Physical Laws Research Articles

End‐to‐end demonstration for CubeSatellite quantum key distribution

AbstractQuantum key distribution (QKD) provides a method of ensuring security using the laws of physics, avoiding the risks inherent in cryptosystems protected by computational complexity. Here, the authors investigate the feasibility of satellite‐based quantum key exchange using low‐cost compact nano‐satellites. The first prototype of system level quantum key distribution aimed at the Cube satellite scenario is demonstrated. It consists of a transmitter payload, a ground receiver and simulated free space channel to verify the timing and synchronisation (T&S) scheme designed for QKD and the required high loss tolerance of both QKD and T&S channels. The transmitter is designed to be deployed on various up‐coming nano‐satellite missions in the UK and internationally. The effects of channel loss, background noise, gate width and mean photon number on the secure key rate (SKR) and quantum bit error rate (QBER) are discussed. The authors also analyse the source of QBER and establish the relationship between effective signal noise ratio (ESNR) and noise level, signal strength, gating window and other parameters as a reference for SKR optimisation. The experiment shows that it can tolerate the 40 dB loss expected in space to ground QKD and with small adjustment decoy states can be achieved. The discussion offers valuable insight not only for the design and optimisation of miniature low‐cost satellite‐based QKD systems but also any other short or long range free space QKD on the ground or in the air.

Motility-induced coexistence of a hot liquid and a cold gas

If two phases exist at the same time, such as a gas and a liquid, they have the same temperature. This fundamental law of equilibrium physics is known to apply even to many non-equilibrium systems. However, recently, there has been much attention in the finding that inertial self-propelled particles like Janus colloids in a plasma or microflyers could self-organize into a hot gas-like phase that coexists with a colder liquid-like phase. Here, we show that a kinetic temperature difference across coexisting phases can occur even in equilibrium systems when adding generic (overdamped) self-propelled particles. In particular, we consider mixtures of overdamped active and inertial passive Brownian particles and show that when they phase separate into a dense and a dilute phase, both phases have different kinetic temperatures. Surprisingly, we find that the dense phase (liquid) cannot only be colder but also hotter than the dilute phase (gas). This effect hinges on correlated motions where active particles collectively push and heat up passive ones primarily within the dense phase. Our results answer the fundamental question if a non-equilibrium gas can be colder than a coexisting liquid and create a route to equip matter with self-organized domains of different kinetic temperatures.

Physics Based Animation Using Computer Graphics

Physics-based animation is a multidisciplinary area that uses ideas from physics, computer science, and mathematics to create realistic and dynamic movements in virtual settings. The basic ideas and methods of physics-based animation are introduced in this research paper, with an emphasis on how mathematical models and physical laws are used to produce realistic motion in computer-generated images. To construct realistic virtual environments, key disciplines covered include fluid simulation, fabric simulation, rigid body dynamics, and soft body dynamics. The report highlights recent developments in real-time physics simulations and addresses other topics such as processing economy, accuracy, and scalability. Applications for physics-based animation can be found in many different fields, such as virtual reality, simulation training, movies, and video games. The pursuit of more precise and effective physics-based animation is essential as technology develops to produce captivating and realistic virtual worlds. Keywords— Physics-based animation(PBA), virtual environment, Physics simulation, Dynamics, Animation software, Real-time physics, Collision detection, Fluid dynamics, Particle systems, Cloth simulation, Smooth particle hydrodynamics, finite element method, Navier-Stokes, Rigid body dynamics, Soft body dynamics, Deformation, Motion capture, Character animation, Kinematics, Rendering, Game development, Virtual reality, Augmented reality, Computer graphics, Simulation accuracy

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo.

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

Information in The Three Dimensions of Time

The relationship between physics and information processing is a topic of great significance in the realm of science and technology. The laws of physics, which govern the behavior of matter and energy, also have a profound influence on the way information is processed and utilized. While the mathematics governing information processing has not extensively utilized the laws of physics, it is evident that there is potential for these laws to play a more significant role, particularly in the context of quantum systems. based on the equivalence between information and energy, we can explore how other laws of physics can effectively contribute to more efficient and advanced information processing techniques. This research sheds light on the interconnectedness of physics and information processing, and its findings have the potential to significantly enhance our ability to process information, particularly in cutting-edge quantum systems. By recognizing and leveraging the influence of physics, we can pave the way for further advancements in information processing technologies.

GBM-ILM: Grey-Box Modeling Based on Incremental Learning and Mechanism for Unmanned Surface Vehicles

Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.

Physics-Informed Neural Networks with skip connections for modeling and control of gas-lifted oil wells

Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs). The recently introduced PINC framework extends PINNs to control applications, allowing for open-ended long-range prediction and control of dynamic systems. In this work, we enhance PINC for modeling highly nonlinear systems such as gas-lifted oil wells. By introducing skip connections in the PINC network and refining certain terms in the ODE, we achieve more accurate gradients during training, resulting in an effective modeling process for the oil well system. Our proposed improved PINC demonstrates superior performance, reducing the validation prediction error by an average of 67% in the oil well application and significantly enhancing gradient flow through the network layers, increasing its magnitude by four orders of magnitude compared to the original PINC. Furthermore, experiments showcase the efficacy of Model Predictive Control (MPC) in regulating the bottom-hole pressure of the oil well using the improved PINC model, even in the presence of noisy measurements.

A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3)

AbstractPurely data‐driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics‐Informed Neural Network based on fully physical models SAMI3 (PINN‐SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal‐spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN‐SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN‐SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).

An airflow velocity field reconstruction method with sparse or incomplete data using physics-informed neural network

Velocity field is critical for creating a healthy and comfortable indoor environment but it is difficult to be measured experimentally. Due to the limitations of measurement instruments and site conditions, the captured data are usually sparse, incomplete, and low-dimensional, which limits further research on indoor velocity field. This study proposes a practical reconstruction and super-resolution method based on physics-informed learning that seamlessly integrates imperfect measured data and physical laws. The ability to reconstruct the airflow velocity field with imperfect data as the training dataset was validated using a three-dimensional and three-component airflow field generated with an axial fan. This method successfully recovered the airflow field from sparse or insufficient data (even when the amount of data was only 5 % of the original vector fields) and incomplete data. This indicates that the proposed method can reconstruct and super-resolve the airflow velocity vector fields from sparse, incomplete, and low-dimensional data.

An entropy conserving/stable Discontinuous Galerkin solver in entropy variables based on the Direct Enforcement of Entropy Balance

Numerical schemes that guarantee the preservation of flow properties as prescribed by physical laws, ensure high-fidelity results and improved computational robustness. This work presents a highly accurate entropy conserving/stable solver for the solution of the compressible Euler equations. The method uses a modal Discontinuous Galerkin approximation and takes advantage of both entropy variables and the Direct Enforcement of Entropy Balance (DEEB), proposed by Abgrall [1] to fulfill the second law of thermodynamics at the discrete level. Thanks to the use of an orthonormal modal basis, the cost of adding the DEEB correction to the spatially discretized equations is negligible. Entropy variables are used directly, as unknowns, or to assemble the discrete operators while solving for the conservative variables. In the latter case, the conservative variables are “converted” into the entropy ones through an L2 “entropy projection”. If this strategy is coupled with DEEB, a very efficient alternative to the direct use of the entropy variables is obtained, with a dramatic improvement in robustness over the direct use of the conservative variables. For the time integration both an explicit Strong Stability Preserving Runge-Kutta scheme and the implicit Generalized Crank-Nicolson method are considered. The latter allows for the construction of a discrete entropy conservative/stable scheme both in space and time to be used as a reference. Convergence and conservation properties are demonstrated together with robustness and computational efficiency on a suite of two-dimensional test cases, showing that by coupling DEEB with entropy projection the solver robustness is improved while not affecting the stability limit.

An invariance constrained deep learning network for partial differential equation discovery

The discovery of partial differential equations (PDEs) from datasets has attracted increased attention. However, the discovery of governing equations from sparse data with high noise is still very challenging due to the difficulty of derivatives computation and the disturbance of noise. Moreover, the selection principles for the candidate library to meet physical laws need to be further studied. The invariance is one of the fundamental laws for governing equations. In this study, we propose an invariance constrained deep learning network (ICNet) for the discovery of PDEs. Considering that temporal and spatial translation invariance (Galilean invariance) is a fundamental property of physical laws, we filter the candidates that cannot meet the requirement of the Galilean transformations. Subsequently, we embedded the fixed and possible terms into the loss function of neural network, significantly countering the effect of sparse data with high noise. Then, by filtering out redundant terms without fixing learnable parameters during the training process, the governing equations discovered by the ICNet method can effectively approximate the real governing equations. We select the one-dimensional (1D) Kuramoto–Sivashinsky equation, the two-dimensional (2D) Burgers equation, the equation of two-dimensional channel flow over an obstacle, and the equation of three-dimensional (3D) intracranial aneurysm as examples to verify the superiority of the ICNet for fluid mechanics. Furthermore, we extend similar invariance methods to the discovery of wave equation (Lorentz Invariance) and verify it through single and coupled Klein–Gordon equation. The results show that the ICNet method with physical constraints exhibits excellent performance in governing equations discovery from sparse and noisy data.

Treat It Like A Circuit, Part I: Comparison of Concepts from Electronics to Flow in LC Systems

The analogy that electrons flowing in wires is like water flowing through a tube can be remarkably effective for teaching and learning about fluid flow in liquid chromatography (LC) systems. In this installment, we will go over the basics of this analogy, as well as explicitly discuss the phenomena observed in the two cases and the physical laws that we can use to rationalize and predict the behavior that we see. In the second installment in this series, we will then use this knowledge to demonstrate how these ideas can be used to troubleshoot problems when they arise in LC systems.

Synergic action of linear dispersion, second-order nonlinearity, and third-order nonlinearity in shaping the spectral profile of a femtosecond pulse transporting in a lithium niobate crystal

We present a detailed theoretical and numerical analysis on the temporal-spectral-spatial evolution of a high-peak-power femtosecond laser pulse in two sets of systems: a pure lithium niobate (LN) plate and a periodically poled lithium niobate (PPLN) plate. We develop a modified unidimensional pulse propagation model that considers all the prominent linear and nonlinear processes and carried out the simulation process based on an improved split-step Fourier transformation method. We theoretically analyze the synergic action of the linear dispersion effect, the second-order nonlinearity (2nd-NL) second-harmonic generation (SHG) effect, and the third-order nonlinearity (3rd-NL) self-phase modulation (SPM) effect, and clarify the physical mechanism underlying the peculiar and diverse spectral broadening patterns previously reported in LN and PPLN thin plate experiments. Such analysis and discussion provides a deeper insight into the synergetic contribution of these linear and nonlinear effects brought about by the interaction of a femtosecond laser pulse with the LN nonlinear crystal and helps to draw a picture to fully understand these fruitful optical physical processes, phenomena, and laws.

Psychical Inertia: Origins and Transformations

In this article I map a set of points that could begin to tell the story of inertia’s trajectory from physics and physiology to figurations of psychic life in Freudian psychoanalytic theory. Both preceding and contemporaneous with Freud’s use of the concept of inertia to describe an element of psychic life, work was taking place in other intellectual and geographical contexts to translate this law of physics and physiology to a property of mental life. As a particular illustration of this, I examine a lecture given by British scientist Michael Faraday in 1818, wherein he makes the leap from the inertia of matter to ‘mental inertia’ and is arguably the first recorded thinker to do so in the English language. I then explore the use of the phrase ‘psychic inertia’ in 1900 by Scottish physiologist D.F. Harris and argue that Harris’s work is not only emerging at the same time as Freud’s discoveries, but that he prefigures Freud’s ideas about the relationship between inertia and the death drive in interesting ways. The final part of the paper returns to sketch out Freud’s engagement with the concept in several of his papers.

Tidal forces in the Simpson–Visser black-bounce and wormhole spacetimes

The concept of regular black holes has gained attention in recent years, especially in the context of quantum gravity theories. In these theories, the existence of singularities is paradoxical as they represent a breakdown of the laws of physics. Motivated by the recent developments in this area, we study the tidal force effects in one such family of regular geometries described by the Simpson–Visser metric. We find the radial and angular force profiles for a radially in-falling particle in this spacetime and calculate the variation of the geodesic separation vector with the radial coordinate using two different initial conditions. These results are then compared with that of Schwarzschild black hole spacetime. We show that for a regular black hole, both radial and angular tidal forces show a peak outside the horizon and then fall to ultimately switch their behavior from stretching to compression and vice-versa. Also, they are finite at r=0 unlike the Schwarzschild spacetime. It is also seen that the angular deviation profile shows an oscillating behavior for a particular initial condition. Our analysis can be used to distinguish between regular black hole, one-way and two-way wormholes and a singular black hole spacetimes.

WR21 marine gas turbine thermodynamic simulator for ship propulsion studies

An original modular mathematical model, based on physical laws, for steady state and dynamics performance simulation of the WR21 gas turbine is presented in the paper. The model, developed in Matlab-Simulink language, is organized in modular form. Each gas turbine component (i.e. compressor, turbine, combustor, heat exchanger) is modelled in a specific simulator block, that simulates the performance of its pertinent component by means of steady state performance maps and/or correlations, time dependent momentum, energy and mass equations, nonlinear algebraic equations. The great application flexibility of the component simulator modules, allows to modelling any gas turbine layout (i.e. axial or radial flow compressor and turbine, rotating shafts number, heat exchangers). The WR21gas turbine, developed mainly for marine propulsion application, is a three shafts gas turbine with thermal regeneration. It is characterized by a high thermodynamic efficiency, which remains high up to 30% of the design power. The WR21 simulator results are validated by comparison with manufacturer or literature data. The WR21 gas turbine main performances are compared to those of the LM 2500 gas turbine, obtained by a previously author paper. The LM 2500 is the gas turbine currently most used in naval propulsion plants. In the article, a comparison between the steady state working data of the two gas turbines is reported in tabular and graphical form and commented.

Mathematical concepts as the basis for solving physical problems

Mathematical concepts play a fundamental role in physics. They are used to express physical laws and relationships, build models of physical systems, and solve physical problems. In the article, we noted that the application of mathematics in physics has a number of features that distinguish it from the application of mathematics in other fields of science. In particular, physicists use mathematics to express physical meaning rather than to study mathematical structures abstractly. This leads to a number of differences in the use of symbols and semantics by physicists and mathematicians. In physics, symbols are often used to represent physical quantities, not just abstract numbers. For example, the symbol E can be used to represent energy, M for mass, and c for the speed of light. This allows physicists to use mathematics to express physical laws and relationships in a natural and intuitive way. In addition, physicists often use units of measurement when interpreting mathematical expressions. For example, the equation E=mc^2 means that energy is equal to mass multiplied by the speed of light squared, provided that all values are expressed in the appropriate units of measurement. The application of mathematics in physics is a complex and multifaceted process. This requires not only mathematical skills, but also an understanding of the physical meaning of mathematical concepts.

Finite element (FE) analysis of thermal stress in production process of multi-layer lining ladle

This paper presented the development of the thermo-mechanical coupling model and simulation analysis of a composite refractory layer ladle. The thermomechanical modeling of the ladle was carried out using the Finite Element Method (FEM), taking into account the joints between the slag line bricks as well as the contact between the composite refractory layers. The temperature and thermal stress characteristics of the overall ladle structure under different transit conditions were investigated using the finite element analysis software ANSYS. We also performed a comparative analysis on the improvement effect of thermal stresses by expansion joints of different sizes. The simulation results were validated with previous studies. The results showed that the installation of expansion joints within a suitable range did not significantly change the thermal expansion characteristics of the refractory lining, but it also reduced the thermal stress level of the lining to a large extent. A larger temperature gradient produced higher thermal stresses, and in the direction of the ladle height, the more downward the position of its maximum compressive stress was greater. Under different working conditions, compared with the axial and hoop stress, the radial stress was small in magnitude. Axial stress exhibited a greater degree of sensitivity to pressure variations than hoop stress. The developed thermodynamic model considering the full contact between the bricks and the refractory layer gave us a more complete view of the physical field laws inside the ladle.

Tensor-based physics-encoded neural networks for modeling constitutive behavior of soil

Data-driven constitutive models are increasingly addressing non-elastic and three-dimensional scenarios. However, their robustness can be significantly impacted by the inadequate integration of physical information. Accordingly, this study introduces a tensor-based physics-encoded neural network to characterize the constitutive behavior of soil, exemplified by isotropic hypoplasticity with dependency on pressure and porosity. The framework ensures strict adherence to fundamental physical laws for small strain, rate-independent isotropic constitutive behavior. The network utilizes stress tensor invariants and soil state parameters (porosity) as inputs, and outputs crucial coefficients for the tensorial constitutive relations. The model has been calibrated using only symmetric triaxial test data (both drained and undrained). The effectiveness of the neural network-based constitutive model has been validated through simulations of drained and undrained triaxial tests under various initial conditions, as well as boundary value problems with complex loading. The results demonstrate that the proposed model offers the following distinguishing advantages: 1) Applicability to both two- and three-dimensional non-elastic cases, even when trained with two-dimensional data; 2) Strict adherence to fundamental physical laws, avoiding soft constraints; 3) An incremental, tensor-based architecture, suitable for integration in numerical software for boundary value problems.

Comparison of cooling grey solutions in the sense-city urban demonstrator for adapting cities to heat waves

SummaryFaced with the increase in urban heat in summer, cities must quickly adapt by offering cool islands. If nature-based solutions are efficient and popular, other so-called grey solutions exist and can also provide solutions. In this paper we compare standard grey solutions in Sense-city, an urban demonstrator equipped with sensor, located in the scientific campus of Cité Descartes at Marne la Vallée. Our objective is on the one hand to compare the effectiveness of standard solutions over time, but also to study the ability of sense-city and its canyon street to study this phenomenon despite its relatively small size. MethodsIn this work we follow an experimental approach using a wide coverage of calibrated sensors deployed in Sense-city. We have conducted two measurement campaigns in July 2021 and July–August 2022 where we study the effectiveness of standard solutions (shading, white floor surface, street watering) to reduce the air temperature in the canyon street of sense-city. As the days are different, we compare the temperature difference in the street canyon equipped with these cooling solutions with that on the surrounding area. This method allows us to overcome daily variations. To interpret the results, we analyse the shades and we follow the temperature over time and not only during the hottest hours. We also calculate the UTCI (Universal Thermal Climate Index) to compare the perceived temperature. FindingsThe results show that despite its small size, the canyon street of sense-city behaves like a canyon street and that the measured temperature differences due to heat exchanges comply with physical laws and that the number and spatial distribution of sensors is validated. The Sense-city demonstrator can therefore be used to carry out thermal efficiency experiments. In addition, the method we used to compare cooling solutions allows us to limit the impacts due to slight daily variations. Finally, while watering is effective in rapidly lowering the temperature in the street, shading remains an optimal solution for the perceived temperature, even if it would be ideal to close them at the end of the day to allow air circulation.