 Research Article
 Open Access
 Published:
Parameter identification of twotimescale nonlinear transient models
Advanced Modeling and Simulation in Engineering Sciences volume 2, Article number: 8 (2015)
Abstract
The aim of this paper is to study twotimescale nonlinear transient models and their associated parameter identification. When it is possible to consider two wellseparated time scales, and when the fast component of the applied loading is periodic, a periodic time homogenization scheme, similar to what exists in space homogenization, can be used to derive a homogenized model. A parameter identification process for this latter is then proposed, and applied to an academic example, which allows to show the benefits of such a strategy.
Background
Context
Material fatigue is one of the key phenomena whose study is essential to design and develop modern industrial parts: many of the classical methods used in the industry, such as those described in Ref. [1], allow to estimate fatigue life through cumulative formulas. However, these latter do not take into account the history associated with the loading, and tend to give poor results as soon as the loading frequencies are high and the inertia effects should be considered. One particularly difficult case of study corresponds to combined cycle fatigue (CCF), where two different periodic loads (generally, one being ‘slow’, the other ‘fast’) are involved. In the meantime, more and more complex material laws have been proposed to describe cyclic behaviors, and the ideal way to use these models consists in solving them in the time domain.
As far as the numerical calculation of a timedependent model is concerned, the question of the computational cost can be of utmost relevance, especially when the considered model deals with fast phenomena, which require the use of very small time steps, when compared with the length of the time interval of study. In order to drastically reduce the computational cost, a periodic time homogenization method can be used when two wellseparated time scales exist, and when the components of the applied loading are periodic with respect to the fast time scale [2]. The resulting homogenized model can then be solved with a drastically reduced computational cost, using time steps related to the slow time scale only, whereas the fast time scale is taken into account in an average way in the homogenization scheme.
In order to give accurate predictions, such timehomogenized models have to be compared with experimental data. The key point is to define an identification strategy able to deal with such models and using a process that remains cheap and efficient. The aim of this paper is to analyze on a specific academic example how a relevant identification strategy can be proposed, and to comment on the different choices that can be made throughout the whole identification process. First, the basic elements of the periodic time homogenization method are described, as well as one particular case of study showing the efficiency of the method; then, the general parameter identification strategy that will be adapted to the case of timehomogenized models is introduced. After this introduction, the main part of the paper focuses on a simple academic example allowing to give the theoretical developments associated with a truly twotimescale identification strategy, and to analyze the associated numerical results upon which conclusions can be drawn.
Periodic time homogenization method
Main ingredients
Periodic time homogenization, as it was initially proposed in Ref. [2], can be seen as a transposition to time of the classical periodic space homogenization methods, such as the techniques described in Ref. [3] or in Ref. [4]: it is typically based on the introduction of a twoscale asymptotic expansion for every timedependent variable sought for in the equations of the reference problem:
where \(t\) stands for the socalled slow time scale and \(\tau =t/\xi\) for the fast time scale. Indeed, when the ratio \(\xi\) is very small, it is possible to separate the two time scales by considering that they are independent one from the other, implying that any derivative with respect to time has to use the partial derivatives with respect to the two time scales:
where \({{\mathrm {d}}_t}\bullet\), \({\partial _t}\bullet =\dot{\bullet }\) and \({\partial _\tau }\bullet =\bullet '\) stand for the total time derivative, the partial derivative with respect to the slow time and the partial derivative with respect to the fast time respectively. Moreover, if the applied loading has a component, which is periodic with respect to the fast time \(\tau\), it is possible to assume that each variable is quasiperiodic, meaning that it is periodic with respect to \(\tau.\) Figure 1 gives an illustration of such a quantity. This quasiperiodicity assumption is all the more justified as the ratio \(\xi\) is small, and can then be written as:
where \(T_f\) is the period associated with the fast periodic component of the loading.
Using the asymptotic expansion (1) for each quantity in the reference equations, and balancing the different orders of \(\xi\), the timehomogenized equations are determined by averaging over a fast period the different quantities:
This fasttime average actually allows to separate slowevolving phenomena and fasttime periodic components by using the fact that the quasiperiodicity assumption can actually be rewritten for any timedependent variable as:
In addition to the different timehomogenized quantities \(\langle \alpha \rangle\), the residuals associated with this fasttime average are then denoted as \(\alpha ^*=\alpha \,\langle \alpha \rangle\), and depend on both time scales \(t\) and \(\tau\) a priori. They usually have to verify a very simple problem (typically a linear, elastic one), which will be solved for a time interval corresponding to one fast period \(T_f\). Eventually, the obtained timehomogenized equations are solved relatively to the slow time scale only, by introducing the averaged influence of the fast cycles corresponding to the solution of the fast problem at each slow time step, hence allowing to solve for all the zerothorder timehomogenized variables with a drastic reduction in the computational cost when compared with what would be required to solve the reference problem (for example by using the classical rule of thumb of 20 time steps per fast cycle).
Cases of study
References on the time homogenization method still tend to be quite scarce, as showed a recent tentative review in Ref. [5]. Nevertheless, the possible applications are numerous, as we can see with a significant number of references dealing with topics as diverse as the behavior of viscoelastic materials in Ref. [6], the vibration of preloaded beams in Ref. [7], the ultrasonic welding of composites in Ref. [8], the ultrasonic imaging of tissues in Ref. [9], the cyclic loading of normally consolidated clay in Ref. [10] or the viscoelasticviscoplastic behavior of polymers in Ref. [11].
Recently, we focused on validating the method for different cases of simulations of structures withstanding fatigue loads with two periodic components:

material fatigue with a viscoplastic law defining two hardening variables in Ref. [12];

material fatigue with an isotropic damage law in Ref. [13];

extension of the method to three different time scales in Refs. [5] and [14];

extension of the method to the case of a resonant excitation in Ref. [5].
In this latter case, a real case example, coming from the European Project PREMECCY on fatigue, related to gas turbine blades, and briefly described in Ref. [15], was presented: indeed, these latter, during operation, withstand a loading with two periodic components whose time evolution is sketched in Figure 2. The PREMECCY project’s principle was to experimentally study CCF with several bladeshaped specimens, such as the one depicted in Figure 3. Numerical simulations using the periodic time homogenization method have been run on such a geometry, with a loading implying frequencies \(F=0.14\ {\text {Hz}}\) and \(F/\xi =1{,}400\ {\text {Hz}}\), this latter value corresponding to the specimen’s second bending eigenfrequency. A specific result is shown in Figure 3, where it is easily seen that the highest values of the longitudinal plastic strain are located at the specimen’s edges, and that these values nearly vanish everywhere else. More details can be found in Ref. [5].
Gradientbased parameter identification using adjoint state formulations
To improve the quality of the model’s predictions, especially the fatigue life estimate, it is mandatory to compare computed quantities with experimental data and to define an identification scheme allowing to update the model’s parameters judiciously. In this section, a general description of the proposed identification strategy is given, which, as we will see, is relevant for both reference and timehomogenized problems: it will thus be illustrated in this latter case in the “Methods”.
The forward problem is considered as an implicit formulation with a vector function \({\mathcal F}\) over a time interval \([0,T]\) :
where \({{\mathrm {d}}_t}\) and \({\mathrm {d}^2_t}\) are the first and secondorder time derivatives respectively. \({\mathbf {U}}_0\) and \({\mathbf {V}}_0\) stand for the initial conditions of the dynamic problem. Whereas \({\mathbf u}\) is the state vector of size \(N\), composed of all the timedependent degrees of freedom (DOFs) describing the studied problem, \({\mathbf p}\) stands for the vector containing the \(P\) scalar parameters associated with the differential equation (6).
Formulation of the identification problem
The identification problem consists in finding the parameter vector \({\mathbf p}_{opt}\) such that the solution \({\mathbf u}(t;{\mathbf p}_{opt})\) of (6) obtained with the parameters \({\mathbf p}_{opt}\) is as close to the available experimental data as possible. These latter are indeed compared with the corresponding quantities \({\mathbf A}{\mathbf u}(t;{\mathbf p})\), where \({\mathbf A}\) is a projection operator allowing to select, for each quantity, the closest DOF to the experimental measurement point. In order to use consistent notations, the corresponding experimental quantity is denoted \({\mathbf A}{{\mathbf u}}_{exp}(t)\); however, it does not mean that such a vector \({{\mathbf u}}_{exp}(t)\) actually exists.
The following misfit function is then introduced: it consists of a norm measuring the discrepancy between the quantities predicted with the forward model (6) and experimental data:
where \({\mathbf u}(t;{\mathbf p})\) satisfies Eq. (6). The \(L^2\)norm proposed here is completed with a Tikhonov regularization term, allowing to deal with the illposedness of the identification problem, by bounding the magnitude of the parameter vector \({\mathbf p}\) to be identified: this regularization term uses a vector \({\mathbf p}_0\) containing nominal values corresponding to a priori experience, and a diagonal weighing matrix \({\mathbf R}\). Eventually, the solution of the identification problem can be sought as the parameter vector \({\mathbf p}_{opt}\) minimizing the misfit function \({\mathcal J}({\mathbf p})\):
Adjoint state formulation
The determination of this minimum is achieved using gradientbased minimization methods, therefore the question of avoiding local minima by means of an appropriate regularization process should be carefully addressed. In some cases, rather than using the classical Tikhonov regularization term, the a priori experience can be introduced in some specific ways, as in Ref. [16]. Similarly, the fact of using a homogenized model in the parameter identification process can introduce a regularizing effect, just as explained in Ref. [17]. However, we will not address here this specific question, but rather focus on the identification process itself.
To estimate the gradient of the misfit function, we solve here an adjoint state problem. A typical example in mechanical engineering is given in Ref. [18], where the parameters of an elastoplastic material law are identified with indentation tests. In the strategy proposed here, the generic form of the adjoint state problem, which can be obtained by expressing the stationarity of a Lagrangian as in Ref. [19], is as follows:
where \(\nabla _{\mathbf u}{\mathcal F}\), \(\nabla _{{{\mathrm {d}}_t}{\mathbf u}}{\mathcal F}\) and \(\nabla _{{{\mathrm {d}}^2_t}{\mathbf u}}{\mathcal F}\) stand for the directional derivatives of \({\mathcal F}\) with respect to \({\mathbf u}\), \({{\mathrm {d}}_t}{\mathbf u}\) and \({{\mathrm {d}}^2_t}{\mathbf u}\) respectively. The adjoint state problem is then a timebackward differential equation with two final conditions, and where the firstorder sensitivities of the forward problem are concerned.
Once the adjoint state problem (9) is solved, it can be shown that the misfit function’s gradient with respect to the parameter vector \({\mathbf p}\) can be expressed as:
This specific way of estimating the misfit function’s gradient can be compared with a classical finite difference formula, such as the central finite difference scheme: in this latter case, when the parameter vector is of size \(P\), the gradient calculation is obtained by evaluating the misfit function in \(2P\) additional ‘points’, each couple of points corresponding to two symmetrical perturbations of the misfit function associated with each parameter in the vector \({\mathbf p}\). The resulting computational cost for each gradient evaluation consists of \(2P\) solutions of the forward problem (6) and \(2P\) time integral evaluations. By contrast, when the adjoint state solution is used, only two differential equation solutions are required: one for the forward problem (6) and one for the adjoint problem (9). The associated computational cost for each gradient evaluation is then two differential equation solutions, and \(P\) time integral evaluations. The resulting gain is as high as the number of parameters to be identified. Moreover, it is easier to control the accuracy of the gradient estimate with the adjoint state method than with finite difference formulas, for which the choice of the discretization steps has a strong influence on the final estimate.
Methods
We propose to study an academic example in order to discuss the different steps of the parameter identification problem associated with a timehomogenized model. To the best of our knowledge, it is the first time that such a twotimescale identification strategy using an adjoint state formulation is proposed: only one alternative has been briefly studied in Ref. [20] with the use of a genetic method. First, we will recall how the identification process is formulated on a classical reference problem, i.e. with no time homogenization. Then, we will apply the identification strategy on the timehomogenized version of the problem, and see what connections exist with the identification process associated with the reference problem.
The case of study detailed here consists of a straight bar of length \(L\), clamped at one end, and withstanding at the other end a normal force with two periodic components. The measured displacement at this end is then used to determine the parameter values of the material elastic viscoplastic law.
Reference problem
Forward problem
The dynamic equilibrium of the bar is a scalar equation, with the normal (nonviscous) stress \(\sigma (x,t)\) and the longitudinal displacement \(u(x,t)\) defined at each point \(x\in [0,L]\) and for any \(t\in [0,T]\):
where \(\partial _x\) is the partial space derivative, \(\rho\) is the mass density, and \(c_K\) is the damping ratio where damping is assumed as proportional to stiffness. Homogeneous initial conditions for the displacement and the velocity are assumed. No load is applied along the bar, and, whereas this latter is clamped at \(x=0\), a surface force \(f_{\mathrm {s}}(t)\) is applied at \(x=L\) for any \(t\in [0,T]\):
This surface force is biperiodic, with the ratio of the associated low frequency over the fast one equal to \(10^{4}\).
The constitutive relation links the normal (nonviscous) stress to the longitudinal strain along the bar for any \((x,t)\in [0,L]\times [0,T]\):
where \(E\) is the Young’s modulus, and \(\varepsilon ^{\mathrm {p}}(x,t)\) stands for the longitudinal plastic strain. One assumes that this latter verifies a Norton’s viscoplastic evolution law for any \((x,t)\in [0,L]\times [0,T]\), which depends on the nonviscous stress only:
with \(\varepsilon ^{\mathrm {p}}\,_{t=0}=0\ \forall x\in [0,L]\). \(K\) and \(n\) are the two constant parameters to be identified.
The numerical implementation consists of quadratic 1D finite elements under MATLAB for the spatial discretization of the displacement, whereas the longitudinal plastic strain is linearly interpolated using the ‘external’ nodes of the different elements. The time integration uses the ‘ode45’ procedure, based on a fourthorder embedded Runge–Kutta formula according to Ref. [21]: depending on the number of finite elements used, the time step to be chosen can verify the classical rule of thumb of 20 time steps per fast cycle, or must be smaller than the maximal time step associated with the Courant–Friedrichs–Lewy condition, making in both cases the computational cost become unaffordable as soon as a high number of cycles is calculated.
Parameter identification process
It is assumed that experimental data consist of the knowledge of the displacement \(u_{exp}(t)\) measured at \(x=L\). The proposed misfit function is then:
where \(u(x,t;K,n)\) is the solution of the forward problem (11)–(15) with parameter values \(K\) and \(n\).
In order to derive the adjoint state problem, the following Lagrangian is introduced:
where \((u,\varepsilon ^{\mathrm {p}},K,n,z,\lambda )\) are considered as independent quantities. The minimization of the misfit function (16) under the constraints (11)–(15) is then equivalent to express the stationarity of the Lagrangian (17) with no constraint [19].
The adjoint states \(z(x,t)\) and \(\lambda (x,t)\) are Lagrange multipliers verifying, for any \((x,t)\in (0,L)\times [0,T]\), equations coming from the stationarity of the Lagrangian with respect to \(u\):
and \(\varepsilon ^{\mathrm {p}}\):
Formally, the latter equations correspond to a dynamic elasto–viscoplastic problem (with negative damping), when one considers the first Lagrange multiplier \(z\) as a kind of displacement (even if it is not homogeneous to a length) and the second Lagrange multiplier \(\lambda\) as a kind of plastic strain (even it is not dimensionless). The discrepancy between the forward model’s predictions and experimental data is directly imposed as a Neumann boundary condition at \(x=L\), i.e. where the measurements are available. One should notice that this problem is timebackward with final conditions (at \(t=T\)): once the adjoint state problem has been properly discretized with respect to space, the obtained time differential equations can be solved with classical time integration methods by applying a change in variables \(\theta =Tt\) in order to deal with initial conditions rather than final ones.
Once solved, the adjoint state solution is used to evaluate the misfit function’s gradient, whose components are equal to the partial derivatives of the Lagrangian (17) with respect to the parameters:
One should notice that, beside the forward state solutions, only the second Lagrange multiplier \(\lambda\) appears in the previous expressions: this is logical here, because one is interested in the parameters \((K,n)\) of the evolution law (15) only. The first Lagrange multiplier \(z\) would have appeared in these expressions if one had tried to identify parameters associated with the dynamic equilibrium equation, such as the Young’s modulus \(E\) or the damping ratio \(c_K\).
These estimates of the misfit function’s gradient components are used by the minimization algorithm to determine the best search direction and step size. However, even if the strategy using the adjoint state problem is the cheapest one in terms of computational cost, this latter can be awfully prohibitive if a high number of fast cycles has to calculated, as it is the case here, since viscoplasticity is a slowevolving phenomenon, implying that the change in the longitudinal viscoplastic strain is very small for each individual fast cycle.
Timehomogenized problem
In order to obtain a drastic reduction in the computational cost, the periodic time homogenization method is applied. Following the different steps briefly described in the “Background”, the timehomogenized equations to be solved are obtained.
Dynamic equilibrium equation
The first equation to be dealt with is the dynamic equilibrium equation, which can be rewritten in a normalized way in order to compare the orders of magnitude of the different terms:
where \(F\) is the frequency of the slow component of the loading, and the different normalized quantities are denoted as \(\hat{\bullet }\). Different cases arise, depending on the orders of magnitude of \(\rho L^2 F^2/E\) and \(Fc_K\). Here, we assume that:
where \(\beta \le O(1)\) and \(\gamma \le O(1)\). Physically, the first assumption (26) is equivalent to having \(L/\lambda =\sqrt{\beta }\), where \(\lambda\) is the wavelength associated with the fast loading frequency: this means that the order of magnitude of \(\lambda\) should be at least equal to the length of the bar.
Using these two assumptions and introducing an asymptotic expansion of the displacement \(u=u_0+\xi u_1+\xi ^2u_2+O(\xi ^3)\) allows to express the acceleration as follows:
It can then be shown [5] that the two time scales are separable and that the homogenization method can be applied: the zerothorder dynamic equilibrium equation becomes:
and the associated zerothorder boundary condition at \(x=L\) reads:
Eventually, by taking the fasttime average (4) of the two previous equations, and by using the quasiperiodicity assumption (5), one can obtain the timehomogenized zerothorder dynamic equilibrium equation:
for any \((x,t)\in (0,L)\times [0,T]\), and the timehomogenized zerothorder boundary condition:
which corresponds to the solution of a quasistatic problem.
Evolution law
In the same way, the evolution law (15) gives, up to order zero:
First, the \(1/\xi\)order has to be considered, giving: \(\varepsilon ^{\mathrm {p}}_0\,'= 0\). This is equivalent to say that the zerothorder plastic strain only depends on the slow time variable:
The physical interpretation of this latter result is that viscoplasticity is a slowevolving phenomenon, even if the material withstands a highfrequency loading. This slow variation is described by the zerothorder evolution law, which can be expressed as:
In order to make the contribution of the firstorder plastic strain disappear from this relation, the fasttime average (4) of this latter is evaluated. Since, on the one hand, Eq. (34) is equivalent to say that \(\langle \dot{\varepsilon }_0^{\mathrm {p}} \rangle \, =\dot{\varepsilon }_0^{\mathrm {p}}\), and, on the other hand, the quasiperiodicity assumption (5) implies that \(\langle \varepsilon ^{\mathrm {p}}_1\,'\rangle \,=0\), this average eventually gives that, for any \((x,t)\in [0,L]\times [0,T]\):
with a zero initial condition.
Zerothorder timehomogenized problem
The final system consists of the following equations to be solved at the slow time scale only, for any \((x,t)\in (0,L)\times [0,T]\):
The resulting system then corresponds to a quasistatic, elastic viscoplastic problem, where the loading consists of the slow component of the surface force.
Of course, the fast component does have an effect on these homogenized equations, by means of the evolution law (41): since this latter is nonlinear, it is required to calculate the zerothorder ‘instantaneous’ stress field \(\sigma _0\) instead of directly using the corresponding homogenized quantity \(\langle \sigma _0\rangle\). To do this, it is mandatory to estimate the residual quantity \(\sigma _0^*(x,t,\tau )\), defined according to the following relation:
This residual is then the solution of the following system of residual equations:
obtained by subtracting the equations of the timehomogenized problem (37)–(40) to the zerothorder equations of the reference problem. This residual system consists in solving, for the fast time scale \(\tau\), and at each slow time step \(t_k\), a dynamic problem for a purely viscoelastic material, with a surface force \(f_{\mathrm {s}}^*=f_{\mathrm {s}}\ \langle f_{\mathrm {s}}\rangle\). Since viscoplasticity (which is the only source of nonlinearity here) appears in the zerothorder timehomogenized equations only, this residual problem is linear, and can be conveniently solved in the frequency domain. If needed, additional orders could be addressed, as detailed in Ref. [5].
The evaluation of the fasttime average appearing in (41) can be achieved by means of a numerical integration formula, such as the trapezoidal rule with \(N+1\) points, defined as:
This choice is not only related to its formal simplicity, but can actually be justified using the conclusions presented in Ref. [22]: the author shows that the trapezoidal rule converges extremely fast when integrating smooth periodic functions, as it is the case here, and that no substantial additional gain would be obtained with the use of more elaborated formulas, such as Simpson’s for example.
Associated parameter identification strategy
The identification strategy using adjoint state formulations described in the “Background” is applied now on the timehomogenized problem. First, a consistent misfit function has to be selected, then the adjoint state problem required to estimate the misfit function’s gradient is introduced.
Choice of the misfit function
The first step consists in describing, through the misfit function, the discrepancy between the timehomogenized model’s predictions and experimental data: indeed, on the one hand, the model has been solved on slow time steps only, whereas, on the other hand, the experimental data can be available on a much finer scale.
The most efficient choice in terms of computation cost is to use timehomogenized quantities in the misfit function (16), since it allows to address the time integral on slow time steps only:
Whereas \(\langle u_0\rangle (L,t;E,K,n)\) is the solution of the zerothorder timehomogenized forward problem (37)–(41), \(\langle u_{exp}\rangle (t)\) stands for the corresponding experimental quantity, which is obtained by fastaveraging the experimental data for each slow time step \(t_k\) of the timehomogenized displacement:
Adjoint state problem
The misfit function’s gradient is evaluated in the same way as in the section associated with the reference problem, using the solution of an adjoint state problem. Starting from the Lagrangian associated with the misfit function (48) and the timehomogenized forward problem (37)–(41), it is first obtained that the second Lagrange multiplier depends on the slow time scale only:
because it is associated with the zerothorder timehomogenized evolution law (41), which depends on \(t\) only. On the contrary, the first Lagrange multiplier is decomposed as \(\langle z_0\rangle (x,t)+z_0^*(x,t,\tau )\) to deal with the two systems (37)–(40) and (43)–(46) respectively.
Then, the two following PDEs corresponding to the adjoint state problem are obtained, for any \((x,t)\in (0,L)\times [0,T]\):
and they actually correspond to the zerothorder timehomogenized versions of Eqs. (18)–(22) giving the adjoint state solutions associated with the identification process for the reference problem. Figures 4 and 5 show the comparison between the two corresponding adjoint state solutions \(\lambda\) and \(\lambda _0\) for the identification process detailed in the “Results and discussion”: the adjoint state solution \(\lambda _0\) corresponding to the timehomogenized problem is homogeneous along the bar, and is very close to the (homogeneous) fasttime average of the (heterogeneous) adjoint state solution \(\lambda\) associated with the reference problem.
Indeed, in this case, the previous equations can be decoupled in order to get the PDE verified by the second Lagrange multiplier \(\lambda _0\) for any \((x,t)\in [0,L]\times [0,T]\):
finally implying that, for any \((x,t)\in [0,L]\times [0,T]\):
which is a timebackward ODE with a final condition equal to zero. This equation can be solved using the slow time steps \(t_k\) only, which allows to derive the solution in a way as efficient as for the timehomogenized forward solution.
Misfit function’s gradient
The misfit function’s gradient then consists of the two following partial derivatives:
Once again, these relations correspond to the zerothorder timehomogenized estimates of the two misfit function’s gradient components (23)–(24) obtained for the identification problem associated with the reference problem. Actually, this is a result that already occurs in periodic space homogenization, as shown for example in Ref. [23].
Results and discussion
Validation of the time homogenization method
The studied bar, which is \(1\ {\text {m}}\) long, is discretized in ten quadratic finite elements. The surface force at \(x=L\) is a combined cycle load, defined as the sum of two sines of frequencies \(F\) and \(F/\xi\). For this case of study, the assumption (26) is verified, since the two loading frequencies are \(F=0.05\ {\text {Hz}}\) and \(F/\xi =500\ {\text {Hz}}\) (cyclic loads with respective amplitudes \(1\ {\text {kN}}\) and \(0.25\ {\text {kN}}\)) and the material considered is a typical steel (\(E=200\ {\text {GPa}}\) and \(\rho =7,800\ {\text {kg}}\, {\text {m}}^{3}\)). Moreover, the damping ratio \(c_K\) is equal to \(10^{5}\), hence verifying assumption (27).
The reference calculations consist in solving the Eqs. (11)–(15) of the reference problem for a time interval corresponding to the first slow loading period \([0,20]\mathrm {s}\): the time step is chosen according to the classical rule of thumb of 20 time steps per fast loading period, i.e. \(10^{4}\ {\text {s}}\). MATLAB’s ‘ode45’ procedure is used.
The zerothorder timehomogenized equations (37)−(41) are solved with the same algorithm, but the chosen time step is \(0.01\ {\mathrm {s}}\), which reduces by 100 the number of iterations required for the calculation. Since the loading is the sum of two periodic components (one slow and one fast), the residual problem, consisting in Eqs. (43)–(46), depends on the fast time scale only, and can be solved in the frequency domain once and for all.
The associated results are compared in Figure 6 presenting how evolves, for the first slow period, the zerothorder longitudinal plastic strain at \(x=0\) and \(x=L\): the estimates are in excellent agreement, as confirmed by Table 1, and tend to indicate that the predictions can remain good even if a high number of slow cycles is applied. Figure 7 shows the previous time evolutions (at \(x=0\)) between two close time steps, and allows us to see that the zerothorder longitudinal plastic strain evolves smoothly, while the reference strain increases step by step for each fast loading period. All these results allow to validate with this typical example the periodic time homogenization method, and to get an estimate of the computational gain it allows through the drastic reduction in the number of required time steps for the numerical simulation. This number is indeed the main criterion concerning the computational cost of the time homogenization method, since, when compared with the reference problem, the only additional calculations consist in solving the fast residual system (once and for all) and in estimating the fasttime averages with the trapezoidal rule (47).
Parameter identification
In order to evaluate how the misfit function performs, synthetic data \(u_{exp}(t)\) are created by solving the Eqs. (11)–(15) of the reference forward problem, using \(K_{exp}=100 \times 10^6\ {\text {SI Units}}\) and \(n_{exp}=10\) as parameter values. As previously, the bar withstands a combined cycle loading with \(F=0.05\ {\mathrm {Hz}}\) and \(F/\xi =500\ \mathrm {Hz}\). \(K_0=50 \times 10^6\ \text {SI Units}\) and \(n_0=5\) are chosen as initial parameter values for the identification process, which is based on an interiorreflective Newton method [24] with a BFGS formula in order to minimize the misfit function \({\mathcal J}_0\). Since we do not want to address specifically the question of regularization, no regularization term is added to the misfit function (48). Eventually, given the fact that the two parameters have very different orders of magnitude, a normalization is introduced, using the values of \(K_0\) and \(n_0\), in order to prevent numerical inaccuracies in the gradient estimate. The logarithm of the misfit function is depicted in Figure 8, showing a welldefined global minimum despite a crescentshaped valley.
Subsequent results are listed in Table 2: the parameter values identified are very close to the ones used to create the synthetic data. Figure 9 shows the comparison between the identified model and the synthetic reference, more precisely the variations of the longitudinal plastic strain, which is not directly observable.
In addition, to demonstrate the robustness of the process, centered Gaussian noise is added to the synthetic data used in the identification strategy: different levels (corresponding to a standard deviation proportional to the mean of the measured displacement) are proposed, and the associated identification results are listed in Table 2. They show that the identification works, even for high levels of noise: the choice of the misfit function (48) with the application of the fasttime average to the noisy data has allowed to filter significantly the introduced noise, hence strongly limiting its impact on the identification results. This is a particularly interesting property, whereas identifying using the reference model and the whole set of noisy data undoubtedly would have led to poor results: indeed, as said above, the fact of using a homogenized model in a parameter identification process generally introduces a regularizing effect, as explained in Ref. [17]; unfortunately, even in this academic case of study, the numerical cost for solving the reference identification problem is too prohibitive to rigorously check this property.
Eventually, the computational cost associated with the identification process is significantly reduced when compared with what is obtained when the inverse problem related to the reference problem (11)–(15) is considered, which allows to address the case of slowevolving phenomena, requiring the simulation over a large time interval.
Conclusions
Here we have proposed a first preliminary study of a twotimescale parameter identification process, using timehomogenized models: the adaptation of a classical identification strategy based on an adjoint state formulation to estimate the misfit function’s gradient can be used in this specific framework: this leads, on the proposed example, to the determination of the timehomogenized counterpart of the adjoint solution associated with the reference identification problem. Despite its simplicity, the academical example studied here showed the relevance of the strategy and its reduced computational cost: these results can be viewed as a first step before dealing with more complex cases of study.
To come back more specifically to CCF life prediction, a possible prospect could be to use the experimental information available (with classical high cycle fatigue tests for instance) to tune correctly the parameters of the considered material laws. In addition, to further improve the reduction in the computational cost, the extension to models with three different time scales, such as those described in Ref. [5], should be straightforward as well.
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Authors’ contributions
DA started the project and the initial coding. GP developed the current version of the code, analysed the data and wrote the manuscript. Both collaborated to develop the analyses and expertise on the different aspects of the model. Both authors read and approved the final manuscript.
Acknowledgements
The authors would like to acknowledge the support of the European Commission through the project PREMECCY, described in [15], which allowed to initiate this study.
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Competing interests The authors declare that they have no competing interests.
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Keywords
 Inverse problem
 Identification
 Adjoint state
 Time multiscale
 Periodic homogenization