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In systems theory, a system is said to be transient or in a transient state when a process variable or variables have been changed and the system has not yet reached a steady state. In electrical engineering, the time taken for an electronic circuit to change from one steady state to another steady state is called the transient time.

Examples

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Chemical Engineering

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When a chemical reactor is being brought into operation, the concentrations, temperatures, species compositions, and reaction rates are changing with time until operation reaches its nominal process variables.

Electrical engineering

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When a switch is closed in an electrical circuit containing a capacitor or inductor, the component draws out the resulting change in voltage or current, causing the system to take a substantial amount of time to reach a new steady state. This period of time is known as the transient state.

A capacitor acts as a short circuit immediately after the switch is closed, increasing its impedance during the transient state until it acts as an open circuit in its steady state.

An inductor is the opposite, behaving as an open circuit until reaching a short circuit steady state.

References

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A transient state, also referred to as an unsteady or non-steady state, describes the temporary phase in a dynamic system where internal variables—such as temperature, pressure, concentration, voltage, or energy levels—change over time in response to an external perturbation or input alteration, before the system settles into equilibrium or steady-state conditions.^[1] This phase is characterized by time-dependent behavior that eventually decays, often governed by differential equations modeling the system's response, and is distinct from steady-state operation where properties remain constant.^[2] In physics, transient states are fundamental to understanding short-lived phenomena, such as quantum mechanical processes where particles or waves exhibit temporary excitations before decaying to ground states, as seen in diffraction in time or radioactive decay dynamics.^[3] For instance, in astrophysics, transients manifest as sudden flux variations in celestial objects on timescales of days to months, providing insights into events like supernovae or gamma-ray bursts.^[1] These states highlight the non-equilibrium trajectories of systems, contrasting with equilibrium thermodynamics.^[4] In chemical engineering, transient states are critical during process startups, shutdowns, or disturbances in reactors and separation units, where mass and energy balances involve accumulation terms that drive temporal changes in composition and temperature until steady flow is achieved.^[5] Transient analysis here often employs methods like the method of lines to solve partial differential equations for predicting response times and optimizing control strategies.^[6] Similarly, in electrical engineering, transients occur in circuits due to switching events, capacitor charging, or inductive surges, lasting from microseconds to seconds and potentially causing voltage spikes that require protective measures like surge arrestors.^[7]^[8] The duration and magnitude of these transients are quantified using time constants derived from circuit parameters, ensuring system stability and reliability.^[9]

General Concepts

Definition

A transient state occurs in dynamic systems when system variables are changing over time, as the system transitions between an initial condition and a final steady state without yet having reached equilibrium.^[1] This phase represents the temporary period during which the system's response to a perturbation or input change evolves toward stability.^[7] In contrast to a steady state, where variables remain constant and independent of time (time-invariant), a transient state is fundamentally time-dependent and finite in duration, eventually giving way to equilibrium under typical conditions.^[2] The distinction underscores the transient's role as an intermediary dynamic, driven by imbalances that resolve over time.^[10] The concept of transient states applies generally across physical, chemical, biological, and engineered systems, arising whenever inputs, disturbances, or initial conditions cause deviations from balance, leading to evolving behaviors.^[1] The term "transient" derives from the Latin transire, meaning "to cross over" or "to pass through."^[11] The concept emerged in 19th-century dynamics, notably in electromagnetic theory as developed by James Clerk Maxwell, whose work on field propagation laid foundational insights into time-varying phenomena.^[12]

Characteristics

Transient states in dynamical systems are inherently short-lived phases during which the system's variables evolve away from initial conditions toward equilibrium, typically decaying or oscillating over a finite duration until approaching a steady state. This temporal behavior often follows patterns of exponential decay in simpler systems or damped oscillations in more complex ones, with the transition period varying based on the system's inherent dynamics.^[13]^[7] Key properties of transient states include time-dependent evolution during the transition, where the system may exhibit unpredictable or amplified responses before stabilization, alongside high sensitivity to initial conditions that can significantly alter the path and outcome of the evolution. In oscillatory systems, transients frequently involve overshoot, where variables exceed their final values, or ringing, characterized by repeated oscillations that gradually diminish. These traits distinguish transients from the constant equilibrium of steady states, serving as the endpoint of this decay process.^[14]^[13]^[7] The duration and amplitude of transients are influenced by several factors, including system inertia, which contributes to slower responses in massive or high-momentum setups, and damping mechanisms that control the rate of energy dissipation and oscillation decay. External forcing functions, such as sudden inputs or disturbances, can prolong or intensify transients, while nonlinearity in the system's governing relations may introduce irregular or chaotic-like behavior during the phase. These elements collectively determine how quickly or erratically the system navigates the transition.^[14]^[13]^[7] Observability of transient states is achieved through time-series data analysis, where non-constant variables—such as fluctuating voltages, temperatures, or positions—reveal the ongoing evolution, in stark contrast to the uniform readings indicative of steady-state equilibrium. This detection is essential for assessing system performance and stability in real-time monitoring.^[13]^[14]

Transient States in Physical Sciences

In Physics

In physics, transient states refer to temporary dynamic behaviors in physical systems that evolve from an initial condition toward equilibrium or a steady state, often involving energy dissipation or propagation effects. In mechanical systems, particularly oscillatory ones like pendulums or mass-spring setups, transients manifest as damped motion following an initial displacement. This process is driven by restoring forces, such as gravity or elasticity, opposed by frictional damping, leading the system to gradually lose amplitude until rest. The damped harmonic oscillator exemplifies this, where the transient response decays exponentially over time, illustrating how initial kinetic and potential energy dissipates into heat via friction.^[15] In quantum mechanics, transient states appear in time-dependent processes, such as atomic excitations where the time-dependent Schrödinger equation describes the evolution from an initial ground state to an excited configuration before relaxation to equilibrium. For instance, laser-induced excitations create short-lived superpositions in atomic systems, with the transient dynamics involving coherent oscillations that decay via spontaneous emission or environmental interactions. Specific examples include diffraction in time, where quantum particles exhibit wave-like interference patterns during free evolution, and radioactive decay, where unstable nuclei transition to stable states through probabilistic emission of particles, following exponential decay laws.^[3] This underscores the role of quantum transients in phenomena like fluorescence, where the system bridges non-stationary wavefunctions en route to stable eigenstates.^[16] In astrophysics, transients are observed as sudden variations in the flux of celestial objects on timescales from days to months, such as supernovae explosions or gamma-ray bursts, providing insights into high-energy astrophysical processes before the system returns to a steady emission state.^[1] A practical example of mechanical transients is seen in structural vibrations following an impact, such as a vehicle collision with a bridge, where initial kinetic energy excites oscillatory modes that dissipate through material damping and friction, preventing prolonged resonance. This transient response, characterized by decaying free vibrations, is critical for assessing structural integrity, as energy absorption via viscoelastic effects or joints ensures the system returns to equilibrium without catastrophic failure.^[17]^[18]

In Chemistry and Thermodynamics

In chemical kinetics, transient states occur during the initial phases of a reaction when reactant concentrations decrease and product concentrations increase, often involving short-lived intermediates that do not reach steady-state levels until equilibrium is approached. These dynamics are governed by rate laws that describe the time-dependent evolution of species concentrations, revealing mechanisms through techniques like pulsed laser ionization to track non-stationary behavior in catalytic reactions. For instance, in CO oxidation on palladium surfaces, oxygen coverage depletes rapidly under transient conditions, shifting kinetics from reaction-limited to diffusion-controlled regimes before stabilization.^[19] Thermodynamic transients manifest in processes like heat transfer and phase changes, where systems deviate from equilibrium due to temperature or pressure gradients, leading to temporary imbalances resolved over time. In the cooling of a hot object exposed to a cooler fluid, convective heat transfer drives a transient temperature profile, modeled by lumped-parameter analysis where the object's internal temperature uniformizes faster than external convection for low Biot numbers (Bi < 0.1), allowing exponential decay toward ambient conditions. Similarly, during evaporation, such as in pot-in-pot cooling systems, latent heat absorption creates non-equilibrium vapor-liquid interfaces, with wind enhancing mass transfer and cooling rates up to 10-15°C below ambient through increased evaporation flux.^[20]^[21] Non-equilibrium thermodynamics, as developed by Ilya Prigogine, explains how transient states far from equilibrium can produce ordered dissipative structures through irreversible processes, where fluctuations amplify into coherent patterns sustained by energy dissipation. In these regimes, systems exhibit instabilities like the Bénard convection cells or chemical oscillations in autocatalytic reactions (e.g., the Brusselator model), transitioning from disordered transients to self-organized steady states via bifurcations. Prigogine emphasized that "non-equilibrium may be a source of order," linking microscopic irreversibility to macroscopic structure formation in open systems.^[22] A key example of transient states in chemistry is the formation and decay of free radicals during combustion, where species like H, OH, and CH₃ arise via chain-branching reactions (e.g., H + O₂ → OH + O) and exceed equilibrium concentrations due to upstream diffusion, accelerating ignition before steady flame propagation. These highly reactive intermediates decay through slower recombination (e.g., H + H + M → H₂ + M), with their transient abundance critical for overall reaction rates in the pre-steady phase.^[23]

Transient States in Engineering

Electrical Engineering

In electrical engineering, transient states in circuits arise from sudden changes in excitation, such as switching, leading to temporary deviations from steady-state conditions before settling. In RC networks, a step voltage input causes the capacitor to charge exponentially, with the output voltage following

v(t) = V_s (1 - e^{-t/\tau})

, where the time constant

\tau = RC

determines the response speed, reaching approximately 95% of the final value after

3\tau

.^[24] Discharging exhibits similar exponential decay,

v(t) = V_0 e^{-t/\tau}

, without overshoot but potentially inducing current spikes limited by the resistor. In RL networks, a step current response through the inductor rises or decays exponentially as

i(t) = I_s (1 - e^{-t/\tau})

i(t) = I_0 e^{-t/\tau}

, with

\tau = L/R

, where initial voltage spikes across the inductor can reach the supply voltage during switching.^[24] RLC networks exhibit more complex transients due to energy exchange between the inductor and capacitor. The response is governed by the damping ratio, with underdamped cases (

\alpha < \omega_0

, where

\alpha = R/(2L)

and

\omega_0 = 1/\sqrt{LC}

ω0=1/LC

) producing oscillatory voltage and current with decaying amplitude, $v_c(t) = V_s + A_1 e^{s_1 t} + A_2 e^{s_2 t}$ where $s_{1,2} = -\alpha \pm j \sqrt{\omega_0^2 - \alpha^2}$

, often resulting in spikes exceeding the supply voltage before settling.^[25] Overdamped responses ( $\alpha > \omega_0$ ) show non-oscillatory exponential decay without spikes, while critically damped cases ( $\alpha = \omega_0$ ) provide the fastest settling without overshoot. These behaviors, analyzed via differential equations, are essential for designing filters and amplifiers to handle switching-induced transients.^[25] In power systems, transients from lightning strikes or faults generate high-magnitude surges that propagate along transmission lines, analyzed to ensure insulation withstands overvoltages up to several per unit.^[26] Lightning surges, modeled as double-exponential currents, induce transient overvoltages through direct strikes or inductive coupling, requiring coordination of insulation levels and protective margins as per IEEE guidelines.^[27] Faults, such as line-to-ground short circuits, produce similar surges during clearing, with peak values influencing arrester selection and grounding design.^[26] Notable phenomena include ferroresonance in transformers, a nonlinear transient oscillation between the saturated core inductance and line capacitance, often triggered by switching under light load, resulting in overvoltages up to 11 per unit and harmonic distortion that risks insulation failure.^[28] Capacitor inrush currents during bank energization create high-frequency transients, with single-bank switching yielding peaks around 3 kA at hundreds of Hz and back-to-back operations reaching 28 kA at 5.6 kHz, stressing breakers and inducing voltage magnification.^[29] Mitigation strategies focus on limiting surge magnitude and duration. Metal-oxide surge arresters clamp transients by conducting during overvoltages above their rating, diverting lightning or switching currents while blocking steady-state power flow, as standardized for AC systems above 1 kV.^[30] Snubber circuits, typically RC networks across switches, absorb energy from inductive kickback to suppress voltage spikes and ringing, with component values selected as $R = \sqrt{L/C}$

to damp oscillations effectively in power electronics.^[31] These devices, informed by simulations like EMTP, reduce transient durations from milliseconds to microseconds, enhancing system reliability.^[26]

Chemical Engineering

In chemical engineering, transient states occur during dynamic operations in unit processes such as reactors, distillation columns, and heat exchangers, where system variables like concentration, temperature, and flow rates evolve over time in response to changes in inputs or operating conditions.^[32] These transients are critical for ensuring safe startups, shutdowns, and responses to disturbances, as they can lead to temporary deviations from steady-state performance, affecting product quality and energy efficiency.^[33] Unlike steady-state operations, transient analysis relies on unsteady-state mass, energy, and momentum balances to model the time-dependent behavior of these systems.^[34] Reactor transients are prominent during startup and shutdown of batch or continuous stirred-tank reactors (CSTRs) and plug flow reactors (PFRs), where concentration profiles and temperatures adjust dynamically. For instance, in a startup phase, an empty or inert-filled reactor is gradually filled with feed while heating to reaction conditions, resulting in evolving species concentrations that approach steady-state values over time; this process can take hours depending on reactor size and reaction kinetics.^[32] Shutdown involves ceasing feed flow and cooling, often requiring careful control to prevent side reactions or thermal runaway, with transient periods lasting minutes to hours based on heat transfer rates.^[33] These dynamics are governed by differential mass and energy balances, highlighting the need for predictive modeling to optimize transition times and minimize off-specification product.^[35] In distillation and separation processes, transient profiles arise from events like column flooding or pressure fluctuations, which disrupt vapor-liquid equilibrium and alter separation efficiency. Flooding, caused by excessive liquid buildup, leads to temporary reductions in throughput and purity, with recovery times on the order of minutes to hours as liquid holdup redistributes.^[36] Pressure changes, such as those from valve adjustments, induce composition transients that propagate through the column, impacting distillate and bottoms yields; for example, a step change in feed pressure can cause initial overshoots in product purity before stabilizing.^[37] Dynamic models approximate these responses using two-time-constant approximations, where a dominant slow time constant (e.g., 194 minutes for large columns) governs overall purity recovery, while faster internal flows affect local compositions.^[38] Process control transients in pipelines and heat exchangers manifest as responses to feed disturbances, such as sudden changes in flow rate or inlet composition, leading to temporary instabilities in transport and heat transfer. In pipelines, a step disturbance in feed flow can generate pressure waves that propagate at speeds up to hundreds of meters per second, causing surges that affect downstream units until damped by friction.^[39] For heat exchangers, feed temperature perturbations result in outlet temperature lags, with transient durations influenced by wall capacitance; high capacitance can extend response times to over an hour, potentially destabilizing connected reactors.^[40] These effects underscore the importance of transient analysis in designing robust control strategies to maintain process stability. A representative example is the transient mass balance in a CSTR subjected to a step input change in feed concentration, which illustrates temporary instability during approach to steady state. The unsteady-state mass balance for reactant A is given by

\frac{d(NA)}{dt} = F_{A0} - F_A + r_A V

where

N_A

is moles of A,

F_{A0}

and

F_A

are inlet and outlet molar flows,

r_A

is the reaction rate, and

V

is reactor volume; assuming constant volume and density, this simplifies to a first-order differential equation in concentration

C_A

.^[32] For a step increase in

F_{A0}

C_A

initially rises above steady-state value before declining exponentially to equilibrium, with the time constant

\tau = V / F

(where

F

is volumetric flow rate) determining the duration of instability, often 3–5

\tau

for 95% settling.^[41] This behavior highlights risks like over-conversion or catalyst deactivation during transients.^[42]

Control Systems

In control systems, transient states manifest prominently during the step response of closed-loop configurations, such as those employing proportional-integral-derivative (PID) controllers, where key performance metrics include rise time, settling time, and overshoot. Rise time is defined as the duration for the system's output to transition from 10% to 90% of its final steady-state value following a step input, reflecting the speed of initial response. Settling time measures the interval required for the response to remain within a specified tolerance band, typically 2% or 5%, of the steady-state value, indicating how quickly the system stabilizes after perturbation. Overshoot quantifies the maximum deviation beyond the steady-state value, expressed as a percentage, which can lead to undesirable oscillations if excessive. These metrics are critical for evaluating PID performance, as proportional gain (Kp) primarily reduces rise time but may increase overshoot, while derivative gain (Kd) dampens overshoot and shortens settling time at the cost of noise sensitivity.^[43] Stability during transient phases is governed by the locations of poles in the s-domain of the system's transfer function, which dictate the nature of decay in the response. Poles with negative real parts ensure asymptotic stability, where the transient decays over time; real poles yield monotonic exponential decay, while complex conjugate poles with negative real parts produce damped oscillatory behavior, with the imaginary part determining oscillation frequency and the real part controlling decay rate. If any pole has a positive real part, the transient response grows unbounded, leading to instability. In PID-controlled systems, proper tuning shifts poles leftward in the s-plane to enhance damping, reducing oscillatory transients and ensuring the response converges reliably.^[44] Practical applications highlight the importance of managing transients for system efficacy, as seen in robotic arms where feedback control adjusts joint positions against payload disturbances, with transient phases determining precision and avoiding collisions during motion. Similarly, in aircraft autopilots, transient responses are essential for countering atmospheric gusts, enabling rapid stabilization of flight path without excessive maneuvering that could compromise passenger comfort or structural integrity. These scenarios underscore how transient behavior influences overall performance, particularly in dynamic environments requiring quick adaptation.^[45]^[46] Design considerations in control systems prioritize tuning strategies to minimize transient effects, such as optimizing PID parameters via methods like Ziegler-Nichols to achieve settling times below 5% error tolerance while constraining overshoot to under 10%. This involves iterative adjustments where integral gain (Ki) eliminates steady-state error but may prolong settling if not balanced, emphasizing simulation tools for predicting pole placements and response characteristics. Electrical components, like actuators in control hardware, briefly interface with these transients by converting controller signals into mechanical action, but system-level tuning remains paramount.^[43]

Mathematical Modeling

Differential Equations

Transient states in physical systems are often modeled using ordinary differential equations (ODEs) for lumped-parameter systems, where the system's variables are assumed to be uniform across space. A first-order ODE describes simple exponential decay, such as in an RC circuit during capacitor discharge, given by the equation

\frac{dv}{dt} + \frac{1}{RC} v = 0,

where

v(t)

is the voltage across the capacitor,

R

is resistance, and

C

is capacitance.^[47] For more complex behaviors like damped oscillations in an RLC circuit, a second-order ODE is used:

L \frac{d^2 i}{dt^2} + R \frac{di}{dt} + \frac{1}{C} i = 0,

where

i(t)

is the current,

L

is inductance, and the terms represent inertial, dissipative, and restorative forces analogous to mechanical systems.^[48] In distributed-parameter systems, such as heat conduction in a solid, partial differential equations (PDEs) capture spatial and temporal variations during transients. The one-dimensional heat equation models transient temperature evolution as

\frac{\partial T}{\partial t} = \alpha \frac{\partial^2 T}{\partial x^2},

where

T(x,t)

is temperature,

x

is position,

t

is time, and

\alpha

is thermal diffusivity.^[49] Transient models distinguish between linear and nonlinear differential equations. Linear ODEs and PDEs obey the superposition principle, allowing solutions to be combined additively for multiple inputs or initial conditions, which simplifies analysis of transient responses in systems like electrical circuits or linear heat flow.^[50] In contrast, nonlinear equations can exhibit complex transients, including chaotic behavior where small perturbations lead to unpredictable evolution before settling, as seen in certain dynamical systems.^[51] Modeling transient states typically involves initial value problems (IVPs), where the differential equation is supplemented with initial conditions specifying the system's state at

t=0

, such as

y(0) = y_0

for an ODE or

u(x,0) = f(x)

for a PDE, to uniquely determine the time-dependent solution describing the transient evolution.^[52]

Solution Methods

Analytical methods for solving transient differential equations often involve transforming the problem into the frequency domain to simplify the algebra. The Laplace transform converts ordinary differential equations (ODEs) describing transient behavior into algebraic equations, allowing for straightforward manipulation before inverting back to the time domain to obtain the transient response.^[53] This approach is particularly effective for linear systems with constant coefficients, as demonstrated in the analysis of initial value problems where initial conditions are incorporated directly into the transform.^[54] The inverse Laplace transform then yields the time-domain solution, capturing the exponential decay or oscillatory transients typical in physical systems.^[55] Numerical methods provide practical solutions for nonlinear or complex transient equations that resist analytical treatment. The Euler method, a first-order Runge-Kutta variant, approximates the solution by stepping forward in time using the derivative at each point, offering simplicity for initial simulations of transient dynamics.^[56] Higher-order methods like the fourth-order Runge-Kutta algorithm improve accuracy by evaluating the derivative multiple times per step, making it suitable for simulating transient responses in software environments such as MATLAB, where built-in solvers like ode45 implement these techniques efficiently.^[57] These numerical integrations are essential for validating transient behaviors in control systems, where step responses confirm stability and settling times.^[58] The time constant, denoted as

\tau

, quantifies the speed of transient decay in first-order systems, representing the time for the response to reach approximately 63% of its final value. In electrical circuits, for instance,

\tau = RC

for resistor-capacitor networks, where

R

is resistance and

C

is capacitance, providing a scale for how quickly the system approaches steady state.^[59] Similar forms apply to other domains, such as

\tau = L/R

in inductor-resistor circuits, aiding in the prediction of transient duration without full simulation.^[60] For complex systems near linearity, approximation techniques like modal analysis decompose the transient response into contributions from dominant modes, reducing computational demands by focusing on low-frequency behaviors.^[61] Perturbation methods further refine this by treating small deviations from equilibrium as corrections to the base solution, enabling efficient analysis of transient perturbations in structures or fluids.^[62] These approaches are widely used in modal dynamic analyses to capture short-term transients while assuming modal superposition for longer-term responses.^[63]

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General Concepts

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In Chemistry and Thermodynamics

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Mathematical Modeling

Differential Equations

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