making processes, extending the reach of the current classical and quantum physical models of human behaviour and decision making.

Physics-Guided Inverse Regression for Crop Quality Assessment

AbstractWe present an innovative approach leveraging Physics-Guided Neural Networks (PGNNs) for enhancing agricultural quality assessments. Central to our methodology is the application of physics-guided inverse regression, a technique that significantly improves the model’s ability to precisely predict quality metrics of crops. This approach directly addresses the challenges of scalability, speed, and practicality that traditional assessment methods face. By integrating physical principles, notably Fick’s second law of diffusion, into neural network architectures, our developed PGNN model achieves a notable advancement in enhancing both the interpretability and accuracy of assessments. Empirical validation conducted on cucumbers and mushrooms demonstrates the superior capability of our model in outperforming conventional computer vision techniques in postharvest quality evaluation. This underscores our contribution as a scalable and efficient solution to the pressing demands of global food supply challenges.

Tyndall, Rayleigh, Mei, and Raman scattering: Understanding their role in aesthetics.

The role of various light scattering phenomena in aesthetics and clinical practice is explored in this review. Four main types of light scattering, Tyndall, Rayleigh, Mie, and Raman, are discussed. Each type is explained in terms of its physical principles and its applications in aesthetic medicine. Tyndall scattering is relevant in understanding the blue appearance of certain dermal fillers. Rayleigh scattering contributes to skin tone perception and plays a role in certain laser treatments. Mie scattering is important in laser hair removal and the appearance of skin conditions like melasma. Raman scattering, while primarily used in research, shows promise for non-invasive skin analysis, personalized skincare, treatment monitoring, and early skin cancer detection. It is important to understand these scattering phenomena for optimizing light-based aesthetic procedures and developing effective treatments. Properly applying the appropriate scattering theory based on relative particle size is crucial in clinical aesthetic practice.

Tree internal defects detection method based on ResNet improved subspace optimization algorithm

The erosion behavior of trunk borers leads to the destruction of trunk structure and the formation of internal defects, which significantly impacts the ecological and economic value of trees. Traditional non-destructive testing (NDT) methods are costly and have low resolution, whereas electromagnetic NDT methods are more suitable for high-resolution detection and imaging. However, solving the highly nonlinear electromagnetic inverse scattering problems (ISPs) for small-sized defects with high contrast is challenging. Therefore, this paper proposes an improved subspace optimization algorithm based on a ResNet network called SOM-ResNet. SOM-ResNet incorporates physical principles into deep learning networks by simulating the iterative process of induced current and contrast, thereby enhancing its ability to accurately detect small objects with high contrast. Experimental results demonstrate that SOM-ResNet outperforms single inversion algorithms in detecting complex scatterers with small to medium-sized targets, validating its excellent performance.

Building a Model of the Physical Principle of Operation of an Autonomous Locomotive with a Steam Power Unit

Building a Model of the Physical Principle of Operation of an Autonomous Locomotive with a Steam Power Unit

A Co-Doping Materials Design Strategy for Selective Ozone Electrocatalysts.

Catalysts for electrochemical ozone production (EOP) face inherent selectivity challenges stemming from thermodynamic constraints. This work establishes a design strategy for minimizing these limitations and inducing EOP activity in tin oxide, which is an intrinsically EOP-inactive material. We propose that selective ozone production using tin oxide catalysts can be broadly achieved by co-doping with two elements: first, n-type dopants to enhance electrical conductivity, and second, transition metal dopants that leach and homogeneously generate essential hydroperoxyl radical intermediates. Synthesizing tantalum, antimony, and tungsten n-type dopants with nickel, cobalt, and iron as transition metal dopants confirms that properly co-doping tin oxide yields EOP-active catalysts. This study offers a robust framework for advancing EOP catalyst design and serves as a case study for the application of fundamental co-catalysis and solid-state physics principles to induce catalytic activity in inert materials.

Cutting–Edge Physics Driven Advancements in Medical Industry

Science contributed to technology by serving as a direct source and creation of ideas for new knowledge. Technology emerged from the application of this knowledge in terms of products, processes and services. Nowadays, due to the multidisciplinary application of physics, medical technology and radiation treatment has begun and widely accompanied within a technology based medical diagnosis and treatment due to the development of physics. In this paper a detailed description and analysis of scientific achievement and technological advancements of physics in medical industry has been clearly reviewed. The application of physics principles to medical sector has made an outstanding contribution in the diagnosis and treatment of diseases. Many extraordinary tools, those could have played a great role in diagnostic and treatment methods, have appeared as a result of physics discoveries in the last century. This description of physics applications in medical technology and principles has mainly based on comprehensive review of the literature, contributes to suggest the novel ideas used for the innovation of modern medical technologies. This journal paper anecdote the fundamental physical principles used for the innovation of these technological advances, emphasizing their applications to the practice of modern medical industry based on comprehensive review of the literature.

Physicochemical model of uranium hexafluoride (UF6) radiolysis under action of alpha particles

Abstract A multiscale model describing the kinetics of the processes taking place during the radiolysis of uranium hexafluoride (UF6) has been developed using fundamental physical and chemical principles. The model incorporates both the radiolysis and thermal terms. The processes of the radiolysis products release from the gas phase to the solid and vice versa are taken into account. The predictions of the model are verified against the available experimental data.

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With the ever-increasing prices of and demand for traditional fuels and the decreasing availability thereof, renewable energy sources, such as wind energy, are gaining enormous popularity. First of all, this branch of "green" energy is environmentally friendly. A significant increase in the use of wind power plants (WPPs) is observed all over the world. Modern WPPs are of two types: vertical- and horizontal-axis ones. Vertical-axis WPPs, in contrast to horizontal-axis ones, have a number of specific design advantages, such as, for example, insensitivity to the wind direction, which significantly simplify their design and increase their reliability. The operation of vertical-axis WPPs involves the need to stabilize their operating regimes, the main objective of which is to stabilize electricity production in conditions of a variable wind speed using appropriate stabilization systems (SSs). In SS development, use is made of various control algorithms, which make a basis for harnessing physical principles of SS construction. Recently, SSs based on blade swept area variation have become widespread. Such systems, unlike systems based on, for example, generator load variation, actually use the adaptation of WPPs to a variable wind speed, and they dispense with the need for mechanical dissipation of excess energy by resistance forces and, to some extent, with the need to transfer it to the support. The last point significantly reduces the load on the rotor-to-generator transmission systems and alleviates the requirements for anchor systems in the case of WPPs installed on floating platforms. In terms of design, the stabilization of vertical-axis WPPs by swept area variation can be performed in three ways: by varying the blade length, varying the length of the traverses whereby the blades are attached to the rotor shaft, and by simultaneously varying the length of the blades and the traverses, i.e., by varying WPP rotor configuration. The elaboration of approaches to the development of algorithms for the stabilization of vertical-axis WPPs controlled by rotor configuration variation is an important and promising task. The goal of this paper is to develop efficient algorithms for stabilizing the variable-configuration WPP rotor speed providing the stability and operability of the channels of blade and traverse length variation in their simultaneous operation. The problem is solved using methods of the classical theory of automatic control and mathematical simulation. The novelty lies in extending the concept of control by swept area variation to Darrieus vertical-axis WPPs, synthesizing efficient algorithms for stabilizing the rotor speed of Darrieus vertical-axis WPPs controlled by rotor configuration variation, and determining conditions for their stability and operability. The algorithms and approach developed may be used in substantiating design solutions for Darrieus vertical-axis WPPs.