A system and method for the computational design of the transient dynamics of a new photovoltaic device

Abstract

The application discloses a kind of novel photovoltaic device transient dynamics calculation design system and method, system includes: user parameter input module is used to receive and manage the input parameter required by simulation;Device structure construction module is used to construct the layered structure model of target device based on input parameter, and define the space interval, physical property and interface boundary condition of each layer;Physical model selection and coupling module is used to construct corresponding coupled partial differential equation set according to the device type of target device;Space grid division module is used to construct the space discrete grid of layered structure model, and the calculation precision of interface and thin layer area is enhanced using non-uniform grid division mode;Core numerical solution module is used to solve coupled partial differential equation set, and obtain simulation result;Data export module is used to output simulation result as general data format.

Description

Technical Field

[0001] This invention belongs to the field of semiconductor solar cell and process computer-aided design technology, specifically relating to a novel computational design system and method for transient dynamics of photovoltaic devices. Background Technology

[0002] As the global energy structure shifts towards low-carbon energy, novel photovoltaic devices, represented by perovskite solar cells and organic solar cells, are becoming an important development direction for next-generation solar cells due to their advantages such as high efficiency, low cost, flexible fabrication, and solution processing. Compared with traditional silicon-based photovoltaic devices, the performance of novel photovoltaic devices depends not only on the carrier transport capacity under steady state, but also on the complex transient dynamic processes inside the device, including carrier generation, recombination, migration, and extraction, as well as interface charge accumulation, exciton diffusion dissociation, and ion migration.

[0003] For perovskite solar cells, there are generally migratable ions inside. These ions migrate and redistribute under the influence of applied bias voltage and light, which can cause interfacial electric field reconstruction, hysteresis, and changes in device stability. For organic solar cells, photoelectric conversion first involves exciton generation, diffusion, and dissociation to form charge transfer states, and then further forms free charge carriers that are transported to the electrodes. Therefore, the traditional drift-diffusion model alone cannot accurately describe the entire physical mechanism.

[0004] While existing experimental characterization methods can obtain some macroscopic response characteristics, such as JV curves, impedance spectra, and transient photovoltage / photocurrent, they are difficult to directly analyze the spatial distribution of microscopic physical quantities inside the device and the coupling evolution process over time. Moreover, the experimental cost is high and the repeatability of parameters is limited. Existing commercial simulation software is mostly geared towards traditional inorganic semiconductor devices or steady-state analysis scenarios. When dealing with the strong nonlinearity, strong coupling, and strong transient processes in novel photovoltaic devices, there are the following shortcomings: (1) There is a lack of a complete model suitable for the self-consistent coupling of ion migration and electron / hole transport in perovskite devices; (2) There is a lack of a complete framework suitable for solving the exciton dynamics, CT state formation, and drift-diffusion joint solution of organic solar cells; (3) The commonly used time-progression methods are mostly low-order implicit algorithms or traditional Crank-Nicolson schemes, which are prone to oscillation, distortion, or low computational efficiency when dealing with problems with strong rigidity and the coexistence of fast and slow transient time scales; (4) There is a lack of a dedicated computational design platform that can simultaneously output microscopic and mesoscopic distributions, macroscopic performance indicators, and transient calculation results.

[0005] Therefore, there is an urgent need in this field for a transient dynamics computational design system and method for novel photovoltaic devices, in order to achieve high-precision, multivariable, and self-consistent simulation analysis based on first-principles physics, and to provide theoretical support and digital tools for device material selection, structural optimization and process design. Summary of the Invention

[0006] This invention aims to address the shortcomings of existing technologies and provides the following solutions: A novel computational design system for transient dynamics of photovoltaic devices includes: a user parameter input module, a device structure construction module, a physical model selection and coupling module, a spatial mesh generation module, a core numerical solution module, and a data export module. The user parameter input module is used to receive and manage the input parameters required for the simulation; The device structure construction module is used to construct a layered structure model of the target device based on the input parameters, and to define the spatial range, physical properties and interface boundary conditions of each layer. The physical model selection and coupling module is used to construct a corresponding set of coupled partial differential equations based on the device type of the target device; The spatial mesh generation module is used to construct the spatial discrete mesh of the layered structure model and to enhance the calculation accuracy of the interface and thin-layer regions by using a non-uniform mesh generation method. The core numerical solution module is used to solve the coupled partial differential equations and obtain simulation results; The data export module is used to output the simulation results in a general data format.

[0007] Preferably, the input parameters include: device geometric parameters, material physical parameters, dynamic parameters, and simulation condition parameters.

[0008] Preferably, the workflow of the physical model selection and coupling module includes: Building a general model: in, Represents the vector differential operator. Represents electrostatic potential, Indicates the amount of electron charge. Represents the dielectric constant. Indicates hole concentration. Indicates electron concentration. and Indicates doping concentration. Indicates time, and Represents current. Indicates the carrier generation rate. Indicates the carrier recombination rate. and Indicates mobility. and Indicates the quasi-Fermi level. Indicates intrinsic carrier concentration. and Indicates composite lifetime. This represents the effective hole concentration when the trap is in equilibrium. This indicates the effective electron concentration when the trap is in equilibrium. Based on the general model, an ion continuity equation is added to construct an ion migration model for perovskite devices. The ion continuity equation includes: in, Indicates ion concentration. Indicates thermal voltage. Indicates the ion diffusion coefficient; Based on the general model, exciton dynamics equations are coupled to construct an exciton dynamics model for organic devices: in, Represents a locally excited state. This represents the constant rate at which locally excited excitons form charge-transfer excitons. Represents the charge transfer state. Indicates the exciton transfer rate in the CT state. This represents the recombination rate of LE-state excitons. This represents the equilibrium LE exciton concentration. This represents the CT-state exciton dissociation rate. This represents the constant rate at which free charge carriers form charge transfer states. This represents the equilibrium CT exciton concentration.

[0009] Preferably, the workflow of the spatial grid partitioning module includes: achieving adaptive spatial grid distribution through a hyperbolic tangent mapping function, and partitioning the grid point distribution for each layer using the following formula: in, This represents any spatial location within any material computational domain. Represents the endpoint of the computational domain for any material. Indicates the starting point of the computational domain for any material. Indicates spatial location index, This represents the number of grid points in any material computational domain. Indicates the grid encryption parameters. Represents the computational domain of any material.

[0010] Preferably, the workflow of the core numerical solution module includes: The finite volume method is used to spatially discretize the governing equations, preserving the conservation properties of conserved variables such as electrons, holes, and ions on the discrete control volume. The mass matrix and Jacobian matrix are constructed from the system of equations obtained by spatial discretization using the finite volume method. The transient problem is solved using an implicit Radau IIA family of high-order time-progression methods; An adaptive time step adjustment method based on local truncation error and Newton's iterative convergence constraint is adopted to adjust the time step. The initial transient obtained from solving the transient problem is eliminated under the built-in potential condition, and then pre-bias preprocessing is performed. Then, the first direction voltage scan is performed and the result is recorded. Then, the reverse direction scan is performed with the end point of the first direction scan as the initial value to obtain the complete transient JV hysteresis characteristics.

[0011] This invention also provides a novel computational design method for the transient dynamics of photovoltaic devices, which is applied to the aforementioned system and includes: Receive and manage the input parameters required for the simulation; Based on the input parameters, a layered structure model of the target device is constructed, and the spatial range, physical properties and interface boundary conditions of each layer are defined. Construct a corresponding set of coupled partial differential equations based on the device type of the target device; A spatial discrete mesh is constructed for the layered structure model, and a non-uniform meshing method is used to enhance the computational accuracy of the interface and thin-layer regions. Solve the coupled partial differential equations to obtain simulation results; The simulation results are output in a general data format.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention can simultaneously accommodate the ion migration process in perovskite devices and the exciton dynamics process in organic solar cells, overcoming the problem that traditional simulation software can only handle a single transport mechanism.

[0013] (2) This invention employs a high-order implicit Radau IIA time-progression algorithm, which can effectively reduce numerical oscillations and improve solution accuracy when dealing with rigid transient problems compared to low-order algorithms. The manual compares the differences between the Crank-Nicolson scheme and the Radau IIA scheme in the problem of electron concentration increasing over time. The former exhibits significant oscillations, while the latter has better numerical stability.

[0014] (3) The present invention combines the mass matrix, Jacobi sparse matrix and adaptive time step control mechanism to improve the convergence of solving large-scale coupled transient problems.

[0015] (4) This invention can output not only macroscopic JV curves, efficiency parameters and transient response, but also the spatial distribution and evolution process of microscopic physical quantities inside the device, which is convenient for revealing the mesoscopic mechanism behind the device performance.

[0016] (5) The present invention can predict the performance change trend of the device by adjusting the material parameters, layer thickness, interface parameters and scanning conditions, and provide a calculation basis for the design optimization of new photovoltaic devices and the explanation of anomalous phenomena. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the system structure according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the device geometry according to an embodiment of the present invention; Figure 3 This is a schematic diagram of mesh division according to an embodiment of the present invention; Figure 4 This is a schematic diagram of spatial second-order convergence according to an embodiment of the present invention; Figure 5 This invention achieves fifth-order time convergence in its embodiment. Figure 6 This is a schematic diagram of the transient JV hysteresis characteristic curve of an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1 In this embodiment, as Figure 1 As shown, a novel computational design system for transient dynamics of photovoltaic devices includes: a user parameter input module, a device structure construction module, a physical model selection and coupling module, a spatial mesh generation module, a core numerical solution module, and a data export module.

[0022] The user parameter input module is used to receive and manage the input parameters required for simulation.

[0023] Input parameters include: device geometry parameters, material physics parameters, dynamic parameters, and simulation condition parameters.

[0024] In this embodiment: (1) Device geometric parameters This is used to define the thickness and arrangement order of each functional layer, including the Inductively Transparent Electrode (ITO), Electron Transport Layer (ETL), Active Layer, Hole Transport Layer (HTL), and Electrode. For perovskite solar cells, an ITO / ETL / perovskite / HTL / metal electrode structure can be defined; for organic solar cells, an ITO / electron transport layer / active layer / hole transport layer / metal electrode structure can be defined. Figure 2 As shown.

[0025] (2) Material physical parameters Including the dielectric constant of each layer diffusion coefficient and migration rate and Effective density of states and Edge position and Defect state concentration Composite lifespan and Doping concentration and Interfacial recombination rate and Parameters and various physical constants such as the Boltzmann constant .

[0026] (3) Dynamic parameters For perovskite devices, the input ion vacancy concentration Ion diffusion coefficient ,activation energy And whether ion migration is enabled; for organic devices, input local excited state (LE) and charge transfer state (CT) related parameters, such as exciton diffusion and dissociation rates. recombination rate CT / LE energy level difference wait.

[0027] (4) Simulation condition parameters This includes settings for illumination conditions, absorption model, initial scan voltage, termination voltage, scan rate, total simulation duration, initial conditions, time integration tolerance, and mesh density. The parameter class supports settings for scan voltage, scan rate, and tolerance.

[0028] The device structure construction module is used to build a layered structure model of the target device based on the input parameters, and to define the spatial range, physical properties and interface boundary conditions of each layer.

[0029] The physical model selection and coupling module is used to construct the corresponding coupled partial differential equation system based on the device type of the target device.

[0030] The workflow of the physics model selection and coupling module includes: (1) Construct a general model, including: Poisson's equation is used to solve for electric potential distribution: The electron continuity equation is used to solve for electron concentration and electron current. The hole continuity equation is used to solve for hole concentration and hole current. This uses the quasi-Fermi level. and To represent electric current and : SRH recombination model and bimolecular recombination model are used to describe bulk recombination in devices: in, Represents the vector differential operator. Represents electrostatic potential, Indicates the amount of electron charge. Indicates hole concentration. Indicates electron concentration. Indicates time, Indicates the carrier generation rate. Indicates the carrier recombination rate. Indicates intrinsic carrier concentration. This represents the effective hole concentration when the trap is in equilibrium. This represents the effective electron concentration when the trap is in equilibrium.

[0031] (2) Ion migration model of perovskite devices Based on the aforementioned general model, an ion continuity equation is added to describe the migration, diffusion, and distribution evolution of ions under the combined influence of an electric field and a concentration gradient, thereby achieving self-consistent coupling between ions, charge carriers, and the electric field. The Poisson equation needs to consider the influence of ions. The parameter class defines ion vacancy concentration, ion diffusion coefficient, and ion-related dimensionless parameters. The ion continuity equation includes: in, Indicates ion concentration. This represents thermal voltage.

[0032] (3) Exciton dynamics model of organic devices Based on the general model, exciton dynamics equations are coupled to construct an exciton dynamics model for organic devices. Specifically, exciton dynamics equations are coupled to the aforementioned general model to describe the formation and dissociation of locally excited states (LE) and charge-transferred states (CT), as well as the generation process of free carriers, thereby achieving exciton-charge coupling. This embodiment explicitly states that organic solar cells need to couple the exciton zero-dimensional dynamics model with the drift-diffusion model: Furthermore, the continuity equation should be transformed accordingly as follows: in, This represents the constant rate at which locally excited excitons form charge-transfer excitons. Indicates the exciton transfer rate in the CT state. This represents the recombination rate of LE-state excitons. This represents the equilibrium LE exciton concentration. This represents the CT-state exciton dissociation rate. This represents the constant rate at which free charge carriers form charge transfer states. This represents the equilibrium CT exciton concentration.

[0033] The spatial mesh generation module is used to construct a spatial discrete mesh for layered structure models and enhances the computational accuracy of interfaces and thin-layer regions by using a non-uniform mesh generation method.

[0034] The workflow of the spatial mesh generation module includes: In a preferred embodiment, an adaptive spatial mesh distribution is achieved through a hyperbolic tangent mapping function. The mesh point distribution for each layer is divided according to the following formula, making the mesh denser near the interface and relatively sparser in the bulk region, thus balancing interface gradient resolution capability and computational efficiency. This allows the boundary grid points to cover a small Debye layer: in, This represents any spatial location within any material computational domain. Represents the endpoint of the computational domain for any material. Indicates the starting point of the computational domain for any material. Indicates spatial location index, This represents the number of grid points in any material computational domain. Indicates the grid encryption parameters. This represents the computational domain for any material. The mesh generation effect is as follows: Figure 3 As shown.

[0035] The core numerical solution module is used to solve the coupled partial differential equation system and obtain simulation results.

[0036] In this embodiment, the workflow of the core numerical solution module includes: (1) Conservation Discrete The finite volume method is used to spatially discretize the governing equations, preserving the conservation properties of conserved variables such as electrons, holes, and ions within the discrete control volume. Specifically, the finite volume method is preferred for spatial discretization of the governing equations to maintain the conservation properties of conserved variables such as electrons, holes, and ions within the discrete control volume. To simplify calculations, this invention employs a piecewise linear scheme, assuming that the transient and source terms are locally linearly distributed within each control volume, and approximating the reaction terms using the Simpson method. This method then provides a general discretization form for each control volume: in, This represents each discrete control volume. Represents the generalized transient term. This represents the generalized reaction term. Indicates a generalized source term. This represents the flux on each edge. Represents the Simpson operator acting on space: Simultaneously, the computing system constructs for any function The difference operator, average operator, linear operator, and matched integral matrix are used to achieve the corresponding discretization: In this embodiment, This represents the defined difference operator. This represents the defined average operator. This represents the distance between two adjacent grid points. Numerical experiments have shown that this spatial discretization can achieve second-order accuracy, such as... Figure 4 As shown.

[0037] (2) Construction of the mass matrix and Jacobian matrix The mass matrix and Jacobian matrix are constructed from the system of equations obtained by spatial discretization using the finite volume method. Specifically, the series of equations obtained by spatial discretization using the finite volume method can be regarded as algebraic differential equations (DAEs), as follows: in, Represents the mass matrix, This represents the right-hand term (rhs) of the corresponding equation. This represents an unknown function. The computational system should have separate methods for constructing the mass matrix and Jacobian matrix, and store them in a sparse matrix format.

[0038] (3) Higher-order time advancement algorithm An implicit Radau IIA family of high-order time-progression methods is employed to solve transient problems. Specifically, this method exhibits L-stability and high-order accuracy, making it particularly suitable for handling strongly rigid, multi-timescale coupled transient photovoltaic problems. Compared to conventional first-order implicit Euler methods or Crank-Nicolson methods, it significantly reduces numerical oscillations and improves temporal accuracy.

[0039] Specifically, from the time step arrive The update formula is as follows: Among them, the interior point phase is Calculations are performed at that location. Indicates the time step. This represents the slope weighting coefficient for each stage. Indicates the slope at each stage. Indicates a fixed node. This represents the state of the i-th stage. This indicates the total number of stages.

[0040] In the phase state With stage derivative The following is the configuration equation: This embodiment introduces stage displacement. And stack them into a vector Furthermore, a simplified Newton iteration is employed (freezing the Jacobian matrix at each step). And through numerical differentiation (Approximate calculation of the Jacobian matrix) ultimately yields a 3 n ×3 n Block system: in, Indicates 3× n Given the identity matrix, the corresponding coefficient values ​​can be easily obtained from the Butcher table. This allows for iteration at each stage. Numerical experiments demonstrate that this time discretization can achieve fifth-order accuracy, such as... Figure 5 As shown.

[0041] (4) Adaptive time step control An adaptive time step adjustment method based on local truncation error and Newton's iterative convergence constraint is employed to regulate the time step. Specifically, the step size is decreased when the error is large or the iterative convergence is poor, and increased when the error is small and the convergence is good. This strategy balances computational accuracy and efficiency and is suitable for transient solutions under external bias scanning and pulse excitation.

[0042] Specifically, this invention constructs a Gustafsson-style pre-controller to automatically control the time step, and its update method is as follows: in, m Indicates the preset order. tol This indicates the preset tolerance. error This indicates the current error.

[0043] If we accept this prediction step, then we will set the next time step to... ,in Conversely, if this step is rejected, the settings will be changed. .in, and The update factor and safety factor are calculated using the following formulas: in, k This represents the number of Newton iterations. We prefer the above scheme. , as well as .

[0044] (5) Solution process: The initial transient obtained from solving the transient problem is eliminated under the built-in potential condition, followed by pre-bias preprocessing. Then, a voltage scan in the first direction is performed and the results are recorded. Finally, a scan in the opposite direction is performed using the endpoint of the first direction scan as the initial value to obtain the complete transient JV hysteresis characteristics, such as... Figure 6 As shown, the computing system simultaneously considers the entire process of preprocessing, forward and reverse scanning, result stitching, and dimensional restoration.

[0045] The data export module is used to output simulation results in a common data format.

[0046] In this embodiment, the data export module is used to output the simulation results in a common data format, including txt, csv, image files and animation files, so that users can further perform statistics, plotting or report generation.

[0047] Example 2 In this embodiment, a novel computational design method for transient dynamics of photovoltaic devices includes: Receive and manage the input parameters required for simulation; construct a layered structure model of the target device based on the input parameters, and define the spatial range, physical properties and interface boundary conditions of each layer; construct the corresponding coupled partial differential equation system according to the device type of the target device; construct the spatial discrete mesh of the layered structure model, and use a non-uniform meshing method to enhance the calculation accuracy of the interface and thin layer regions; solve the coupled partial differential equation system to obtain the simulation results; and output the simulation results in a general data format.

[0048] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A system for the computational design of the transient dynamics of novel photovoltaic devices, characterized by, include: The module includes a user parameter input module, a device structure construction module, a physical model selection and coupling module, a spatial mesh generation module, a core numerical solution module, and a data export module. The user parameter input module is used to receive and manage the input parameters required for the simulation; The device structure construction module is used to construct a layered structure model of the target device based on the input parameters, and to define the spatial range, physical properties and interface boundary conditions of each layer. The physical model selection and coupling module is used to construct a corresponding set of coupled partial differential equations based on the device type of the target device; The spatial mesh generation module is used to construct the spatial discrete mesh of the layered structure model and to enhance the calculation accuracy of the interface and thin-layer regions by using a non-uniform mesh generation method. The core numerical solution module is used to solve the coupled partial differential equations and obtain simulation results; The data export module is used to output the simulation results in a general data format.

2. The system for calculating the design of the transient dynamics of the new photovoltaic device according to claim 1, characterized in that, The input parameters include: device geometric parameters, material physics parameters, dynamic parameters, and simulation condition parameters.

3. The system for calculating the design of the new photovoltaic device transient dynamics according to claim 1, characterized in that, The workflow of the physical model selection and coupling module includes: Building a general model: in, Represents the vector differential operator. Represents electrostatic potential, Indicates the amount of electron charge. Represents the dielectric constant. Indicates hole concentration. Indicates electron concentration. and Indicates doping concentration. Indicates time, and Represents current. Indicates the carrier generation rate. Indicates the carrier recombination rate. and Indicates mobility. and Indicates the quasi-Fermi level. Indicates intrinsic carrier concentration. and Indicates composite lifetime. This represents the effective hole concentration when the trap is in equilibrium. This indicates the effective electron concentration when the trap is in equilibrium. Based on the general model, an ion continuity equation is added to construct an ion migration model for perovskite devices. The ion continuity equation includes: in, Indicates ion concentration. Indicates thermal voltage. Indicates the ion diffusion coefficient; Based on the general model, exciton dynamics equations are coupled to construct an exciton dynamics model for organic devices: in, Represents a locally excited state. This represents the constant rate at which locally excited excitons form charge-transfer excitons. Represents the charge transfer state. Indicates the exciton transfer rate in the CT state. This represents the recombination rate of LE-state excitons. This represents the equilibrium LE exciton concentration. This represents the CT-state exciton dissociation rate. This represents the constant rate at which free charge carriers form charge transfer states. This represents the equilibrium CT exciton concentration.

4. The computational design system for the transient dynamics of the novel photovoltaic device according to claim 1, characterized in that, The workflow of the spatial grid partitioning module includes: achieving adaptive spatial grid distribution through a hyperbolic tangent mapping function, and partitioning the grid point distribution for each layer using the following formula: in, This represents any spatial location within any material computational domain. Represents the endpoint of the computational domain for any material. Indicates the starting point of the computational domain for any material. Indicates spatial location index, This represents the number of grid points in any material computational domain. Indicates the grid encryption parameters. Represents the computational domain of any material.

5. The computational design system for the transient dynamics of the novel photovoltaic device according to claim 1, characterized in that, The workflow of the core numerical solution module includes: The finite volume method is used to spatially discretize the governing equations, preserving the conservation properties of conserved variables such as electrons, holes, and ions on the discrete control volume. The mass matrix and Jacobian matrix are constructed from the system of equations obtained by spatial discretization using the finite volume method. The transient problem is solved using an implicit Radau IIA family of high-order time-progression methods; An adaptive time step adjustment method based on local truncation error and Newton's iterative convergence constraint is adopted to adjust the time step. The initial transient obtained from solving the transient problem is eliminated under the built-in potential condition, and then pre-bias preprocessing is performed. Then, the first direction voltage scan is performed and the result is recorded. Then, the reverse direction scan is performed with the end point of the first direction scan as the initial value to obtain the complete transient JV hysteresis characteristics.

6. A novel computational design method for transient dynamics of photovoltaic devices, wherein the method is applied to the system described in any one of claims 1-5, characterized in that, include: Receive and manage the input parameters required for the simulation; Based on the input parameters, a layered structure model of the target device is constructed, and the spatial range, physical properties and interface boundary conditions of each layer are defined. Construct a corresponding set of coupled partial differential equations based on the device type of the target device; A spatial discrete mesh is constructed for the layered structure model, and a non-uniform meshing method is used to enhance the computational accuracy of the interface and thin-layer regions. Solve the coupled partial differential equations to obtain simulation results; The simulation results are output in a general data format.

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