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Weather map as an example of a physical system

A physical system is a collection of physical objects under study.[1] The collection differs from a set: all the objects must coexist and have some physical relationship.[2] In other words, it is a portion of the physical universe chosen for analysis. Everything outside the system is known as the environment, which is ignored except for its effects on the system.

The split between system and environment is the analyst's choice, generally made to simplify the analysis. For example, the water in a lake, the water in half of a lake, or an individual molecule of water in the lake can each be considered a physical system. An isolated system is one that has negligible interaction with its environment. Often a system in this sense is chosen to correspond to the more usual meaning of system, such as a particular machine.

In the study of quantum coherence, the "system" may refer to the microscopic properties of an object (e.g. the mean of a pendulum bob), while the relevant "environment" may be the internal degrees of freedom, described classically by the pendulum's thermal vibrations. Because no quantum system is completely isolated from its surroundings,[3] it is important to develop a theoretical framework for treating these interactions in order to obtain an accurate understanding of quantum systems.

In control theory, a physical system being controlled (a "controlled system") is called a "plant".

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Physical system

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A physical system is a portion of the physical universe selected for study, comprising a collection of interacting material entities such as particles, fields, or objects, distinct from its surroundings known as the environment.[1] This demarcation allows physicists to apply fundamental laws—like conservation principles and equations of motion—to predict and explain the system's behavior, often simplifying complex interactions by idealizing boundaries or neglecting minor influences.[1] Physical systems are classified based on their interactions with the environment, particularly regarding exchanges of matter and energy. An open system can transfer both matter and energy across its boundary, as seen in processes like convection in fluids or chemical reactions in living organisms.[2] A closed system exchanges energy (such as heat or work) but not matter, exemplified by a sealed piston-cylinder containing gas undergoing compression.[2] An isolated system exchanges neither, representing an idealization where total energy and matter remain constant, like the entire universe in cosmological models.[2] These categories facilitate analysis in thermodynamics, mechanics, and other branches of physics, enabling the application of laws such as the first law of thermodynamics, which states that the change in internal energy equals heat added minus work done by the system. The state of a physical system is fully specified when all its relevant physical properties—such as position, velocity, temperature, pressure, or quantum wave function—have definite values, allowing deterministic predictions under classical or quantum frameworks.[1] Properties evolve over time through internal dynamics or external influences, governed by equations like Newton's laws for classical systems or the Schrödinger equation for quantum ones.[3] In practice, real-world systems are modeled approximately, accounting for uncertainties via statistical mechanics for large ensembles or perturbation theory for small deviations.[1] This framework underpins diverse applications, from engineering devices to understanding natural phenomena like planetary motion or subatomic interactions.

Definition and Fundamentals

Core Definition

A physical system is any portion of the physical universe selected for analysis, consisting of matter, energy, or both, with specified boundaries that separate it from its surroundings or environment.[4] This demarcation allows physicists to focus on interactions within the defined region while treating external influences as inputs or outputs across the boundary. The concept encompasses diverse scales, from subatomic particles to astronomical structures, enabling the application of physical laws to predict behavior.[5] While the general concept of a physical system traces back to classical mechanics in the 17th century with Isaac Newton, the notion of a thermodynamic system developed in the 19th century within thermodynamics, notably through the work of Rudolf Clausius, who in 1850 established the foundational principles of modern thermodynamics by unifying heat and work under energy conservation.[6] Clausius's work, building on earlier ideas from Sadi Carnot and Émile Clapeyron, introduced the notion of a thermodynamic system as a bounded entity undergoing processes like heat transfer and work, with his 1854 analysis of the Carnot cycle providing a key mathematical framework.[6] This development evolved from Newtonian mechanics, where Isaac Newton's 17th-century laws described the motion of point masses and forces, laying the groundwork for analyzing isolated mechanical systems before extending to energetic and thermal interactions in the 19th century.[7] In physics, defining a physical system serves to simplify the complexity of the universe by isolating variables and interactions, facilitating predictions and deeper understanding of natural phenomena through controlled analysis. For instance, a gas confined in a piston-cylinder represents a simple physical system where pressure, volume, and temperature can be studied under thermodynamic laws, contrasting with the entire Earth's atmosphere as a highly complex system involving myriad coupled processes like convection and radiation.[6]

System Boundaries and Components

In physics, the boundaries of a physical system are defined as real or imaginary surfaces that separate the system from its surroundings, delineating the portion of the universe under study. These boundaries can be fixed or movable and are often conceptualized as infinitely thin interfaces across which properties such as temperature, pressure, or density may change abruptly. The permeability of these boundaries with respect to matter and energy depends on the nature of exchanges allowed, enabling the isolation of specific interactions for analysis.[8] The internal components of a physical system consist of matter in forms such as particles, molecules, or continuous fields (e.g., electromagnetic fields), along with various energy manifestations including kinetic energy of motion, potential energy due to position or configuration, and internal thermal energy. These components interact through fundamental forces, such as gravitational, electromagnetic, or nuclear forces, mediated by fields that govern the system's dynamics. For instance, in a mechanical system, components might include masses connected by springs, where interactions arise from elastic forces.[9][10] Criteria for selecting system boundaries and components are guided by the goal of making the analysis mathematically tractable, often prioritizing simplicity and relevance to the physical phenomena of interest. Boundaries are chosen at convenient locations to enclose relevant interactions while excluding extraneous influences, such as drawing them around a single object or a group of interacting elements to apply specific frameworks. In conservative systems, where forces derive from a potential, boundaries are selected to encompass all such interactions, facilitating the use of Lagrangian mechanics, which reformulates dynamics in terms of generalized coordinates and minimizes computational complexity for multi-body problems.[11][12] Defining boundaries can present challenges, particularly when they are arbitrary or ill-defined, leading to necessary approximations in modeling. In fluid dynamics, for example, interfaces between fluids or between a fluid and a solid may be fuzzy due to mixing or thin transition layers, requiring techniques like boundary layer approximations to simplify the governing equations while capturing essential flow behaviors near surfaces. Such approximations introduce errors but enable solvable models for complex, real-world scenarios where exact boundaries are impractical to specify.[13][14]

Classification of Physical Systems

By Interaction with Surroundings

Physical systems are classified based on their interactions with the surrounding environment, particularly regarding the exchange of matter and energy. This categorization influences how physical laws, such as those in thermodynamics, apply to the system. The three primary types are isolated, closed, and open systems.[15] An isolated system exchanges neither matter nor energy with its surroundings, making it an idealization rarely achieved in practice. The entire universe is often considered an example of an isolated system, as there is no known external environment with which it can interact.[16] In such systems, all processes occur internally without external influence.[17] A closed system permits the exchange of energy, such as heat or work, but not matter with its surroundings. A sealed thermos flask serves as an approximate example, where heat can slowly transfer across the boundary while the contents remain contained.[15] This type of system maintains fixed composition but can undergo changes in internal energy due to external energy flows.[17] An open system allows both matter and energy to exchange freely with the surroundings. A boiling pot of water with the lid off exemplifies this, as steam (matter) escapes while heat enters from the stove.[15] Open systems are common in natural and engineering contexts, where continuous inputs and outputs drive dynamic behavior.[17] These classifications have significant implications for the applicability of thermodynamic laws. The first law of thermodynamics, which states that energy is conserved and can neither be created nor destroyed, holds universally for all system types, as it reflects the invariance of total energy in any process.[17] In contrast, the second law, concerning entropy, asserts that the entropy of an isolated system never decreases and typically increases over time for irreversible processes, providing a directionality to spontaneous changes within the system.[18] For closed and open systems, entropy changes must account for external exchanges, often requiring consideration of the combined system and surroundings to apply the second law fully.[17]

By Scale and Complexity

Physical systems are classified by spatial scale, which determines the dominant physical principles governing their behavior. At the microscopic scale, systems involve atomic or subatomic particles, such as electrons orbiting atomic nuclei, where quantum mechanics provides the fundamental description due to wave-particle duality and probabilistic outcomes.[19][20] These systems exhibit phenomena like superposition and tunneling, which are negligible at larger scales, and their dynamics are captured by the Schrödinger equation rather than classical trajectories.[20] The mesoscopic scale occupies an intermediate regime, typically at the nanoscale (1–100 nm), where systems like quantum dots or nanowires display behaviors that bridge quantum and classical regimes. In these nanoscale devices, quantum effects such as interference and coherence coexist with classical dissipation, enabling applications in quantum computing and sensors.[21] This scale is characterized by finite-size effects and thermal fluctuations that blur strict quantum-classical boundaries, often modeled using mesoscopic transport theories.[22] At the macroscopic scale, systems encompass everyday objects and larger structures, such as planetary orbits or fluid flows, where classical mechanics suffices due to the averaging out of quantum fluctuations over vast numbers of particles. For instance, the motion of planets around the Sun follows Newtonian gravity, treating bodies as point masses without quantum corrections.[23] These systems are analyzed using continuum approximations, insensitive to atomic details, as statistical mechanics links microscopic interactions to bulk properties like pressure and temperature.[24] Beyond scale, physical systems are categorized by internal complexity, reflecting the number and interaction strength of components. Simple systems feature few degrees of freedom and predictable dynamics, exemplified by a single pendulum, whose motion is governed by linear or weakly nonlinear equations yielding periodic oscillations.[25] In contrast, complex systems involve numerous interacting elements, leading to emergent behaviors like sensitivity to initial conditions, as seen in weather patterns modeled by chaos theory.[26] These systems, such as atmospheric circulation, exhibit deterministic yet unpredictable evolution due to nonlinearities, where small perturbations amplify into large divergences, a hallmark of chaotic dynamics.[27]

Key Properties

Conservation Laws

Conservation laws represent foundational principles in physics, asserting that specific quantities—such as energy, momentum, and mass—remain unchanged within physical systems under defined conditions, reflecting underlying symmetries in nature. These laws enable the prediction and analysis of system evolution without tracking every interaction, applying most rigorously to isolated systems that exchange neither matter nor energy with their surroundings. Derived from empirical observations and theoretical frameworks, they form the bedrock for understanding diverse phenomena from mechanics to thermodynamics.[28] The conservation of energy, encapsulated in the first law of thermodynamics, posits that the total energy of a system is invariant; it can neither be created nor destroyed, only converted between forms. Mathematically, for a thermodynamic system, this is expressed as the change in internal energy ΔU\Delta U equaling the heat QQ added to the system minus the work WW done by the system:
ΔU=Q−W \Delta U = Q - W

where UU denotes internal energy. This principle was independently formulated by Hermann von Helmholtz in 1847 and Rudolf Clausius in 1850, building on earlier work by James Prescott Joule demonstrating the mechanical equivalent of heat. It governs processes in closed systems, ensuring energy balance in everything from chemical reactions to planetary motion.[29][30] Conservation of momentum maintains that the total momentum of an isolated system remains constant if no external forces act upon it. Linear momentum for a particle is defined as p=mv\mathbf{p} = m \mathbf{v}, where mm is mass and v\mathbf{v} is velocity, while angular momentum is L=Iω\mathbf{L} = I \omega, with II as the moment of inertia and ω\omega as angular velocity. These arise from Isaac Newton's laws of motion, particularly the third law stating that action and reaction forces are equal and opposite, as detailed in his 1687 Philosophiæ Naturalis Principia Mathematica. In collisions or interactions within isolated systems, momentum redistribution occurs without net change, exemplified by the recoil of a gun when firing a bullet.[31][32] The conservation of mass asserts that the total mass in a closed system—impermeable to matter exchange—remains constant throughout any process. First established by Antoine Lavoisier through precise experiments in the late 18th century, this law revolutionized chemistry by quantifying reactions and refuting earlier notions of matter creation or annihilation. In the framework of special relativity, Albert Einstein extended it in 1905 to the mass-energy equivalence principle, E=mc2E = mc^2, where energy EE interchanges with mass mm at the speed of light cc, allowing transformations like nuclear fission while conserving total mass-energy.[33][34] These conservation laws apply strictly to isolated or closed physical systems, where external influences are absent or negligible; in open systems, apparent non-conservation arises from unaccounted exchanges with the environment. Violations typically signal overlooked interactions, measurement errors, or the emergence of new physical regimes, such as quantum or relativistic effects. As noted in system classifications, isolated systems provide the ideal context for these principles' exact adherence, guiding analyses across scales from subatomic particles to cosmological structures.[28][35]

Equilibrium and Stability

In physical systems, equilibrium refers to a state where the system experiences no net change over time, maintaining balance among its internal processes and interactions. Thermal equilibrium occurs when two or more systems have reached the same temperature, resulting in no net heat flow between them. This condition is formalized by the zeroth law of thermodynamics, which states that if two systems are each in thermal equilibrium with a third system, they are in thermal equilibrium with each other, enabling the consistent measurement of temperature across systems.[36][37] Mechanical equilibrium describes a state in which a system is at rest or moving with constant velocity, with no net acceleration. For this to hold, the vector sum of all forces acting on the system must be zero ($ \sum \mathbf{F} = 0 ),andthesumofalltorquesaboutanyaxismustalsobezero(), and the sum of all torques about any axis must also be zero ( \sum \boldsymbol{\tau} = 0 $). These conditions ensure that the system's linear and angular momentum remain constant, preventing translational or rotational motion from changing.[38][39] Stability assesses how a system responds to small perturbations from its equilibrium state. In stable equilibrium, the system returns to its original position after a disturbance, as seen in a ball at the bottom of a valley where potential energy is minimized and restoring forces act to restore balance. Unstable equilibrium occurs when a perturbation causes the system to diverge further, such as a ball balanced on a hilltop where any deviation increases potential energy and amplifies displacement. Neutral equilibrium features no net restoring or diverging force, allowing the system to remain in a new position after perturbation, exemplified by a ball on a flat surface where potential energy is constant./09%3A_Statics_and_Torque/9.03%3A_Stability) In the context of dynamical systems, equilibrium points and their stability are analyzed in phase space, which represents all possible states of the system as points in a multidimensional space of variables like position and momentum. Attractors in phase space are subsets toward which trajectories evolve over time, such as fixed points for stable equilibria or limit cycles for oscillatory behaviors. Lyapunov stability specifically characterizes an equilibrium as stable if nearby trajectories remain close for all future times following small perturbations, and asymptotically stable if they converge to the equilibrium; this is determined by the existence of a Lyapunov function that decreases along trajectories, quantifying the system's resilience to infinitesimal disturbances.[40][41]

Modeling and Analysis

Mathematical Frameworks

Physical systems are described and analyzed using various mathematical frameworks that capture their dynamics at different scales and levels of complexity. Newtonian mechanics serves as the cornerstone for classical macroscopic systems, where the behavior of particles and rigid bodies is governed by deterministic laws. Central to this approach is Newton's second law, expressed as F=ma\mathbf{F} = m \mathbf{a}, where F\mathbf{F} is the net force acting on a body of mass mm and acceleration a\mathbf{a}. This equation yields a system of second-order ordinary differential equations for the trajectories, solvable under specified initial conditions and forces derived from potentials or interactions.[31] For more complex systems involving constraints or generalized coordinates, Lagrangian mechanics offers a reformulation that simplifies the analysis by focusing on energy rather than forces directly. Introduced by Joseph-Louis Lagrange, this framework defines the Lagrangian L=T−VL = T - V, where TT is the kinetic energy and VV is the potential energy. The equations of motion follow from the Euler-Lagrange equation: ddt(∂L∂q˙i)−∂L∂qi=0\frac{d}{dt} \left( \frac{\partial L}{\partial \dot{q}_i} \right) - \frac{\partial L}{\partial q_i} = 0, for each generalized coordinate qiq_i and its time derivative q˙i\dot{q}_i. This variational principle, derived from the principle of least action, is particularly advantageous for systems with symmetries or holonomic constraints, enabling the use of coordinates like angles or arc lengths. Hamiltonian mechanics extends the Lagrangian formulation into phase space, providing a symmetric treatment of position and momentum variables that proves essential for statistical mechanics and quantum transitions. William Rowan Hamilton developed this approach, defining the Hamiltonian H=T+VH = T + V as the total energy in terms of generalized coordinates qiq_i and conjugate momenta pip_i. The dynamics are then given by Hamilton's canonical equations: q˙i=∂H∂pi\dot{q}_i = \frac{\partial H}{\partial p_i} and p˙i=−∂H∂qi\dot{p}_i = -\frac{\partial H}{\partial q_i}. This first-order system preserves the symplectic structure of phase space, facilitating the study of conserved quantities via Poisson brackets and long-term stability. At the microscopic scale, quantum frameworks replace classical determinism with probabilistic wave mechanics to model systems where particles exhibit wave-like properties. The Schrödinger equation, formulated by Erwin Schrödinger, governs the time evolution of the wave function ψ\psi: iℏ∂ψ∂t=H^ψi \hbar \frac{\partial \psi}{\partial t} = \hat{H} \psi, where H^\hat{H} is the Hamiltonian operator incorporating kinetic and potential energies, ℏ\hbar is the reduced Planck's constant, and ii is the imaginary unit. Solutions to this partial differential equation yield probabilities via ∣ψ∣2|\psi|^2, enabling predictions for atomic and subatomic phenomena that classical mechanics cannot address.

Computational Approaches

Computational approaches play a crucial role in analyzing physical systems where analytical solutions are infeasible, enabling the simulation of complex dynamics through numerical techniques that approximate continuous equations on discrete computational grids or ensembles. These methods discretize the governing equations derived from Newtonian mechanics or statistical mechanics, allowing researchers to predict system behavior under various conditions, such as in fluid flows, material deformations, or atomic interactions. By leveraging high-performance computing, these techniques have transformed the study of physical systems from idealized models to realistic, large-scale simulations.[42] Numerical integration methods, particularly the Runge-Kutta family, are widely used to solve ordinary differential equations (ODEs) arising from Newtonian equations of motion in simulations of mechanical systems. Developed in the early 20th century, the classical fourth-order Runge-Kutta method (RK4) provides a balance of accuracy and computational efficiency by evaluating the derivative at multiple intermediate points within each time step, achieving an error of order O(h5)O(h^5) where hh is the step size. In physics simulations, such as orbital mechanics or particle trajectories, RK4 is applied to integrate second-order ODEs like r¨=F/m\mathbf{\ddot{r}} = \mathbf{F}/m, where r\mathbf{r} is position and F\mathbf{F} is force, enabling stable predictions over long timescales without excessive computational cost. For instance, in N-body gravitational simulations, RK4 has been employed to model planetary orbits with high fidelity, outperforming lower-order methods like Euler integration in preserving energy conservation.[43][44] Monte Carlo methods employ statistical sampling to estimate properties of probabilistic physical systems, particularly in statistical mechanics where exact solutions are intractable. Originating from work on neutron diffusion but adapted for molecular systems, these methods generate ensembles of random configurations according to a probability distribution, such as the [Boltzmann distribution](/page/Boltzmann distribution), to compute averages like pressure or energy. The seminal Metropolis algorithm, introduced in 1953, uses a Markov chain to sample states by proposing moves and accepting or rejecting them based on an energy difference ΔE\Delta E, with acceptance probability min⁡(1,e−ΔE/kT)\min(1, e^{-\Delta E / kT}), where kk is Boltzmann's constant and TT is temperature; this ensures ergodicity and convergence to equilibrium distributions. In applications to particle interactions in gases, Monte Carlo simulations have quantified phase transitions in hard-sphere systems, revealing phenomena like the fluid-solid transition that align with experimental data.[45] Finite element analysis (FEA) addresses continuum problems in physical systems by discretizing the domain into a mesh of finite elements, solving partial differential equations (PDEs) numerically for fields like stress or heat distribution. Pioneered in the 1950s for structural engineering, the method approximates solutions within each element using basis functions, such as linear polynomials for displacement, and assembles a global stiffness matrix to enforce equilibrium via Ku=f\mathbf{K} \mathbf{u} = \mathbf{f}, where K\mathbf{K} is the stiffness matrix, u\mathbf{u} the nodal displacements, and f\mathbf{f} the forces. This approach excels in simulating stress in solids under complex loads, as seen in early applications to aircraft wing analysis, where mesh refinement improves accuracy for irregular geometries without analytical tractability. Modern FEA implementations handle nonlinear materials and large deformations, providing quantitative insights into failure modes validated against experimental benchmarks.[42] Molecular dynamics (MD) simulates the time evolution of nanoscale physical systems by integrating Newton's laws for interacting atoms, using empirical potentials to model interatomic forces. Introduced in the late 1950s for hard-sphere gases, MD tracks trajectories via velocity Verlet integration, a symplectic method that updates positions and velocities in a single step with second-order accuracy, preserving energy better than explicit Euler schemes. Potentials like the Lennard-Jones form V(r)=4ϵ[(σ/r)12−(σ/r)6]V(r) = 4\epsilon [(\sigma/r)^{12} - (\sigma/r)^6] approximate van der Waals interactions, enabling studies of atomic motions in liquids or solids. For example, early MD simulations of argon revealed diffusion coefficients matching experimental values within about 15%, establishing the method's validity for probing thermodynamic properties at the atomic scale.[46]

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