Method and device for determining and using a diagnostic tool comprising an extreme temperature response spectrum.
The extreme temperature response spectrum diagnostic tool addresses the challenge of accurately determining thermal conditions by processing temperature curves with first-order filters, enabling precise customization and stress assessment, thus optimizing equipment design for actual thermal environments.
Patent Information
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- MBDA FRANCE
- Filing Date
- 2024-04-17
- Publication Date
- 2026-04-24
AI Technical Summary
Existing methods fail to accurately account for actual thermal conditions and thermal inertia of equipment, leading to overdesign due to the lack of knowledge of extreme temperatures and climatic environments, making it impossible to specify equipment accurately.
A diagnostic tool using an extreme temperature response spectrum is developed, comprising steps to receive temperature curves, process them with first-order filters, extract temperature values, and form pairs with characteristic times to create a representative spectrum for thermal conditions, enabling precise customization and stress assessment.
Enables precise customization of equipment to withstand actual thermal conditions without overdesign, allowing for early design adaptation to actual thermal stresses and stress level assessment at reduced costs.
Smart Images
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Abstract
Description
Title of the invention: Method and device for determining and using a diagnostic tool comprising an extreme temperature response spectrum. technical field
[0001] The present invention relates to a method and device for determining and using a diagnostic tool comprising an extreme temperature response spectrum. State of the art
[0002] Although not exclusively applicable, the present invention is particularly relevant to the military field, and especially to any military equipment likely to be subjected to extreme conditions, such as a missile, for example. This can include any type of equipment, such as electronic, pyrotechnical, or organic materials, thermal protection devices, etc.
[0003] To optimize the durability of equipment, particularly military equipment, over time, it is known to implement a customization process (defining the precise requirements) of the mechanical requirements, sometimes severe, to which this equipment may be subjected or has been subjected. In particular, to compare different mechanical environments, it is known to use a shock response spectrum and a fatigue damage spectrum. These spectra help to specify precisely the extremely severe stresses (vibrations, shocks, etc.) to which the equipment in question will be or has been exposed.
[0004] However, in addition to mechanical environments, equipment can also be subjected to climatic environments, such as extreme temperatures of varying severity. For climatic environments, a customization approach to equipment would require knowledge of actual conditions. This would necessitate conducting extremely long campaigns (requiring measurements over several years for meteorological parameters) at numerous sites worldwide (where the equipment could be deployed) to determine these actual conditions, particularly the temperature, to which the equipment might be subjected. Such a solution is therefore not feasible in practice.
[0005] However, the failure to take into account actual thermal conditions (as well as the lack of knowledge of the thermal inertia of the equipment) makes it impossible to specify the equipment accurately and generally leads to considering the environments to which the equipment is subjected as much more severe than they actually are. This means that the equipment is designed and adapted to withstand more severe thermal conditions than it will actually encounter.
[0006] There is therefore a need to find a solution to make available a diagnostic tool capable of determining the effect of thermal conditions, in particular the extreme temperatures to which equipment will be subjected, in particular to carry out customization during the design of the equipment or to assess its level of stress. Description of the invention
[0007] The present invention relates to a method for determining and using a diagnostic tool, which makes it possible to meet the aforementioned need, the diagnostic tool comprising at least one extreme temperature response spectrum for a plurality of (different) types of material.
[0008] To this end, according to the invention, said process comprises at least the following steps: - a step of receiving at least one temperature curve, the temperature curve representing a variation of the temperature as a function of time at a given geographical location; - a processing step comprising a sequence of successive substeps, said sequence being implemented iteratively for each of a plurality of N different values of a characteristic time Tau_i, N being an integer greater than 1 and i being an integer ranging from 1 to N, the characteristic time Tau_i being associated with a given type of material and representative thereof, said sequence of successive substeps comprising, for a given iteration: • a first sub-step consisting of filtering said temperature curve, using a first-order filter having as its time constant said characteristic time Tau_i, so as to obtain a so-called modified curve; • a second sub-step consisting of extracting from the modified curve thus obtained, a temperature value corresponding to a specific temperature parameter (for example a maximum temperature, a minimum temperature, ...); and • a third sub-step consisting of associating this extracted temperature value with the characteristic time Tau_i corresponding to the iteration, to form a pair of values, the pairs of values obtained at the end of the N iterations forming a set of pairs, said extreme temperature response spectrum comprising at least said set of pairs of values; and - a comparison step consisting of making comparisons, from at least said extreme temperature response spectrum, to carry out at least one diagnosis.
[0009] Thus, thanks to the invention, an extreme temperature response spectrum is obtained that is representative of a temperature curve for a given geographical location, for example, a city, and that provides temperature values, including extreme values, as a function of the characteristic weather that is representative of a type of equipment under consideration. Therefore, for a particular type of equipment, for example, missile components, it is possible to determine the temperature conditions (including extreme conditions) to which it has been or will be subjected.
[0010] By making comparisons, for example with the extreme temperature response spectra of different geographical locations or of different types of equipment, we thus have a diagnostic tool that makes it possible to determine the thermal conditions, particularly extreme temperatures, to which equipment will be subjected. This is especially useful for customizing equipment during the design phase (to adapt it to the thermal constraints encountered so that it can withstand these temperatures without being oversized) or for assessing its stress level. This diagnostic tool is applicable to the main types of equipment (electronic, pyrotechnical, organic, thermal protection, etc.) as used, in particular, in the military field.
[0011] Advantageously, said set of pairs of values (forming the extreme temperature response spectrum) is represented in the form of a curve.
[0012] Furthermore, advantageously, the second sub-step of the processing step performs the following operations: - sort the temperatures of said modified curve in ascending order; and - identify, in said sorting, the temperature value corresponding to the specific temperature parameter.
[0013] In a first embodiment, the reception step and the processing step are implemented for each of a plurality of different temperature curves (i.e. relating to different geographical locations) allowing to obtain a plurality of extreme temperature response spectra, and the comparison step consists of making comparisons from at least two of these extreme temperature response spectra.
[0014] This makes it possible to compare the effects of temperature on a given piece of equipment or on several given pieces of equipment, for several different temperature curves, i.e., for several different geographical locations. This makes it possible, in particular, to adapt the protection of the equipment to the thermal conditions to which it will be subjected in a given geographical location, for example, a city with very high temperatures.
[0015] In a second embodiment, as a variant or complement to the first embodiment mentioned above, the processing step is implemented for a plurality of Different specific temperature parameters (maximum temperature, minimum temperature, ...) allow obtaining a plurality of extreme temperature response spectra, each of which is associated with a specific temperature parameter, and the comparison step consists of making comparisons from at least two of these extreme temperature response spectra.
[0016] Advantageously, the specific temperature parameter corresponds to a particular, non-zero percentage of a maximum temperature, this percentage taking a value greater than 0 and less than or equal to 100.
[0017] Preferably, the specific temperature parameter corresponds to one of the following temperature values: - a maximum temperature; - 99% of the maximum temperature; - a percentage between 1% and 100% of the maximum temperature; - an average temperature; - a minimum temperature; - 99% of the minimum temperature; - a percentage between 1% and 100% of the minimum temperature.
[0018] Thanks to the second embodiment mentioned above, a plurality of different specific temperature parameters are taken into account, allowing a plurality of extreme temperature response spectra to be obtained, highlighting different types of temperature, such as the maximum temperature to which the material will be subjected, but also other temperatures (such as the minimum temperature for example), which may be relevant for certain applications or in certain situations.
[0019] Moreover, advantageously, the process includes a preliminary step consisting of determining, for a plurality of different materials, the associated characteristic time.
[0020] Advantageously, at this preliminary stage, the characteristic time associated with a material is obtained from a so-called representative curve illustrating the temperature measured inside the material as a function of time, starting at the moment when said material (which initially has an initial internal temperature Ti) is subjected to a given external temperature Tf, and the characteristic time corresponds to one of the following times: - the time for the temperature measured inside the material to reach 63% of the temperature difference Tf-Ti between the external temperature Tf and the initial internal temperature Ti; - one third of the time for the temperature measured inside the equipment to reach 95% of the temperature difference Tf-Ti; - one fifth of the time for the temperature measured inside the material to reach 99% of the temperature difference Tf-Ti.
[0021] In a preferred embodiment, the temperature curve(s) received at the reception stage are obtained from one or more public, generally free, databases, thus enabling the process to be implemented at a reduced cost. Furthermore, numerous temperature curves for different locations are currently available.
[0022] The present invention also relates to a device for determining and using a diagnostic tool comprising at least one extreme temperature response spectrum for a plurality of (different) types of material.
[0023] According to the invention, said device comprises at least the following units: - a receiving unit configured to receive at least one temperature curve, the temperature curve representing a variation of the temperature as a function of time at a given geographical location; - a processing unit performing iterative processing for each of a plurality of N different values of a characteristic time Tau_i, where N is an integer greater than 1 and i is an integer ranging from 1 to N, the characteristic time Tau_i being associated with and representative of a given type of equipment, said processing unit comprising: • a first subunit configured to filter said temperature curve, using a first-order filter having as its time constant said characteristic time ri, so as to obtain a so-called modified curve; • a second subunit configured to extract from the modified curve thus determined, a temperature value corresponding to a specific temperature parameter; and • a third subunit configured to associate this extracted temperature value with the characteristic time ri corresponding to the iteration, to form a pair of values, the pairs of values obtained at the end of the N iterations forming a set of pairs, said extreme temperature response spectrum comprising at least said set of pairs of values; and - a comparison unit configured to perform, from at least said extreme temperature response spectrum, comparisons to carry out at least one diagnosis.
[0024] The present invention also relates to a method of assisting in the adaptation of equipment to future thermal constraints.
[0025] According to the invention, this method comprises at least the following operations: - carrying out a diagnostic assessment of the equipment at one or more geographical locations where it is expected to be used, by implementing the aforementioned procedure and using a characteristic time associated with the type of said equipment; and - a design of the equipment that is adapted to the result of this diagnosis.
[0026] Within the framework of the present invention, a simplified transfer function corresponding to a first-order filter is taken into account as a heat transfer function linking the external thermal conditions of a considered piece of equipment (whose thermal behavior is not exactly known) to the internal thermal conditions of this equipment. This function has as its time constant a characteristic time Tau associated with (and representative of) this equipment (and of its storage and / or use conditions).
[0027] The present invention has many advantages. In particular: - extreme temperature response spectra provide an advantageous solution in the field of thermal environment specification, and more particularly for extreme temperature environments; - they allow, for equipment currently in use, to determine the level of stress on that equipment; - above all, they allow us to design future equipment that is as precise as possible, so that it can withstand the thermal stresses to which it is likely to be subjected; - they allow for the earliest possible design, adapted to the actual thermal conditions; - the process is applicable to a wide range of materials (equipment, missiles, containers, etc.) whose thermal behavior is not precisely known; - the process can be implemented at reduced cost thanks to generally free access to temperature curves. Brief description of the figures
[0028] Other features and advantages of the device, process and / or method according to the invention will become clearer from the following description of illustrative and non-limiting examples of embodiments, annexed to the following figures.
[0029] Fig. 1 is the synoptic diagram of a device according to a particular embodiment of the invention.
[0030] Fig. 2 is a schematic view of an example of a system, in this case a missile, equipped with materials to which the present invention can be applied.
[0031] The [Fig.3] is a graph that explains the characteristics and the method of determining a characteristic time associated with a given material.
[0032] Fig. 4 schematically illustrates a particular embodiment of a process according to the invention.
[0033] Fig. 5 is a graph showing temperature curves associated with the first two cities.
[0034] Fig. 6 is a graph derived from the processing of the temperature curves of Fig. 5, showing curves illustrating, each, the evolution of the extreme temperature response spectrum as a function of characteristic time, and this for several specific temperature parameters and for each of these first two cities.
[0035] Fig. 7 is a graph showing temperature curves associated with two second cities.
[0036] Figure 8 is a graph derived from the processing of the temperature curves in Figure 7, showing curves illustrating, each, the evolution of the extreme temperature response spectrum as a function of characteristic time, for several specific temperature parameters and for each of these two secondary cities. Detailed description
[0037] Device 1 illustrating a particular embodiment of the invention and represented schematically in [Fig. 1] is a device for determining and using a diagnostic tool comprising at least one Extreme Temperature Response Spectrum (ETRS).
[0038] Device 1 applies more particularly to the military field, and especially to any military equipment likely to be subjected to extreme temperature conditions, such as a missile, for example. This can include any type of equipment, such as electronic, pyrotechnical, or organic materials, thermal protection devices, etc.
[0039] By way of example, the components 2A and 2B shown in [Fig. 2] correspond to two components of a missile 3, for example, electronic equipment of the missile 3 for component 2A and a propulsion unit of the missile 3 for component 2B. The missile 3 is mounted in a container 4 which is installed on the ground 5 in a location where it is subjected to thermal conditions specified below and illustrated by a thermometer 6 and arrows 7.
[0040] In the context of the present invention, a parameter called the "characteristic time" Tau is associated with a given type of material. This characteristic time Tau represents the time required for the internal temperature of the material in question to adapt to the external temperature at the location of this material (or the system comprising this material). This characteristic time Tau varies, in particular, according to the size of the material in question (missile equipment, missile, etc.), its configuration, and its storage conditions (in a container or outside a container, etc.). This characteristic time represents the time constant of a (simplified) heat transfer function used, linking external conditions to internal conditions of the material, as specified below.
[0041] By way of illustration: - The time required to completely thermally stabilize a single cubic electronic device measuring 20 cm on each side is a few hours. In this case, the characteristic Tau time is considered, for example, to be on the order of 1 hour; and - the time required to completely thermally stabilize a propellant block of a long-range missile, when the missile is in logistical condition, can reach several tens of hours, or even a few days. In this case, the characteristic Tau time is considered, for example, to be on the order of 20 hours.
[0042] Said device 1 comprises, as shown in [Fig. 1]: - a receiving unit 8 configured to receive at least one temperature curve Cl, C2, C3, C4 (figures 5 and 7). The temperature curve represents a variation of the temperature T (expressed in °C) as a function of time t (expressed for example in hours) at a given geographical location, for example at the level of a city as specified below; - a processing unit 9 receiving the temperature curve(s) Cl, C2, C3, C4 from the receiving unit 8. The processing unit 9 is configured to perform processing using this temperature curve(s) Cl, C2, C3, and C4. The processing unit 9 performs the processing iteratively, for each of a plurality of N values of a characteristic time Tau_i, in order to determine at least one extreme temperature response spectrum (ETRS). N is an integer greater than 1 and i is an integer ranging from 1 to N. Each characteristic time Tau_i is associated with a given type of equipment and is representative of it as specified below; and - a comparison unit 10 configured to perform, from at least the extreme temperature response spectrum (ETRS) (received from the processing unit 9), comparisons to carry out at least one diagnosis, as also specified below.
[0043] In addition, the processing unit 9 comprises: - a subunit 11 configured to, at each iteration i, filter the temperature curve using a first-order filter whose time constant is said characteristic time Tau_i, so as to obtain a so-called modified curve; - a subunit 12 configured to, at each iteration i, extract from the modified curve thus determined, a temperature value corresponding to a specific temperature parameter (for example, a maximum temperature, a minimum temperature, etc.); and - a subunit 13 configured to, at each iteration i, associate this extracted temperature value with the characteristic time Tau_i corresponding to iteration i, to form a pair of values.
[0044] The pairs of values obtained at the end of the N iterations form a set of pairs. The extreme temperature response spectrum (ETRS) includes at least this set of pairs of values.
[0045] Said device 1 also includes a receiving unit 14 configured to receive the minimum and maximum characteristic times associated with the plurality of different materials considered. The minimum and maximum characteristic times are inputs entered by the user based on their knowledge of the materials. The minimum and maximum characteristic times are transmitted to the processing unit 9 and used by the subunits 11, 12, and 13 during the processing operations performed. They can also be used by the comparison unit 10.
[0046] Device 1, as described above, is intended to implement a method P for determining and using a diagnostic tool comprising at least one extreme temperature response spectrum (ETRS). This method P implemented by device 1, which is shown in [Fig. 4], is now described.
[0047] Said process P comprises at least the following steps: - a step El of receiving at least one temperature curve Cl, C2, C3, C4 (figures 5 and 7) which shows a variation of the temperature T as a function of time t at a given geographical location; - a processing step E2 comprising a sequence of successive substeps E2A, E2B, and E2C. This sequence is implemented iteratively for each of a plurality of N values of the characteristic time Tau_i. N is an integer greater than 1, and i is an integer ranging from 1 to N. A characteristic time Tau_i is associated with a given type of material, for example, material 2A or material 2B in [Fig. 2], and is representative of that material. The processing step E2 comprises, for a given iteration i: • the sub-step E2A consisting of filtering said temperature curve Cl, C2, C3, C4 using a first-order filter having as its time constant said characteristic time Tau_i, so as to obtain a so-called modified curve; • substep E2B, which consists of extracting from the modified curve thus determined, a temperature value corresponding to a specific temperature parameter; and • The E2C substep consists of associating this extracted temperature value with the characteristic time Tau_i corresponding to iteration i, to form a pair of values. The pairs of values obtained at the end of the N iterations form a set of pairs. This set of pairs of values is preferably represented as shape of a curve. The extreme temperature response spectrum (ETRS) includes at least this set of pairs of values; and - a comparison step E3 consisting of carrying out, from at least said extreme temperature response spectrum (SRET), comparisons to perform at least one diagnosis.
[0048] The main steps of the process P are described in more detail below.
[0049] The temperature curves Cl, C2, C3 and C4 that we wish to use are extracted from databases and received by the receiving unit 8 at the El reception stage. These temperature curves are generally sampled hourly, i.e. with a temperature measurement every hour, and this for periods of several years sometimes.
[0050] In a preferred embodiment, the temperature curve(s) received in the reception step E1 are obtained from one or more commonly available public databases, including databases available on government websites. Access to information in such databases is generally free. This allows for the implementation of process P at a reduced cost. Furthermore, numerous temperature curves for different locations, subjected to varying and sometimes extreme thermal conditions, are currently available. This provides a wide range of diagnostic possibilities.
[0051] After receiving the temperature curve(s) Cl, C2, C3, C4, which we wish to use, the subunit 11 performs, at substep E2A, for each temperature curve Cl, C2, C3, C4, a usual filtering, using a first order filter having as its time constant the characteristic time Tau considered.
[0052] In the context of the present invention, the heat transfer function (HT) relating the external conditions to the internal conditions of the equipment under consideration is therefore reduced to a first-order filter (with the characteristic time Tau as the time constant). This simplified heat transfer function (HT) is described by: - a thermal inertia Cth which will slow down the propagation of external conditions, which is described by: • m: mass (in kg); and • Cp: specific heat capacity (in J / kg / K); and - a thermal conductance URth which will promote the propagation of external conditions. Thermal conductance is described: O by hS for convection phenomena, with: • h: the convective heat transfer coefficient (W / m2 / K); and • S: the convective heat transfer surface area (m2); O or by AS / e for conduction phenomena, with: • e: the thickness traversed by the flux (in m); • Â: thermal conductivity (in W / m / K); and • S: the surface area for heat exchange by conduction (in m2).
[0053] The internal temperature of the equipment is the solution to the first-order differential equation (1), which can be written as follows: Tau^+T(t) =Text
[0054] With: • Text: the outside temperature; • T(t): the internal temperature as a function of time t; and • Tau: a constant characteristic of the thermal behavior of the equipment.
[0055] When the equipment is subjected to a temperature step between 0 and Text, equation (1) has the following solution: T(t) = Text(1-6¾)
[0056] When the equipment is subjected to a temperature step between Ti and Tf ([Fig. 3]), equation (1) can be written as: Tau^+T(t) =Tf
[0057] The solution to this equation is: T(t) = Ti+(Tf-Ti) ( 1-6¾ )
[0058] The CR curve of [Fig.3] illustrates the response of a first-order filter with time constant Tau=20, when subjected to a temperature step between 15°C (Ti) and 50°C (Tf).
[0059] By observing the behavior (illustrated by this CR curve), the characteristic time Tau can be identified using one of the following properties: - after a duration of Tau, warming is 63% of the temperature difference Tf-Ti considered; - after a duration of 3 Tau, warming is 95% of the temperature difference Tf-Ti considered; - after a duration of 5 Tau, warming is 99% of the temperature difference Tf-Ti considered.
[0060] The filtering carried out in substep E2A makes it possible to obtain a modified curve, which is then used in substep E2B to extract the temperature value corresponding to a specific temperature parameter.
[0061] The specific temperature parameter used in substep E2B of the processing step E2 corresponds to a particular, non-zero percentage of the maximum temperature, this percentage taking a value greater than 0 and less than or equal to 100.
[0062] Preferably, the specific temperature parameter corresponds to one of the following temperature values: - a maximum temperature; - 99% of the maximum temperature; - a percentage between 1% and 100% of the maximum temperature; - an average temperature; - a minimum temperature; - 99% of the minimum temperature; - a percentage between 1% and 100% of the minimum temperature.
[0063] More specifically, in substep E2B of processing step E2, subunit 12 performs the following operations to extract the desired temperature value: - it sorts all the temperatures of the modified curve obtained in substep E2A in ascending order; and - it identifies, in the said sorting thus carried out, the temperature value which corresponds to the specific temperature parameter, for example 99% of the maximum temperature.
[0064] The temperature value thus identified is associated, in substep E2C, by subunit 13, with the characteristic time corresponding to the iteration, so as to form a pair of values for that iteration. The set of pairs of values obtained in substep E2C, at the end of the iterations, forms the extreme temperature response spectrum (ETRS). This set of pairs of values is preferably represented as a curve.
[0065] Figures 6 and 8 show different curves thus obtained, which will be specified below.
[0066] Then, in the E3 comparison step, comparisons are made, from the extreme temperature response spectra (ETRS) determined in the E2 processing step, to perform at least one diagnosis concerning the material(s) considered.
[0067] Furthermore, in a particular embodiment, the process P includes a preliminary step E0. This preliminary step E0 consists of receiving, for a plurality of different materials, the minimum and maximum characteristic times.
[0068] The behavior of each piece of equipment is simplified by a single-parameter system, namely the time (characteristic time) required for its temperature to stabilize (thermal inertia).
[0069] In the preliminary step E0, the characteristic time associated with a given piece of equipment, such as the electronic equipment 2A or the propulsion unit 2B of the missile 3 in [Fig. 2], is obtained from a so-called representative curve CR as shown in [Fig. 3]. This representative curve CR indicates the temperature T (expressed in °C) measured inside the equipment in question 2A, 2B as a function of time t (expressed in hours). It is representative of the material under consideration. The measurement used begins when the material, which initially has an internal temperature Ti (initial) of 15°C, is subjected to a given external temperature Tf of 50°C. For the sake of simplicity in the diagram, a representative curve CR showing a theoretical evolution is shown in [Fig. 3], as curves obtained from actual measurements generally have a less smooth and less consistent trace.
[0070] The characteristic time Tau associated with the representative curve CR is obtained from this representative curve CR, by identifying one of the following times: - the time for the temperature measured inside the material to reach 63% of the temperature difference Tf-Ti. In the example of [Fig.3], the time to reach 63% is 20 h; - one third of the time for the temperature measured inside the material to reach 95% of the temperature difference Tf-Ti. In the example of [Fig.3], the time to reach 95% is 60 h; - one fifth of the time for the temperature measured inside the material to reach 99% of the temperature difference Tf-Ti.
[0071] Therefore, for a given material, for example material 2B of [Fig.2], which has a representative curve CR such as that of [Fig.3], it is deduced in the preliminary step E0 that the characteristic time Tau associated with this material corresponds to 20 hours.
[0072] Thus, thanks to the P method, an extreme temperature response spectrum (ETRS) is obtained which is representative of a temperature curve for a given geographical location, for example a city, and which provides temperature values, including extreme values, as a function of characteristic times that are representative of a plurality of types of equipment considered. Consequently, for a particular type of equipment, for example a missile component, it is possible to know the temperature (maximum temperature, minimum temperature, etc.): - to which it would have been subjected if it had been positioned at the geographical location considered; or - to which it will be subjected if it is positioned at the geographical location in question.
[0073] Thus, by making comparisons, for example with the spectrum of Extreme Temperature Response (ETR) of a different geographical location or for different types of equipment, a diagnostic tool that determines the thermal conditions, particularly extreme temperatures, to which equipment will be subjected, especially for customization during equipment design (to adapt it to the thermal constraints encountered so that it can withstand these temperatures without being oversized) or to assess its stress level. This diagnostic tool is applicable to the main types of equipment (electronic, pyrotechnical, organic, thermal protections...), such as those used particularly in the military field.
[0074] Different types of diagnosis are possible thanks to the invention. Two different embodiments of the method P are specified below by way of illustration.
[0075] In a first embodiment of the process P, the reception step El and the processing step E2 are implemented for each of a plurality of different temperature curves Cl, C2, C3, C4 allowing to obtain a plurality of extreme temperature response spectra (SRET), and the comparison step E3 consists of making comparisons from at least two of these extreme temperature response spectra (SRET).
[0076] By way of illustration, two examples of implementation of this first embodiment are presented below.
[0077] For the first example, reference is made to the temperature curves (or time series) Cl and C2 of two cities named VI and V2, as shown in [Fig. 5]. These temperature curves Cl and C2 are sampled hourly between 2010 and 2016; that is, they include the values of the external temperatures measured respectively in cities VI and V2, each hour, from 2010 to 2016. The maximum temperature is 50°C for city VI and 47°C for city V2. The average diurnal temperature range is higher for city VI (8.2°C) than for city V2 (5.3°C).
[0078] Figure 6 represents the extreme temperature response spectra (ETRS) for cities VI and V2, obtained from the temperature curves Cl and C2. The extreme temperature response spectra (ETRS) are represented as a solid line for city VI and as dashed lines for city V2.
[0079] For each city, three temperature curves (as a function of the characteristic time Tau) are shown, respectively, for different values of specific temperature parameter.
[0080] More specifically, the following is shown in [Fig. 6]: - for city V1: • a TmaxlA curve determined for a specific temperature parameter corresponding to the maximum temperature; • a TmaxlB curve determined for a specific temperature parameter corresponding to 99% of the maximum temperature; and • a TmaxlC curve determined for a specific temperature parameter corresponding to 95% of the maximum temperature; - for city V2: • a Tmax2A curve determined for a specific temperature parameter corresponding to the maximum temperature; • a Tmax2B curve determined for a specific temperature parameter corresponding to 99% of the maximum temperature; and • a Tmax2C curve determined for a specific temperature parameter corresponding to 95% of the maximum temperature.
[0081] By comparing some of these extreme temperature response spectra (ETRS) in comparison step E3, it can be seen, from the Tmax1A and Tmax2A curves, that for small equipment or material (TauclOh), city VI is more severe. Conversely, for larger equipment or material (Tau >10h), for example a long-range propulsion system, city V2 is more severe.
[0082] Furthermore, it is also observed, from the curves Tmax1B and Tmax2B (99% of the maximum temperature), that the two cities VI and V2 are equivalent regardless of the value of the characteristic time Tau (i.e. regardless of the material).
[0083] For the second example, reference is made to the C3 and C4 temperature curves (or time curves) of two cities named V3 and V4, as shown in [Fig. 7]. These C3 and C4 temperature curves are sampled hourly between 2010 and 2016; that is, they include the values of the external temperatures measured respectively in cities V3 and V4, every hour, from 2010 to 2016. The maximum temperature is 40°C for city V3 and 37.7°C for city V4.
[0084] Figure 8 represents the extreme temperature response spectra (ETRS) for cities V3 and V4, obtained from the temperature curves C3 and C4. Temperatures are represented as solid lines for city V3 and as dashed lines for city V4.
[0085] For each city, three temperature curves (as a function of the characteristic time Tau) are shown, respectively, for different values of specific temperature parameter.
[0086] More specifically, the following is shown in [Fig.8]: - for city V3: • a Tmax3A curve determined for a specific temperature parameter corresponding to the maximum temperature; • a Tmax3B curve determined for a specific temperature parameter corresponding to 99% of the maximum temperature; and • a Tmax3C curve determined for a specific temperature parameter corresponding to 95% of the maximum temperature; - for city V4: • a Tmax4A curve determined for a specific temperature parameter corresponding to the maximum temperature; • a Tmax4B curve determined for a specific temperature parameter corresponding to 99% of the maximum temperature; and • a Tmax4c curve determined for a specific temperature parameter corresponding to 95% of the maximum temperature.
[0087] By carrying out a comparison of some of these extreme temperature response spectra (ETRS) in the comparison step E3, it is observed, from the curves Tmax3A and Tmax4A, that, for any type of equipment or material (Tau<50h), city V3 is more severe than city V4 (with a temperature difference of 3°C on the maximum values).
[0088] Furthermore, it can also be observed, from the Tmax3B and Tmax4B curves (99% of the maximum temperature), that for small equipment or materials (Tau < 5h), the two cities V3 and V4 are relatively equivalent. On the other hand, for large equipment or materials (Tau > 10h), city V4 is more severe than city V3.
[0089] Thanks to this first embodiment (described above), it is possible to compare the effects on a given material or on several given materials of temperatures, for several different temperature curves, that is to say for several different geographical locations (such as cities VI, V2, V3 and V4).
[0090] This makes it possible in particular to adapt the equipment to the thermal conditions to which it will be subjected in a given geographical location, for example a city with very high temperatures.
[0091] In a second embodiment of process P, as a variant or in addition to the first embodiment described above: - The E2 processing step is implemented for a plurality of different specific temperature parameters, allowing for the acquisition of a plurality of extreme temperature response spectra (ETRS), each of which is associated with a specific temperature parameter, such as the 3 curves TmaxlA, TmaxlB and TmaxlC in [Fig.6] for city VI, which correspond, respectively, to the following specific temperature parameters: • the maximum temperature; • 99% of the maximum temperature; • 95% of the maximum temperature; and - The E3 comparison step consists of making comparisons from at least two of these extreme temperature response spectra (ETRS).
[0092] Thanks to this second embodiment, a plurality of different specific temperature parameters are taken into account, which makes it possible to obtain a plurality of extreme temperature response spectra (ETRS), highlighting different types of temperature, such as the maximum temperature to which the material will be subjected, but also other temperatures such as the minimum temperature, or percentages of the maximum or minimum temperature, which may be relevant for certain applications or in certain situations.
[0093] The first example mentioned above in Figures 5 and 6 and the second example mentioned above in Figures 7 and 8 (which, in addition to different geographical locations, generate extreme temperature response spectra (ETRS) for different specific temperature parameters) are also obtained, in part, from said second embodiment of process P.
[0094] Device 1 and process P therefore use a "standard" model allowing comparison of the effect of temperature curves (or time curves) on a material whose heat transfer function is not precisely known, namely the time required for the temperature to propagate from the outside to the core of the material in question.
[0095] Device 1 and method P, as described above, thus offer numerous advantages. In particular: - the extreme temperature response spectra obtained provide an advantageous solution in the field of thermal environment specification, and more particularly for extreme temperature environments; - they allow, for equipment currently in use, to determine the level of stress on that equipment; - above all, they allow for the design of future equipment, as precisely as possible, to withstand the thermal stresses to which it is likely to be subjected, i.e., to achieve customization. Such customization (particularly for climatic environments with regard to extreme stresses) makes it possible to best dimension the equipment to the thermal stresses, regardless of the type of equipment; - they allow for the earliest possible design, adapted to the actual thermal conditions; - the process is applicable to a wide range of materials (equipment, missile, container, etc.) whose thermal behavior is not precisely known; and - the process can be implemented at reduced cost thanks to generally free access to temperature curves.
Claims
Demands
1. Method for determining and using a diagnostic tool comprising at least one extreme temperature response spectrum for a plurality of material types, the diagnostic tool being capable of determining the effect of thermal conditions, characterized in that it comprises at least the following steps: - a step (El) of receiving at least one temperature curve (Cl, C2, C3, C4), the temperature curve (Cl, C2, C3, C4) representing a variation of temperature as a function of time at a given geographical location;- a processing step (E2) comprising a sequence of successive substeps (E2A, E2B, E2C), said sequence being implemented iteratively for each of a plurality of N different values of a characteristic time Tau_i, N being an integer greater than 1 and i being an integer ranging from 1 to N, the characteristic time Tau_i being associated with a given type of material (2A, 2B) and representative thereof, said sequence of successive substeps comprising, for a given iteration: • a first substep (E2A) consisting of filtering said temperature curve (C1, C2, C3, C4), using a first-order filter having as its time constant said characteristic time Tau_i, so as to obtain a so-called modified curve; • a second substep (E2B) consisting of extracting from the modified curve thus obtained, a temperature value corresponding to a specific temperature parameter;and • a third sub-step (E2C) consisting of associating this extracted temperature value with the characteristic time Tau_i corresponding to the iteration, to form a pair of values, the pairs of values obtained at the end of the N iterations forming a set of pairs, said extreme temperature response spectrum comprising at least said set of pairs of values; and - a comparison step (E3) consisting of carrying out, from at least said extreme temperature response spectrum, comparisons to perform at least one diagnosis.;
2. A method according to claim 1, characterized in that the receiving step (E1) and the processing step (E2) are implemented for each of a plurality of different temperature curves (Cl, C2, C3, C4) allowing to obtain a plurality of extreme temperature response spectra and in that the comparison step (E3) consists of making comparisons from at least two of these extreme temperature response spectra.
3. A method according to any one of claims 1 and 2, characterized in that the processing step (E2) is carried out for a plurality of different specific temperature parameters allowing a plurality of extreme temperature response spectra to be obtained, each of which is associated with a specific temperature parameter, and in that the comparison step (E3) consists of making comparisons from at least two of these extreme temperature response spectra.
4. A method according to any one of the preceding claims, characterized in that the specific temperature parameter corresponds to a particular, non-zero percentage of a maximum or minimum temperature.
5. A method according to claim 4, characterized in that the specific temperature parameter corresponds to one of the following temperature values: - a percentage between 1% and 100% of the maximum temperature; - an average temperature; - a percentage between 1% and 100% of the minimum temperature.
6. A method according to any one of the preceding claims, characterized in that the second substep (E2B) of the processing step (E2) performs the following operations: - sorting the temperatures of said modified curve in ascending order; and - identifying, in said sorting, the temperature value corresponding to the specific temperature parameter.
7. A method according to any one of the preceding claims, characterized in that it comprises a preliminary step (E0) consisting of determining, for a plurality of different materials (2A, 2B), the associated minimum and maximum characteristic times.
8. Method according to claim 7, characterized in that at the preliminary step (E0), the characteristic time associated with a material (2A, 2B) is obtained from a so-called representative curve illustrating the temperature measured inside the material (2A, 2B) as a function of time, starting at the moment when said material (2A, 2B), which initially has an initial internal temperature Ti, is subjected to a given external temperature Tf, and in that the characteristic time corresponds to one of the following times: - the time for the temperature measured inside the material (2A, 2B) to reach 63% of the temperature difference Tf-Ti between the external temperature Tf and the initial internal temperature Ti; - one third of the time for the temperature measured inside the material (2A, 2B) to reach 95% of the temperature difference Tf-Ti; - one fifth of the time for the temperature measured inside the material (2A, 2B) to reach 99% of the temperature difference Tf-Ti.
9. A method according to any one of the preceding claims, characterized in that the temperature curve(s) (Cl, C2, C3, C4), received at the (El) reception stage, are from a public database.
10. A method according to any one of the preceding claims, characterized in that said set of pairs of values is represented in the form of a curve.
11. Device for determining and using a diagnostic tool comprising at least one extreme temperature response spectrum for a plurality of equipment types, the diagnostic tool being capable of determining the effect of thermal conditions, characterized in that it comprises at least the following units: - a receiving unit (8) configured to receive at least one temperature curve, the temperature curve (C1, C2, C3, C4) representing a temperature variation over time at a given geographical location; - a processing unit (9) performing iterative processing for each of a plurality of N different values of a characteristic time Tau_i, N being an integer greater than 1 and i being an integer ranging from 1 to N, the characteristic time Tau_i being associated with a given type of material (2A, 2B) and representative thereof, said processing unit comprising: • a first subunit (11) configured to filter said temperature curve (Cl, C2, C3, C4), using a first-order filter having as its time constant said characteristic time Tau_i, so as to obtain a so-called modified curve; • a second subunit (12) configured to extract from the modified curve thus determined, a temperature value corresponding to a specific temperature parameter; and • a third subunit (13) configured to associate this extracted temperature value with the characteristic time Tau_i corresponding to the iteration, to form a pair of values, the pairs of values obtained at the end of the N iterations forming a set of pairs of values, said extreme temperature response spectrum comprising at least said set of pairs of values;and - a comparison unit (10) configured to perform, from at least said extreme temperature response spectrum, comparisons to carry out at least one diagnosis.;
12. A method for assisting in the adaptation of equipment to future thermal constraints, characterized in that it comprises at least the following operations: - carrying out a diagnosis relating to the equipment (2A, 2B) at one or more geographical locations where it is expected to be used, by implementing the process (P) according to any one of claims 1 to 10 and using a characteristic time associated with the type of said equipment (2A, 2B); and - a design of the equipment which is adapted to the result of this diagnosis.