This section presents the main theoretical contributions of the paper, structured into four subsections.
3.2. Generalized Laplace Transform on Space
This section is devoted to the study of the generalized Laplace transform formulations for both the derivative and the -Hilfer fractional derivative on space . We begin by establishing sufficient conditions for the existence of the generalized Laplace transform for functions in this weighted space, including the concept of -exponential order. We then derive key operational properties, such as the transform of the first derivatives, a convolution theorem tailored to the -structure, and the action of the transform on -Riemann–Liouville fractional integrals and derivatives. Furthermore, we extend these results to obtain explicit Laplace transform formulas for the -Caputo and -Hilfer fractional derivatives.
Definition 14 (Exponential-order function, [
14])
. A function is said to be -exponential-order if there exist non-negative constants such that We can now establish a sufficient condition for the existence of the generalized Laplace transform. The following theorem guarantees that the transform converges for a specific region in the complex plane.
Theorem 3 (Existence of generalized Laplace transform). Let be a strictly increasing and differentiable function. Suppose is integrable on every finite interval and of ψ-exponential order . Then, the generalized Laplace transform exists for all with .
Proof. We will show that the generalized Laplace transform of
f, defined by (
10), converges absolutely for all
s such that
.
By assumption,
f is of
-exponential order
. This means there exist constants
and
such that
We split the integral defining the transform into two parts:
Since f is integrable on the finite interval of , is continuous and differentiable with on , and the exponential term is continuous.Therefore, the integrand is continuous on the closed interval of , which implies the first integral is finite.
For
, we use the following exponential order bound:
The equality expressed as
holds because
. We integrate this upper bound:
To evaluate this integral, we use the substitution of
. Then,
. The limits change as follows: when
,
; when
,
. Applying the substitution yields the following:
This is a standard exponential integral. It converges if and only if
, and its value is expressed as follows:
Therefore, according to the Comparison Test for improper integrals, the integral of
converges absolutely for
.
Since both parts of the original integral converge, the generalized Laplace transform () exists for . This concludes the proof. □
The existence theorem implies a particularly useful result for the well-behaved functions in space .
Corollary 1 (Existence of generalized Laplace transform). Let for any with ψ-exponential order . Then, its generalized Laplace transform exists for .
Proof. Since for any , it follows that f is continuous on (because ). Therefore, is integrable on every finite interval . According to Theorem 3, this implies that the generalized Laplace transform exists for all . This completes the proof. □
Remark 5. When , the generalized Laplace transform reduces to the classical Laplace transform , and the ψ-exponential order condition becomes the classical exponential order condition. In this case, the corollary recovers the well-known existence theorem for the classical Laplace transform, requiring that and for to ensure convergence for .
A fundamental property of the Laplace transform is its action on derivatives. The following lemma provides the analogous result for the generalized operator .
Theorem 4 (Generalized Laplace transform of the first-order
derivative)
. Let for any and be of -exponential order Then, the generalized Laplace transform of exists and is given byfor , where Proof. We begin with the definition of the generalized Laplace transform for
:
The strategy is to evaluate this integral via integration by parts. We now analyze the boundary terms. The lower limit is straightforward:
For the upper limit, we use the assumption that
f is of
-exponential order
. This means there exist constants
M and
such that
for all sufficiently large
. Therefore, for
, we have the following:
Since
is a strictly increasing function,
as
and
, it follows that the exponential term
. According to the squeeze theorem, we conclude the following:
Substituting these results for the boundary terms back into our equation, we find the following:
The integral in the final term is precisely the definition of the generalized Laplace transform of
. The convergence of this integral for
is guaranteed by the existence theorem (Corollary 1). Therefore,
for
which completes the proof. □
While a definition of convolution is provided in [
14], it requires the function to be piecewise continuous, a restriction we do not impose based on the function space under consideration. The following definition introduces a convolution operation compatible with the structure of the generalized Laplace transform. This
-convolution is crucial for formulating a convolution theorem and an inversion formula.
Definition 15 (Generalized
-convolution)
. Let be a strictly increasing and differentiable function with for all . Let be functions such that the integral below exists. The generalized convolution of f and h with respect to ψ is defined by We now show that the generalized -convolution operation is commutative, mirroring a fundamental property of the standard convolution.
Theorem 5 (Commutativity of generalized
-convolution)
. Let be a strictly increasing and differentiable function with for all . If are functions such that the generalized convolutions and are well-defined, then Proof. We show the equality by starting with the definition of
and applying a change of variable. By definition,
Let
, which is equivalent to
. Then,
and
. The limits change as follows: when
,
; when
,
. Substituting into the integral yields the following:
Substituting back for
and renaming the dummy variable (
u) to
yields the following:
which completes the proof. □
The convolution theorem is a cornerstone of operational calculus. The following result shows that the generalized Laplace transform converts the generalized -convolution into a simple product in the transformed domain.
Theorem 6 (Convolution theorem for the generalized Laplace transform)
. Let be a strictly increasing and differentiable function. If are functions such that their generalized Laplace transforms and exist for for some , then the generalized Laplace transform of the convolution () is given by Proof. According to the definitions of the generalized Laplace transform and the convolution and by changing the order of integration (which is justified by Fubini’s theorem for functions of exponential order and the fact that integrals converge absolutely for
), we obtain the following:
Let us focus on the inner integral. We perform the change of variable as follows:
Then,
. When
,
; when
,
. Also, note that
and
Substituting into the inner integral yields the following:
Now, perform a second change of variable: let
, so
and
. When
,
; when
,
. This yields the following:
Finally, we perform a third change of variable: let
, so
and
. When
,
; when
,
. This yields the following:
According to the definition of the generalized Laplace transform,
for
. Therefore,
Now, substituting Equations (
18)–(
21) back into Equation (
17) yields the following:
Again, according to the definition of the generalized Laplace transform,
for
. Therefore,
Substituting this back yields the final result:
for
. This completes the proof. □
We now establish a key representation formula that expresses the -Riemann–Liouville fractional integral of a function in terms of its higher-order derivative and initial values.
Theorem 7 (Representation of fractional integral in space
)
. Let , with , , and be strictly increasing with on . If , then the ψ-Riemann–Liouville fractional integral of f has the following representation: Proof. Given
, it has the following representation (
12):
where
and
Applying the
-fractional integral operator (
) throughout the above equation, we obtain
For the double integral term, we apply the Dirichlet technique for exchanging integrals and use the substitution of
:
where
is the Beta function and we use the identity of
. For the single integral sum, we similarly compute the following:
Combining these results, we obtain (
22), which is the desired result. □
An important property of fractional integrals is that they preserve regularity. The following theorem shows that the -Riemann–Liouville fractional integral maps functions from into the space of continuous functions.
Theorem 8 (Regularity preservation under -Riemann–Liouville fractional integration). Let , with , , and be strictly increasing with for all . If , then the ψ-Riemann–Liouville fractional integral is continuous on , i.e.,
Proof. The representation of
is given by (
22):
The integral term is denoted by
, and the sum term is denoted by
. We show that both
and
are continuous on
.
Since and , we have . For each , Thus, each term is continuous on (vanishing at and nonzero continuous for ). Since is finite for each k, the sum is continuous on .
To show is continuous on , we consider the following:
1. With respect to the continuity of
on
, let
. For
, we split the integral as follows:
where we define the kernel function as
Now, we denote the two integrals as follows:
(a) For
: For
,
is bounded and
is bounded, since
(as
implies
). Also,
is bounded near
. Thus, the integrand is bounded, say by
L, and
(b) For
: Fix
. The integrand converges pointwise to 0 as
(the set
has a measure zero and can be ignored). To apply the dominated convergence theorem, note that for values of
t sufficiently close to
, the continuity of
in
t implies
Since
, it follows that
. Therefore, for some constant
,
Substitute into the integral:
We claim this dominating function is integrable on
. Let
, so
. When
,
; when
,
. Then, the integral becomes the following:
This is a Beta integral. Let
, so
,
. Then,
and
. The integral becomes the following:
The Beta function
converges because
and
(since
). Therefore, the dominating function is integrable. Accordign to the dominated convergence theorem,
Thus,
as
. A similar argument holds for
. Hence,
is continuous on
.
2. Continuity of
at
. We have the following:
Using the bound of
and computing the integral similarly to part 1(b), we obtain
Since
(as
and
), we have
as
. Hence,
as
, and
is continuous at
.
We have shown that both and are continuous on . Therefore, is continuous on . □
The generalized Laplace transforms of the generalized fractional integrals and the generalized fractional derivatives are presented below.
We first consider a special case of the main regularity result when .
Theorem 9. Let , with , and let be strictly increasing with for all . Suppose satisfiesIf , then . Proof. Let
. According to Theorem 7, it can be represented as follows:
Then, by using the Leibniz rule, we obtain the following:
We will show that is continuous on .
Multiplying both sides of (
23) by
, the second term becomes
and the first term becomes
Since
, the second term
is continuous on
. It remains to be shown that
is continuous on
, which is done as follows:
1. With respect ot he continuity of
on
, let
. For
, we split the integral as follows:
where we define the kernel function as
Now, we define the two integrals
and
as follows:
(a) For
, because
, the
function is continuous on
and, therefore, is bounded on the compact interval of
. Also,
is continuous and positive on
, so it is bounded above and bounded away from 0 on
. Thus, there exist constants
and
such that
Then, we have
Making the change of variable of
,
; then, using
, we obtain the following:
Combining these estimates yields the following
for a constant of
. As
, we have
; therefore,
. This proves
as
.
(b) For
, for each fixed
, the integrand converges pointwise to 0 as
(the point, i.e.,
, is a set of measure zero and can be ignored). To dominate, note the following:
for values of
t sufficiently close to
. We have the following:
Since
, we know that
, meaning
for some constant
(because
is continuous on
and, hence, bounded). Substituting into the integral yields the following:
We claim that the dominating function, i.e.,
is integrable on
(although it has a singularity at
). Let
so that
. When
,
; when
,
. Then, the integral becomes the following:
This is a Beta integral. Let
, so
and
. When
,
; when
,
. Then,
and
Therefore, the integral becomes the following:
The integral is the Beta function:
converges because the exponents satisfy
and
. Therefore,
Thus, according to the dominated convergence theorem,
Thus,
as
. A similar argument holds for
. Hence,
is continuous on
.
2. Continuity of
at
. We had
for some
on
. Also,
is continuous on
and, hence, bounded. Therefore,
Let
, so
, and when
,
; when
,
. Then,
This is a Beta integral. Let
, so
,
, and
Therefore,
Since
, this tends to 0 as
; then,
is continuous at
.
We have shown that is continuous on . Hence, . Also, according to Theorem 8 for . Therefore, . This completes the proof. □
The next fundamental result provides the generalized Laplace transform of the
-Riemann–Liouville fractional integral, which generalizes the classical property of
Theorem 10 (Generalized Laplace transform of
-Riemann–Liouville fractional integral)
. Let , with Suppose is integrable on every finite interval and of ψ-exponential order . Then, the generalized Laplace transform of the ψ-Riemann–Liouville integral exists, and Proof. The proof proceeds by expressing the fractional integral as a generalized convolution, then applying the convolution theorem. First, recall the
-Riemann–Liouville fractional integral of Equation (
5)
Since
f is integrable, the fractional integral
is well-defined on every interval
.
The kernel function is defined as
Through a change of variable
, the fractional integral can be rewritten as follows:
According to the generalized convolution theorem (Theorem 6), we have the following:
According to the assumption in Corollary 1, the generalized Laplace transform of
f exists for
. The generalized Laplace transform of
is computed directly using the substitution of
:
for
. Substituting this result yields the final formula:
The existence for
follows from the assumptions on
f. We have therefore proven the claim. □
Corollary 2 (Generalized Laplace transform for -RL integrals on weighted spaces). The result of Theorem 10 remains valid under either of the following regularity conditions on f:
- I.
(or ) for every and ;
- II.
for every .
Proof. It is sufficient to establish the well-definedness of the fractional integral (
5) under both regularity conditions.
The key issue is the integrability of the kernel
, which exhibits a potential singularity at
. Since
(as
with
), this singularity is integrable. To see this, make the substitution
near
:
which converges as
precisely when
.
Now consider the following two cases:
Case I: For
, the continuity of
f on
ensures that
f is bounded. Let
. Then,
The right-hand side is integrable on
, since
, as shown above.
Case II: For
, the weighted continuity condition implies there exists a continuous function
such that
with
. Let
. The integrand becomes the following:
We analyze the singularities at each endpoint separately:
Near , the factor produces a singularity of order . Since , we have , ensuring integrability.
Near , the factor produces a singularity of order . Since , we have , which also guarantees integrability.
Since these singularities occur at different endpoints and the
function is bounded on
, the product is integrable on
. More precisely, we can bound the integrand:
To verify integrability, make the substitution of
, which transforms the integral into a Beta function:
which is finite when
and
.
Thus, in both cases, is integrable on , ensuring that is well-defined. The generalized Laplace transform result then follows from Theorem 10. □
We now present a key theorem for solving fractional differential equations: the generalized Laplace transform of the -Riemann–Liouville fractional derivative on the weighted space . This result extends the classical formula for derivatives to the fractional case and requires careful handling of initial conditions.
Theorem 11 (Generalized Laplace transform of
-Riemann–Liouville fractional derivative)
. Let with , and let satisfyLet for any , and assume is a strictly increasing function with for all . Suppose f and are of ψ-exponential order . Then, the generalized Laplace transform of the ψ-Riemann–Liouville fractional derivative is expressed as follows:for , where and is the ψ-Riemann–Liouville fractional integral of order . Proof. We begin by taking the definition of the
-Riemann–Liouville fractional derivative (
6), i.e.,
where
denotes the
-Riemann–Liouville fractional integral of order
. Now, taking the generalized Laplace transform on both sides, we have
for
Since
and
, it follows from Theorem 9 that
. Moreover, by hypothesis,
is of
-exponential order. Therefore, applying Theorem 4 to the right-hand side of (
24) yields the following:
for
As
and is given to be of
-exponential order, we invoke Theorem 10 to obtain the following:
for
According to (
24)–(
26), we have
for
This completes the proof. □
We now present the generalized Laplace transform of the -Caputo fractional derivative on weighted space . This result is particularly useful for solving initial value problems.
Theorem 12 (Generalized Laplace transform of
-Caputo fractional derivative)
. Let with and for any . Assume is a strictly increasing function with for all and let be of -exponential order . Then,for , where . Proof. We begin by taking the definition of the
-Caputo fractional derivative (
7), i.e.,
where
denotes the
-Riemann–Liouville fractional integral of order
. Now, taking the generalized Laplace transform on both sides, we have
for
Since
, it follows that
. Moreover, by hypothesis,
is of
-exponential order
. Therefore, applying Corollary 2 II to the right-hand side of (
27) yields the following:
for
As
and, by hypothesis,
are of
-exponential order
, we invoke Theorem 4 to obtain the following:
for
This completes the proof. □
We now present the most general result of this section: the generalized Laplace transform of the -Hilfer fractional derivative. This theorem provides a unified formula that incorporates both the -Riemann–Liouville and -Caputo definitions as special cases (when and , respectively).
We present the specialized result for order , which is a common case in applications. The formula simplifies significantly when .
Theorem 13 (Generalized Laplace transform of
-Hilfer fractional derivative)
. Let with and for any Assume is a strictly increasing function with for all and are of -exponential order . Then,for , where . Proof. Let
with
. For the convenience of computation, we begin by taking the definition of the
-Hilfer fractional derivative using the
-Caputo fractional derivative given in Remark (2),
where
denotes the
-Riemann–Liouville fractional integral of order
. Now, taking the generalized Laplace transform on both sides, we have
for
Since
, where
(because
), it follows from Theorem 9 that
. Moreover, by hypothesis,
are of
-exponential order
. Therefore, applying Theorem 12 to the right-hand side of (
29) yields the following:
for
. As
and is given to be of
-exponential order
, we invoke Theorem 10 to obtain the following:
for
. Substituting (
31) into (
30), we obtain
for
. This completes the proof. □
The exponential function plays a very important role in the theory of integer-order differential equations. Its one-parameter generalization and two-parameter generalization are defined below. The two-parameter function of the Mittag-Leffler type plays a very important role in fractional calculus.
Definition 16 (One-parameter Mittag-Leffler function [
2])
. The Mittag-Leffler function involving one parameter is given by Definition 17 (Two-parameter Mittag-Leffler function [
2])
. The Mittag-Leffler function involving two parameters is given by The following lemma provide essential generalized Laplace transforms of Mittag-Leffler functions. These results are crucial for solving fractional differential equations using the transform method, as they allow us to invert transforms and identify solutions in the time domain.
Lemma 4 (Generalized Laplace transform of the Mittag-Leffler function, [
14])
. Let and Then, the generalized Laplace transform of one-parameter and two-parameter Mittag-Leffler functions areandrespectively. 3.3. Application of the Generalized Laplace Transform
In
Section 3.2, we established the generalized Laplace transform formulations for both the
derivative and the
-Hilfer fractional derivative. We now apply these results to solve hybrid fractional differential equations that combine
and
-Hilfer fractional derivatives, subject to mixed initial conditions (classical and fractional conditions). The practical utility of the method is illustrated through a detailed example involving a capacitor charging model and a hydraulic door-closer system modified by the introduction of a fractional derivative term.
The following theorem provides the explicit solution to a hybrid fractional Cauchy problem involving the composition of a 1st-order derivative with a -Hilfer fractional derivative. The solution is constructed using multivariate Mittag-Leffler functions and systematically incorporates the given mixed initial conditions.
Theorem 14. Let with , , Suppose is a strictly increasing function with for all and for any Assume further that u and are of ψ-exponential order . Then, for any of ψ-exponential order , the general solution of the hybrid fractional Cauchy problem iswhere is given by Proof. The existence of the generalized Laplace transforms for all terms in (
32) follows from the given assumptions. Since
and is of
-exponential order
, Theorem 4 guarantees that
exists for
. Moreover, the
-exponential order condition on
for
ensures, via Theorem 13, the existence of
. Finally, the continuity and
-exponential order
of
g imply the existence of
. Applying the generalized Laplace transform to both sides of (
32) and invoking linearity with Theorem 2, we obtain
for
. Letting
and
, we have
Solving for
yields
Taking the inverse Laplace transform term by term yields
We now compute each inverse transform separately. For the first term of (
34), using Theorem 6, Theorem 5, Definition 15, and Lemma 4 and taking
,
For the second and third terms, using Lemma 4 and taking
we obtain the following:
Substituting (
35)–(
37) into (
34) yields the general solution:
Finally, applying the initial conditions of
and
yields the desired solution. □
Remark 6. The result established in this paper generalizes Lemma 3.1 of [22]. Specifically, by setting , , , and in Equation (33), we obtainNoting that and applying the Mittag-Leffler function identity, i.e.,with and , the expression simplifies toThis simplified expression is recognized as the general solution ofwhich was originally considered in space . This confirms that our formulation successfully extends the existing result to the more general setting of ψ-Hilfer fractional derivatives. Example 1. In this example, we consider three mathematical models for capacitor charging dynamics. The classical integer-order model serves as a baseline, derived from fundamental circuit theory. To more accurately capture the non-ideal behavior observed in physical systems, we introduce a modified model incorporating a fractional derivative term. This modification is motivated by the need to account for memory effects and distributed relaxation processes inherent in real dielectric materials.
The traditional capacitor charging model is given bywhere represents the electric charge stored on the capacitor’s plates at time t in units of coulombs. For RC circuit charging, the current is defined aswhere is the initial current determined by the source voltage and circuit resistance;
is the circuit time constant, where R represents the total series resistance in the charging path and C is the capacitance value.
The general solution for is given bywhere is the initial charge on the capacitor at time . We propose a generalized modified model through the introduction of a Caputo fractional derivative term:where is the fractional order and is the memory coefficient in units of for dimensional consistency. Using Theorem 14 with , (so that ), , and from (40), we obtain the following:where . By computing using (
41)
and applying the Mittag-Leffler function identity (
39)
for and , we obtain the general solution of (
43)
as follows: Figure 1 presents a comparative analysis of capacitor charging dynamics using two distinct mathematical frameworks. The classical integer-order model (blue solid line), derived from ideal circuit theory, serves as the baseline reference. In contrast, the proposed fractional-order model (green solid line) incorporates memory effects through a Caputo fractional derivative term, providing a more generalized description of capacitor behavior. Both models simulate the capacitor charging process, starting from zero initial charge (). The driving current follows an exponential decay with a time constant of s, which is typical for signal processing circuits, and an initial current of mA, representing moderate excitation levels. For the fractional model, parameters of and (in units of ) were selected to represent weak memory effects. A value of α close to 1 indicates near-ideal capacitor behavior, while the fractional framework maintains the capability to capture non-ideal characteristics observed in real dielectric materials. Figure 2 compares the exact solutions of the fractional differential model for different fractional orders , with parameters of , , , and . The curves are obtained from the general solution given by the fractional integral formulation (
44).
The figure highlights the effect of α on the system dynamics: it can be observed that all curves vary with α and are not strictly increasing. All curves increase during the initial period, then decrease later, where smaller values of α lead to a more pronounced decrease in . In particular, the green curve corresponding to best captures the expected physical behavior of a charging capacitor and is therefore the most suitable choice for modeling in this case. The analytical framework presented in this work also enables model selection through boundary value problems. For instance, if we consider a terminal condition of , the problem becomes a hybrid boundary-value problem. Solving this problem using our exact solution identifies the optimal fractional order that satisfies both the initial and terminal conditions. In this case, the yellow curve corresponding to provides the best fit for the boundary value of , demonstrating how our method can be used for parameter estimation in inverse problems.
Figure 3 illustrates a comparison of the solutions of the fractional differential Equation (
43)
for , , , , and an initial condition of . The solutions are computed using two distinct approaches: the exact solution in the form of (
44)
, which serves as a high-accuracy reference, and a numerical approximation of the Caputo fractional differential equation. For the numerical simulation, the predictor–corrector (PECE) method is implemented in MATLAB R2025a to generate the solution and validate the theoretical results. In the figure, the green solid line represents the exact solution, whereas the red dotted line corresponds to the numerical solution obtained via the Caputo formulation. We propose another generalized modified model for (
40)
through the introduction of a generalized fractional derivative term:where is the fractional order and is the memory coefficient in units of and , as defined in (
41).
Using Theorem 14 and taking , (so that ), , and from (40), we obtain the following: Figure 4 compares three responses: the Caputo fractional solution from (
44)
, the scaled fractional solution from (
46)
(with an additional scaling factor of 1.1), and the classical exponential response . All curves use the same parameters of , , , , and an initial condition of . The fractional solution reflects the influence of memory effects inherent in fractional-order dynamics but shows a noticeable deviation from the classical exponential solution. In contrast, the scaled fractional solution aligns much more closely with the classical curve throughout the observed time period, demonstrating that provides a better approximation of the classical response than while still retaining the essential features of fractional behavior. Ultimately, the most appropriate model should be selected based on its correspondence to actual experimental data. The behavior of the solutions in each model as is analyzed below. For the solution of the fractional model (
44)
, according to the final value theorem and convolution theorem (Theorem 6), we obtainwhere For the second generalized modified model (45) with , a similar calculation yields the following:The solution of the classical model (
42)
is expressed as follows: In the classical case, our analysis shows that the charge on the capacitor increases exponentially and asymptotically approaches a steady-state value (). Once reached, this charge remains constant indefinitely, reflecting an ideal capacitor’s perfect charge retention.
To more accurately represent the behavior of real-world energy storage devices like supercapacitors, we introduced a key modification by incorporating a fractional derivative term into the governing equation. This fractional-order model inherently accounts for the complex, distributed physics within porous electrodes, which integer-order models cannot capture.
The results from our modified model are striking and align perfectly with the theory of non-ideal capacitors:
Instead of reaching a permanent steady state, the charge on the capacitor peaks, then gradually decreases over time.
As time t approaches infinity, the charge decays back to its initial value of .
This behavior is a direct mathematical manifestation of the self-discharge phenomenon. The fractional derivative, characterized by its exponent α, intrinsically models the distribution of relaxation times and the memory effects present in supercapacitors. These effects arise from slow ion redistribution and parasitic reactions within the electrode’s complex pore structure, creating internal leakage paths.
Therefore, the simulation output from our fractional model does not merely show a numerical result; it validates the core physical principle that real electrochemical capacitors cannot maintain their charge indefinitely. The return of the charge to as quantitatively demonstrates the non-ideal, self-discharging nature that fractional calculus is designed to capture.
Example 2. In this example, we analyze the operation of a hydraulic door closer using a mass-damper system model. The door closer’s primary function is to ensure a door closes automatically while preventing it from slamming shut, providing controlled motion through velocity-dependent damping.
The physical system consists of the following:
The door itself, which possesses mass and inertia;
The hydraulic door closer mechanism, providing viscous damping;
The closing torque generated by an internal spring mechanism.
The dynamic behavior of the door’s angular velocity during the closing operation is governed by the rotational form of Newton’s second law:where I represents the moment of inertia of the door about its hinges, characterizing the door’s resistance to angular acceleration;
c is the rotational damping coefficient of the hydraulic door closer, quantifying the viscous torque generated as hydraulic fluid flows through restricted passages within the mechanism;
denotes the angular velocity of the door as a function of time during the closing process;
is the constant torque applied by the internal spring mechanism of the door closer to initiate and maintain closing motion;
is the Heaviside unit step function, mathematically representing the instantaneous release of the door:
The left-hand side of Equation (
47)
represents the system’s internal torques: the inertial term characterizes the door’s resistance to angular acceleration, while the damping term captures the energy dissipation mechanism that prevents slamming. The right-hand side models the external driving input, the sudden application of constant closing torque when the door is released. The analytical solution to Equation (
47)
, with the initial condition of (the door has no initial motion) is given by This mathematical model explains the characteristic closing profile of a well-adjusted door: rapid initial movement followed by a controlled deceleration as the door approaches the latch. The damping coefficient c is crucial in determining the closing speed and final impact force, ensuring reliable latching while preventing damage to the door and frame.
This expression describes the transient angular velocity response of the door. The solution reveals two distinct phases of motion:
- 1.
Initial Transient Phase (): Immediately after release, the exponential term dominates. The velocity increases nearly linearly () as the spring torque works to overcome the door’s inertia.
- 2.
Final Steady-State Phase (): As time increases, the exponential term decays to zero. The angular velocity asymptotically approaches a constant terminal velocity: This steady state represents the dynamic equilibrium where the constant spring torque is perfectly balanced by the velocity-proportional damping torque .
The parameter in the exponent has units of time and defines the characteristic timescale of the system. A larger moment of inertia I slows down the response, while a stronger damper (larger c) accelerates the approach to a steady state. This model successfully captures the essential behavior of a well-designed door closer: a smooth start that prevents jerking, followed by a controlled, constant closing speed that prevents slamming.
To generalize the classical model and capture a broader spectrum of viscoelastic damping behaviors, particularly those exhibiting memory effects and frequency-dependent properties, we reformulate the governing Equation (47) using fractional calculus. The generalized model is expressed as follows:where . In this formulation, the standard viscous damping term is replaced by , where denotes the ψ-Hilfer fractional derivative of order and type . The key physical implication of this generalization is the introduction of a non-local temporal dependence. Whereas the classical viscous damper is a purely instantaneous response, probing only the present velocity, the fractional derivative acts as a temporal probe, integrating the entire history of the velocity for . This memory effect, or heredity, is a hallmark of complex viscoelastic materials, where the damping force at any moment is shaped by the system’s entire deformation history. Under the parameter choices of , , , , and and taking to ensure that first term remains a classical derivative, Theorem 14 yields the solution to the initial value problem (
49)
with and : To illustrate the model with realistic parameters, consider a standard interior door with a mass of 20 kg and a width of 0.81 m. The moment of inertia about its hinges is calculated to be approximately . A typical hydraulic door closer might provide a constant spring torque of and be calibrated with a damping coefficient of to achieve a controlled closing motion. Since the initial value of is arbitrary, we choose for simplicity.
The velocity–time graph in Figure 5 illustrates a critical comparison between the dynamics of a system modeled by (
47)
and those described by (
49).
The primary distinction lies in the system’s long-term behavior. The classical model (dark blue line) given by (
48)
shows the characteristic response of a first-order system: the velocity rapidly increases from zero and asymptotically approaches a fixed steady-state (terminal) velocity of approximately . This saturation indicates that the driving force is perfectly balanced by the linear viscous damping , representing a system with no memory or history dependence beyond the instantaneous velocity. In stark contrast, the fractional model given by (
50)
introduces a long-term memory effect, causing the system’s response to deviate significantly from the classical saturation behavior. For this case, we choose a fractional order of to ensure the damping term exhibits strong non-classical behavior, and we vary the type of the fractional derivative with to study its influence. We observe that with , the solution most closely approximates the behavior of the classical system. Ultimately, the selection of optimal parameters depends on actual experimental values.