Systems, methods, and apparatuses for providing noise removal for flow sensing components

Abstract

Disclosed herein are methods, devices, and systems for providing dehumidification for gas detection components. An example device can include a flow sensing component configured to detect a flow associated with a flow medium in a flow channel of the device, and a controller component in electronic communication with the flow sensing component, the controller component configured to receive a flow indication from the flow sensing component, and in response to determining that the flow indication satisfies a zero condition threshold, apply an exponential smoothing function to adjust an output signal of the device with a variable alpha value.

Description

[0001] Cross Reference to Related Applications

[0002] This patent application claims priority to Indian Patent Application No. 202111058918, filed December 17, 2021, the contents of which are hereby incorporated by reference in their entirety. BACKGROUND

[0003] Flow sensors can be used to measure the flow and / or quantity of a flowing medium or gaseous substance and can be implemented in a variety of applications. Such flow sensors suffer from technical challenges and limitations. Through effort, ingenuity, and innovation, including the development of solutions in embodiments of the present disclosure, many of these identified problems have been addressed, many examples of which are described in detail herein. SUMMARY

[0004] Various embodiments described herein relate to methods, devices, and systems for turbulent flow noise removal for devices such as, for example, flow sensing devices.

[0005] According to various examples of the present disclosure, a device is provided. The device can include a flow sensing component configured to detect a flow associated with a flowing medium in a flow channel of the device, and a controller component in electronic communication with the flow sensing component, the controller component configured to receive a flow indication from the flow sensing component and, in response to determining that the flow indication satisfies a zero condition threshold, apply an exponential smoothing function to adjust an output signal of the device with a variable a value.

[0006] In some examples, the controller component is further configured to perform a compensation operation after applying the exponential smoothing function.

[0007] In some examples, the zero condition threshold is between -25 and +25 standard cubic centimeters per minute (SCCM).

[0008] In some examples, the device includes a mass flow sensor or a liquid flow sensor.

[0009] In some examples, the exponential smoothing function is applied to a digital front end of the device.

[0010] In some examples, the controller component includes an application specific integrated circuit (ASIC).

[0011] In some examples, the value of a is 1 outside of the zero condition threshold, and wherein the value of a is 0.3 at the zero condition.

[0012] According to various examples of the present disclosure, a method is provided. The method can include receiving, by a controller component, a flow indication from a flow sensing component configured to detect a flow of a flow medium in a flow channel of a device; and in response to determining, by the controller component, that the flow indication satisfies a zero condition threshold, applying an exponential smoothing function to adjust an output signal with a variable a value.

[0013] According to various examples of the present disclosure, a computer program product for filtering noise from an output signal of a device is provided. The computer program product can include at least one non-transitory computer-readable storage medium having computer-executable program code instructions embodied thereon, the computer-executable program code instructions comprising program code instructions configured to, when executed implement: receiving, from a flow sensing component, a flow indication associated with a flow medium in a flow channel of a device; and in response to determining that the flow indication satisfies a zero condition threshold, applying an exponential smoothing function to adjust an output signal with a variable a value.

[0014] The foregoing exemplary summary, as well as other exemplary objects and / or advantages of the present disclosure and means for materializing the same, will become more apparent from the following detailed description and accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0015] The description of the exemplary embodiments can be read in conjunction with the accompanying drawings. It will be appreciated that the elements, as shown in the drawings, are not necessarily drawn to scale unless otherwise specified. For example, the dimensions of some of the elements can be exaggerated relative to other elements unless otherwise specified. Embodiments incorporating teachings of the present disclosure are shown and described relative to the drawings presented herein and, as should be understood by those skilled in the art, the drawings are presented by way of illustration only and should not be taken as limiting the subject matter disclosed herein.

[0016] Figure 1 A schematic diagram depicting an exemplary device according to various embodiments of the present disclosure is shown.

[0017] Figure 2 A graph depicting exemplary measurement results according to various embodiments of the present disclosure is shown.

[0018] Figure 3 A graph depicting exemplary measurement results according to various embodiments of the present disclosure is shown.

[0019] Figure 4 A graph depicting exemplary measurement results according to various embodiments of the present disclosure is shown.

[0020] Figure 5 A graph depicting exemplary measurement results according to various embodiments of the present disclosure is shown.

[0021] Figure 6Graphs illustrating exemplary measurement results are shown depicting various embodiments according to the present disclosure; and

[0022] Figure 7 Flowcharts illustrating exemplary operations according to various embodiments of the present disclosure are shown. DETAILED DESCRIPTION

[0023] Some embodiments of the disclosure will be described below with reference to the accompanying drawings, in which some embodiments of the disclosure are shown, but not all embodiments. Indeed, these disclosures can be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Throughout the disclosure, like reference numerals refer to like elements.

[0024] The components illustrated in the figures represent components that can or can not be present in various embodiments of the disclosure described herein, such that embodiments can include fewer or more components than those shown in the figures, without departing from the scope of the disclosure. Some components can be omitted from one or more of the figures, or shown in dashed lines to illustrate that they can be present or absent, in order to make the components shown in the figures more visible.

[0025] The phrases “in an example embodiment,” “some embodiments,” “various embodiments,” and the like, generally mean that the particular feature, structure, or characteristic following the phrase can be included in at least one embodiment of the disclosure, and can be included in more than one embodiment of the disclosure, important with the understanding that such phrases are not necessarily referring to the same embodiment. Additionally, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the purview of one of ordinary skill in the art to effect such feature, structure, or characteristic in connection with other

[0026] The word “example” or “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other implementations.

[0027] If the specification states a component, feature, structure, or characteristic “may,” “might,” “can,” “shall,” “will,” “preferably,” “possibly,” “typically,” “options,” “for example,” “often,” or “may” (or other similar forms of language) be included or have a particular feature, then it is not mandatory or required that the component, feature, structure, or characteristic be included or have the particular feature. Such components, features, structures, or characteristics can be optionally included or excluded from some embodiments.

[0028] The term“electrically coupled” or“electronically communicating” in this disclosure can refer to two or more electronic elements (such as, but not limited to, example processing circuitry, communication modules, input / output modules memory, humidity sensing components, cooling elements, gas detection components) and / or circuitry connected by wired means (such as, but not limited to, electrically conductive wires or traces) and / or wireless means (such as, but not limited to, wireless networks, electromagnetic fields) such that data and / or information (e.g., electronic indications, signals) can be transmitted to and / or received from the electrically coupled electrical elements and / or circuitry.

[0029] The term“flow sensing device” refers to a means by which the flow (including, but not limited to, linear flow rate, non-linear flow rate, mass flow, and / or volumetric flow) of one or more flow media can be detected, measured, and / or identified. In this disclosure, the term“flow media” refers to a substance (such as, but not limited to, a liquid substance and / or a gaseous substance).

[0030] The term“flow path” can refer to a passageway through which a flow media can flow, traverse, or be transported. Example flow paths of this disclosure can be defined / form by and / or include a plurality of channels. In various examples of this disclosure, an example dimension of an example cross-section of an example flow channel can be a height of micrometers to hundreds of micrometers and a width of tens of micrometers to hundreds of micrometers. In various examples of this disclosure, a length of an example flow channel can be greater than one hundred micrometers.

[0031] The term“laminar flow” can be characterized by particles of a flow media following smooth paths in a flow channel with little or no mixing (i.e., high momentum diffusion and low momentum convection). In contrast, the term“turbulent flow” can be characterized by particles of a flow media experiencing irregular fluctuations or mixing.

[0032] The term“thermal conductivity” can refer to a measure of the ability of a substance (e.g., a gaseous substance such as air) to transfer energy (in the form of heat). Generally, a substance with a low thermal conductivity transfers heat at a lower rate than a substance with a high thermal conductivity. A measurement of thermal conductivity is necessary for a variety of sensor-based devices and methods. The transfer of heat can be in the form of“thermal conduction,” which can be defined as the transfer of energy due to microscopic particle collisions and / or electron movement across a temperature gradient. For purposes of calculating thermal conductivity, the energy transferred by a substance due to thermal conduction can also be referred to as the“heat conduction” of that substance. Heat conduction can be quantified by the following vector:

[0033] q(r, t)

[0034] where r represents a location associated with heat conduction, and t represents a time associated with heat conduction. Since heat flows from a high temperature location to a low temperature location, the heat conduction law (also known as Fourier’s law) states that the rate of heat transfer through a substance is proportional to the negative temperature gradient. Fourier’s law can be provided in the following tensor form:

[0035]

[0036] where q(r, t) is the heat conduction, and is the temperature gradient tensor. K is a second tensor of thermal conductivity (also known as the “thermal conductivity tensor”), which can be related to the thermal conductivity of a substance. Based on the tensor form of Fourier’s law described above, the thermal conductivity tensor K can be calculated based on the following:

[0037]

[0038] As shown in the equation above, the thermal conductivity tensor K can be affected by the temperature gradient tensor . In other words, the thermal conductivity of a gaseous substance can be affected by its temperature. The ideal gas equation (also known as the ideal gas law) states that the temperature of a gaseous substance can be affected by its pressure:

[0039] Py = nRT = Nk B T

[0040] where P is the pressure of the gaseous substance, T is the temperature of the gaseous substance, V is the volume of the gaseous substance, n is the amount of the gaseous substance, N is the number of gas molecules, R is the gas constant, and k B is the Boltzmann constant.

[0041] In some embodiments of the present disclosure, exemplary methods, devices, and systems can provide devices for calculating, for example but not limited to, the thermal conductivity of a gaseous substance, such as air, based on, for example but not limited to, Fourier’s law and the ideal gas equation described above.

[0042] Flow sensing devices can be used in a variety of applications, including medical and industrial applications. For example, flow sensing devices can be used in micropipetting, high performance liquid chromatography (HPLC) applications, drug delivery, respirators, ventilators, anesthesia machines, heating, ventilation, and air conditioning (HVAC) equipment, gas analyzers, leak detection equipment, etc. For example, flow sensing devices can be implemented in a drug delivery system to detect, measure, and / or identify the flow of a flow medium associated therewith. In another example, flow sensing devices can be implemented in automotive applications in order to determine the amount of air flow into an engine.

[0043] Referring now to Figure 1, an exemplary schematic diagram of an exemplary controller component 100 depicting an exemplary device according to various embodiments of the present disclosure is provided. Specifically, the exemplary controller component 100 includes an input / output circuit 105, a processing circuit 107, a memory circuit 109, and a communication circuit 111. Additionally, the controller component 100 is electrically coupled to and / or in electronic communication with a flow sensing component 101. The flow sensing component 101, the input / output circuit 105, the processing circuit 107, the memory circuit 109, and the communication circuit 111 can be electrically coupled such that they can transmit and / or exchange information and data via wired or wireless connections between each other.

[0044] The flow sensing component 101 can be or include at least one sensing element (e.g., a flow sensing element). As used herein, the term “sensing element” refers to a device / apparatus that measures or detects a characteristic associated with a location or environment surrounding the sensing element and can also indicate, record, and / or output a record of that characteristic. For example, the flow sensing component 101 can be or include a transducer for detecting and / or measuring an airflow rate that can be caused by, for example, heat transfer. In some embodiments, the flow sensing component 101 can include a microelectromechanical systems (MEMS) flow sensing die. The MEMS flow sensing die can include miniaturized mechanical and electromechanical components for detecting and / or measuring air flow, and these components can be fabricated (such as via a microfabrication process) to form functional circuitry on a block of semiconductor material, such as a die. In some embodiments, the flow sensing component 101 can include a heater resistor and at least one temperature sensing resistor disposed on a substrate. In some embodiments, the heater resistor can maintain a constant temperature (e.g., but not limited to, 150 °C). The heater resistor can be disposed between two temperature sensing resistors.

[0045] In some embodiments, the flow sensing element of the flow sensing component 101 can be positioned on a flow path of the gaseous substance. For example, the two temperature sensing resistors can be configured such that one of the resistors is upstream and the other is downstream. In such embodiments, the gaseous substance moves from the upstream temperature sensing resistor to the downstream temperature sensing resistor, passing the heater resistor. The gaseous substance can decrease the temperature detected by the upstream temperature sensing resistor and increase the temperature detected by the downstream temperature sensing resistor. Accordingly, the flow sensing component 101 can detect the airflow and / or calculate the airflow rate based on the heat transfer rates associated with the temperature sensing resistors.

[0046] Referring back to Figure 1The flow sensing component 101 can be electrically coupled to the processing circuit 107 and can provide one or more inputs to the processing circuit 107. For example, the flow sensing component 101 can be configured to transmit a first signal indicative of an air flow rate (or a flow rate of another gaseous substance such as a liquid) detected by the flow sensing component 101.

[0047] As used herein, the term “processing circuit” refers to one or more circuits that can be configured to perform processing functions and / or software instructions on one or more input signals to generate one or more output signals. In various embodiments of the present disclosure, the processing circuit 107 can perform processing functions and / or software instructions on signals received from the flow sensing component 101 to calculate a thermal conductivity of a gaseous substance based on, for example, but not limited to, Fourier’s Law and the Ideal Gas Equation described above.

[0048] In some embodiments, the processing circuit 107 can be implemented, for example, as various devices including one or more microprocessors with accompanying digital signal processors; one or more processors without accompanying digital signal processors; one or more coprocessors; one or more multi-core processors; one or more controllers; processing circuitry; one or more computers; various other processing elements / instruments including integrated circuits such as, for example, an ASIC or an FPGA, or some combination thereof. In some embodiments, the processing circuit 107 can include one or more processors. In one example embodiment, the processing circuit 107 can be configured to execute instructions stored in the memory circuit 109 or that can otherwise be accessed by the processing circuit 107. When executed by the processing circuit 107, these instructions can enable the controller component 100 to perform one or more functions as described herein. Whether the processing circuit 107 is configured by hardware or by firmware / software methods, or by some combination thereof, the processing circuit can include an entity capable of performing operations according to embodiments of the present application when appropriately configured. Thus, for example, when the processing circuit 107 is implemented as an ASIC, FPGA or the like, the processing circuit 107 can include specifically configured hardware for performing one or more operations described herein. In these examples, the ASICs are integrated circuits specially designed for a particular application. In some examples, the ASICs can be fully custom designed or semi-custom designed for the particular application of processing signals. In some examples, the ASICs can be programmable ASICs that allow reconfiguration of the circuit. In some embodiments, other suitable forms of the processing circuit 107 can be implemented. Alternatively, as another example, when the processing circuit 107 is implemented as an actuator of instructions (such as can be stored in the memory circuit 109), the instructions can specifically configure the processing circuit 107 to perform one or more algorithms and steps / operations described herein, such as those discussed with reference to Figure 7 the algorithms and steps / operations described herein. See, e.g., FIG. 1.Figure 1 The processing circuit 107 can be electrically coupled to the input / output circuit 105, the memory circuit 109, and / or the communication circuit 111.

[0049] As Figure 1 depicted, in some embodiments, the controller component 100 can include a memory circuit 109. The memory circuit 109 can be non-transitory and can include, for example, one or more volatile and / or non-volatile memories. The memory circuit 109 can be configured to store information and data, such as processing functions and / or software instructions. The memory circuit 109, together with the processing circuit 107, can cause the controller component 100 to perform various processing functions and / or software instructions in accordance with example embodiments of the present disclosure, including, for example, calculating the thermal conductivity of a gaseous substance. In some embodiments, the memory circuit 109 can include, for example, a volatile memory, a non-volatile memory, or some combination thereof. Although shown as a single memory in Figure 1 the various embodiments, the memory circuit 109 can include multiple memory components. In various embodiments, the memory circuit 109 can include, for example, a hard disk drive, a random access memory, a cache memory, a flash memory, a compact disk read-only memory (CD-ROM), a digital versatile disk read-only memory (DVD-ROM), an optical disk, a circuit configured to store information, or some combination thereof. The memory circuit 109 can be configured to store information, data, applications, instructions, or the like such that the controller component 100 can perform various functions in accordance with embodiments of the present disclosure. For example, in at least some embodiments, the memory circuit 109 is configured to buffer input data for processing by the processing circuit 107. Additionally or alternatively, in at least some embodiments, the memory circuit 109 is configured to store program instructions for execution by the processing circuit 107. The memory circuit 109 can store information in the form of static and / or dynamic information. The stored information can be stored and / or used by the controller component 100 when performing functions.

[0050] As Figure 1As depicted, in some embodiments, the controller component 100 can include a communication circuit 111. The communication circuit 111 can include, for example, a device or circuit embodied in hardware or a combination of hardware and software configured to receive data from and / or transmit data to a network and / or any other device, circuit, or module in communication with the controller component 100 and / or the flow sensing component 101. In this regard, the communication circuit 111 can include, for example, a network interface for enabling communication with a wired or wireless communication network. In some embodiments, the communication circuit 111 can be embodied as any means for receiving and / or transmitting data from / to another component or device, including in a circuit, hardware, computer program product, or combination thereof. The computer program product includes computer readable program instructions stored on a computer readable medium (e.g., the memory circuit 109) and executed by the controller component 100 (e.g., the processing circuit 107). In some embodiments, the communication circuit 111 (as with the other components discussed herein) can be at least partially implemented in / otherwise controlled by the processing circuit 107. In this regard, the communication circuit 111 can be in communication with the processing circuit 107, for example, via a bus. The communication circuit 111 can include, for example, an antenna, a transmitter, a receiver, a transceiver, a network interface card, and / or supporting hardware and / or firmware / software, and be used to establish communications with another device. The communication circuit 111 can be configured to receive and / or transmit any data that can be stored by the memory circuit 109 by using any protocol that can be used for communication between devices. The communication circuit 111 can additionally or alternatively be in communication with the input / output circuit 105, the memory circuit 109, and / or any other component of the controller component 100, for example, via a bus.

[0051] In some embodiments, the controller component 100 can include an input / output circuit 105. The input / output circuit 105 can be in communication with the processing circuit 107 to receive instructions input by a user and / or to provide audible, visual, mechanical, or other output to the user. As such, the input / output circuit 105 can include support devices such as a keyboard, a mouse, a display, a touch screen display, and / or other input / output mechanisms. Alternatively, at least some aspects of the input / output circuit 105 can be embodied on a device used by a user to communicate with the controller component 100. The input / output circuit 105 can be in communication with the memory circuit 109, the communication circuit 111, and / or any other component, for example, via a bus. One or more input / output circuits and / or other components can be included in the controller component 100.

[0052] In various examples, the flow sensing component 101 can generate a flow indication and transmit the flow indication to the processing circuit 107. Thus, the flow sensing component 101 and the controller component 100 can operate to generate a measurement indicative of a flow associated with a flow medium in a flow channel / path of a device (e.g., a flow sensing apparatus).

[0053] In Figure 1 the components 101, 105, 107, 109, and 111 can be described in terms of functional limitations, it is contemplated that a particular implementation necessarily includes the use of particular hardware. It is also contemplated that certain of these components 101, 105, 107, 109, and 111 can additionally include one or more similar or common pieces of hardware. For example, the flow sensing component 101 can additionally include processing circuitry such that the flow sensing component 101 can process signals generated by the flow sensing element to calculate thermal conductivity. In various examples, the controller component 100 can operate to generate and provide a measurement indicative of a flow of a flow medium within an example device (e.g., a flow sensing apparatus).

[0054] While the above description provides an example controller component 100, it is noted that the scope of the present disclosure is not limited to the above description. In some examples, the example controller component can include one or more additional and / or alternative elements, and / or can be configured / positioned differently than shown. Figure 1

[0055] As noted above, many flow sensing apparatuses suffer from technical challenges and limitations. As noted above, laminar flow can be characterized by particles of a flow medium following a smooth path in a flow channel with little or no mixing, while turbulent flow can be characterized by particles of a flow medium experiencing irregular fluctuations or mixing. In some examples, laminar flow can be achieved for a flow sensing apparatus based on a flow of a flow medium. For example, a flow sensing apparatus (e.g., a mass flow sensor) can include a flow tube that is designed to layer the flow in order to obtain accurate flow measurements. Similarly, a fluid flow sensor can include a specially designed flow tube that operates to reduce turbulence of a flow medium / fluid as it passes over a sensing element. In other words, ideally, a flow of a flow medium (e.g., air) should be laminar as it passes over a sensing element. In various examples, turbulent flow of a flow medium within an example flow sensing apparatus can cause a noise signal that can affect the accuracy of measurements generated by the flow sensing apparatus.

[0056] ​In some examples, there can be unwanted fluctuations in the output signal related to externally induced effects, or noise in the flow signal measured / detected by the flow sensing component (e.g., flow transducer), such as vibrations and / or pressure fluctuations that the example flow sensing device can be subject to, even in the case of laminar flow. These externally induced effects can cause observable jitter in the output signal. In a digital flow sensor, the flow transducer signal is digitized to a number represented in counts. As noted above, the noise, which is actually an unwanted signal induced by external effects such as vibrations or pressure fluctuations, can be observed as a number of peak-to-peak counts in the measurement data, where the magnitude of the noise depends on the flow rate of the flowing medium (e.g., air).

[0057] Referring now to Figure 2 , there is provided a graph 200 depicting example measurement results associated with an example device (e.g., flow sensing device) in accordance with various embodiments of the present disclosure.

[0058] As Figure 2 depicted, the x-axis represents a number of time instances. As depicted, for a first line 201 of the graph 200, the y-axis represents a signal measured in counts detected by the example device (e.g., flow sensing device). As depicted, the detected signal fluctuates / changes between approximately -40 counts and +40 counts, indicating that the signal is noisy. In some examples, the first line 201 of the graph can be associated with turbulent flow of the flowing medium.

[0059] As depicted, for a second line 203 of the graph, the y-axis also represents a signal measured in counts detected by the example device (e.g., flow sensing device). As depicted, the detected signal is stable and remains close to 0 counts over time, indicating that the signal is quiet. In some examples, the second line 203 of the graph can be associated with laminar flow of the flowing medium.

[0060] Referring now to Figure 3 , there is provided another graph 300 depicting example measurement results associated with an example device (e.g., flow sensing device) in accordance with various embodiments of the present disclosure.

[0061] As Figure 3As depicted, the x-axis represents air mass flow measured in standard cubic centimeters per minute (SCCM). As further depicted, for the first line 301 of the graph 300, the y-axis represents a signal showing peak-to-peak noise (measured in counts) detected by an example device (e.g., a flow sensing apparatus). As depicted, there is a minimum background noise of approximately 15 counts when the detected mass flow is between -125 SCCM and -25 SCCM and between +25 SCCM and +125 SCCM. As further depicted, the detected peak-to-peak noise is higher when the detected mass flow is between -25 and 25, between 20 and 105 counts, and peaks at 0 SCCM. Thus, Figure 3 As shown, the signal detected by an example device (e.g., a flow sensing apparatus) is noisier at zero condition / near zero condition when the flow of the flowing medium (e.g., air) is low and when no flow is detected (as depicted, at 0 SCCM along the x-axis).

[0062] In various examples, due to the physical parameters and / or intrinsic characteristics of the flow sensing apparatus, the signal detected by an example flow sensing apparatus is noisier at near zero condition (e.g., in the absence of detected flow / air flow) than at full scale (e.g., in the presence of higher detected flow / air flow). Thus, the performance of the flow sensing apparatus at near zero condition is critical to optimize the apparatus performance, which is due to the wider tolerance at full scale. In other words, the noise incident in the signal detected / generated by the flow sensing apparatus depends on the flow of the flowing medium, and is typically the greatest at zero condition at the location where zero point drift is observable. Attempts to reduce noise incident at zero condition / near zero condition (e.g., with input filtering) can result in undesirable increase in the response time of the flow sensing apparatus. Additionally, compensation operations typically performed to attenuate errors during measurement have proven insufficient to reduce incident noise at zero condition / zero point drift. Thus, any technique to reduce noise incident at zero condition must account for maintaining full scale performance without degrading the response time of the flow sensing apparatus.

[0063] To address the challenges and limitations associated with measuring flow, various examples of the present disclosure can provide example flow sensing apparatuses, devices, methods, computer program products, and systems. For example, various embodiments of the present disclosure provide digital front-end filtering techniques for turbulent flow noise removal.

[0064] In some examples, the present disclosure can provide an apparatus comprising a flow sensing component. An example flow sensing component can be configured to detect a flow rate associated with a flow medium in a flow channel of the apparatus; and a controller component in electronic communication with the flow sensing component, the controller component configured to receive a flow indication from the flow sensing component, and in response to determining that the flow indication satisfies a zero condition threshold, apply an exponential smoothing function to adjust an output signal of the apparatus with a variable a value. In some examples, the controller component is further configured to, after applying the exponential smoothing function, perform a compensation operation. In some examples, the zero condition threshold is between -25 and +25 standard cubic centimeters per minute (SCCM). In some examples, the apparatus comprises a mass flow sensor or a liquid flow sensor. In some examples, the exponential smoothing function is applied to a digital front end of the apparatus. In some examples, the controller component comprises an application specific integrated circuit (ASIC). In some examples, the value of a is 1 outside the zero condition threshold, and wherein the value of a is 0.3 at the zero condition.

[0065] In various embodiments of the present disclosure, a method for filtering noise from an output signal of an apparatus is provided. The method can comprise receiving, by a controller component, a flow indication from a flow sensing component configured to detect a flow rate of a flow medium in a flow channel of the apparatus; and in response to determining, by the controller component, that the flow indication satisfies a zero condition threshold, applying an exponential smoothing function to adjust the output signal with a variable a value. In some examples, the method further comprises performing, by the controller component, a compensation operation after applying the exponential smoothing function. In some examples, the zero condition threshold is between -25 SCCM and +25 SCCM. In some examples, the apparatus comprises a mass flow sensor or a liquid flow sensor. In some examples, the exponential smoothing function is applied to a digital front end of the apparatus. In some examples, the controller component comprises an ASIC. In some examples, the value of a is 1 outside the zero condition threshold, and wherein the value of a is 0.3 at the zero condition.

[0066] In various embodiments of the present disclosure, a computer program product for filtering noise from an output signal from a device is provided. The computer program product can include at least one non-transitory computer-readable storage medium having computer-executable program code instructions embodied thereon, the computer-executable program code instructions comprising program code instructions configured, when executed, to: receive, from a flow sensing component, a flow indication associated with a flow medium in a flow channel of a device; and in response to determining that the flow indication satisfies a zero condition threshold, apply an exponential smoothing function with a variable a value to condition the output signal. In some examples, the instructions are further configured to: perform a compensation operation after applying the exponential smoothing function. In some examples, the zero condition threshold is between -25 SCCM and +25 SCCM. In some examples, the device includes a mass flow sensor or a liquid flow sensor. In some examples, the exponential smoothing function is applied to a digital front end of the device. In some examples, the value of a is 1 outside the zero condition threshold, and wherein the value of a is 0.3 at the zero condition.

[0067] In various examples, filtering can be used to remove noise from a signal detected by a flow sensing device. Filtering can be performed at multiple locations along the signal chain. In some examples, noise can be removed by filtering before digitization (i.e., analog filtering), at the digital front end (i.e., filtering the raw digital transducer signal), or after digital compensation (i.e., output filtering of the data). Many conventional techniques require additional external components, thereby increasing the complexity and cost of the flow sensing device. Additionally, in some examples, these operations can decrease the response time of the flow sensing device.

[0068] By way of example, a moving average filter can be used to filter noise from an output signal from a flow sensing device. An example moving average filter can take N samples of input and determine an average of the N samples to produce a single output point that forms a smoother output. However, in many cases, using a moving average filter requires a buffer of many data points and the ability to perform calculations (e.g., division) quickly, which increases the computation time and complexity, thereby decreasing the response time of an example flow sensing device.

[0069] Using the systems, devices, and techniques disclosed herein, flow sensing devices configured for low flow applications, high flow applications, and combinations thereof are provided. Exemplary flow sensing devices are capable of measuring a wide range of media flow over several orders of magnitude with increased accuracy while reducing noise incidence at near zero conditions. Accordingly, some examples of the present disclosure can, for example, but not by way of limitation, improve the performance, sensitivity, accuracy, and / or drift of a flow sensing device. The techniques described herein include methods of digital filtering of flow sensing device / transducer signals that do not require additional hardware / external components. For example, firmware components can be added to exemplary flow sensing devices by programming filtering algorithms into the digital front end of the signal path. In some embodiments, a novel adaptive filtering mechanism is capable of adapting to varying filter parameters as a function of flow. At zero flow and ultra-low flow, the filter parameters are tuned high to improve zero performance, and at higher flows, the filter parameters are tuned low to not significantly affect the output at those flows.

[0070] According to some embodiments of the present disclosure, an exponential smoothing function can be applied to the digital front end to filter noise from the flow sensing device output signal. An exemplary exponential smoothing function can take a weighted average of the current input and the previous input, given by the following equation:

[0071] X i = a - x i + (1 - a) - X i-1

[0072] In the above equation:

[0073] a is the smoothing factor;

[0074] x i is the latest output value (not averaged);

[0075] X i-1 is the previous average output; and

[0076] X i is the average output at the current sample.

[0077] Similar to the deficiencies of the moving average filter described above, the exponential smoothing filter can reduce response time as a function of the value of a (a). For example, a low a value (i.e., a strong filter) can significantly reduce the response time of an exemplary flow sensing device.

[0078] Referring now to Figure 4 , a plot 400 is provided that depicts exemplary measurement results associated with exemplary devices and various filter configurations according to various embodiments of the present disclosure.

[0079] As Figure 4The x-axis, as depicted, represents the response time of an exemplary device (e.g., a flow sensing device) in milliseconds (ms). As depicted, for each of the multiple lines 401, 403, 405, 407, and 409, the y-axis represents the detected response curve for the multiple filter configurations associated with it. Figure 4 As further described herein, the response time / responsiveness of an exemplary device (e.g., a flow sensing device) can be defined as the time required for the measured output to reach 63.2% of its total step change / final value. In some embodiments, the target or optimal response time for the exemplary device (e.g., a flow sensing device) may be 3 ms.

[0080] like Figure 4 The time constant value / parameter described can be expressed as T. 63 Alternatively, in some examples, it can be represented as τ. This relationship is governed by the following first-order equation for linear time-invariant (LTI) systems:

[0081]

[0082] In the above equation:

[0083] T is the sensor output;

[0084] T1 is the initial sensor output, and T2 is the currently detected sensor output, such that (T2-T1) is a step change;

[0085] t is the time elapsed since T1; and

[0086] τ is a constant value for time.

[0087] For example, if the sensor reading moves from 0 SCCM to 1 SCCM, then the time constant T 63 This is the time required for the sensor reading to reach 63.2% or 0.632 SCCM of a 1 SCCM step change. In some examples, if the sampling rate of the exemplary device is 1 ms, the time constant value is...

[0088] As depicted by the first line 401 of graph 400, the response time of the exemplary device is 0.67 ms when the output of the exemplary device (e.g., a flow sensing device) is not filtered and the sampling rate is 1 ms.

[0089] As shown by the second line 403 of graph 400, the time constant value T is reached without filtering the output of the exemplary device (e.g., the flow sensing device). 63 The required response time is 1.5ms.

[0090] As further depicted by the third line 405 of the graph 400, in the case of using a moving average filter and a value of a of 0.5, a time constant value T 63 The response time required is 2.5 ms.

[0091] As further shown by the fourth line 407 of the graph 400, in the case of using a moving average filter and a value of a of 0.3, a time constant value T 63 The response time required is 3 ms.

[0092] As further shown by the fifth line 409 of the graph 400, in the case of using a moving average filter and a value of a of 0.1, a time constant value T 63 The response time required is 9.5 ms.

[0093] Thus, Figure 4 It is shown that, in order to meet a target flow response time / time constant value of 3 ms or less, the value of a should be greater than or equal to 0.3. However, as discussed herein, Figure 4 The exemplary techniques of

[0094] Referring now to Figure 5 there is provided a graph 500 depicting exemplary measurement results associated with an exemplary device in accordance with various embodiments of the present disclosure. The exemplary device can be configured to implement / apply an exponential smoothing function as described above.

[0095] As Figure 5 depicted, the x-axis represents air mass flow measured in SCCM. As further depicted, for the first line 501 of the graph 500, the y-axis represents a value of a for a moving average filter utilized by the exemplary device (e.g., flow sensing apparatus).

[0096] As Figure 5 depicted, when the detected mass flow is approximately between -125 SCCM and -25 SCCM and +25 SCCM and +125 SCCM, the value of a is 1, such that in these ranges, the full scale performance requiring minimal or no filtering will not be affected. As further depicted, when the detected mass flow is approximately between -25 SCCM and +25 SCCM, the value of a changes. For example, as shown, when the detected mass flow is approximately between -25 SCCM and +25 SCCM, the value of a is between 0.3 and 1. Thus, in various embodiments, the exemplary device (e.g., flow sensing apparatus) will meet a target flow response time T 63< 3ms. Thus, at full scale flow, where a = 1, no filtering operation will be performed at that flow (e.g., between -125 SCCM and -25 SCCM and between +25 SCCM and +125 SCCM) without a filtering operation. Additionally, at / around zero conditions, the value of a is equal to or near 0.3, providing a filtering operation at the most needed filtering operation. As further depicted, between -25 SCCM and +25 SCCM, the value of a is the inverse function of the noise versus flow function.

[0097] Thus, Figure 5 It is shown that the signal detected by an exemplary device (e.g., a flow sensing apparatus) can be filtered using a variable a filter such that the exemplary device will operate optimally at / around zero conditions without compromising the response of the device at full scale conditions.

[0098] Referring now to Figure 6 , a graph 600 is provided that depicts exemplary measurement results associated with an exemplary device (e.g., a flow sensing apparatus) in accordance with various embodiments of the present disclosure. The exemplary device can implement a variable a filter on the digital front end of the device, such as the variable a filter described above in connection with Figure 5 .

[0099] As Figure 6 depicted, the x-axis represents air mass flow measured in SCCM. As further depicted, for a first line 601 of the graph 600, the y-axis represents a signal showing peak to peak noise (measured in counts) detected by an exemplary device (e.g., a flow sensing apparatus). As depicted, there is a minimum noise floor of approximately 15 counts across the entire operating range of the exemplary device between -125 SCCM and +125 SCCM. Thus, Figure 6 It is shown that the signal detected by an exemplary device will be optimally operated across its operating range to effectively filter out noise at / around zero conditions and at full scale conditions.

[0100] Referring now to Figure 7 , a flowchart is provided that shows exemplary operations 700 in accordance with various embodiments of the present disclosure.

[0101] In some examples, the method 700 can be performed by processing circuitry (e.g., but not limited to, an application specific integrated circuit (ASIC) or a central processing unit (CPU)). In some examples, the processing circuitry can be electrically coupled to and / or in electronic communication with other circuitry of an exemplary device, such as, but not limited to, a flow sensing component, a memory circuit (such as, for example, a random access memory (RAM) for storing computer program instructions), and / or a display circuit (for presenting a reading on a display).

[0102] In some examples, one or more of the procedures described in Figure 5 One or more of the procedures described in

[0103] In some examples, embodiments can take the form of a computer program product on a non-transitory computer-readable storage medium having stored thereon computer readable program instructions (e.g., computer software). Any suitable computer readable storage medium can be utilized, including a non-transitory hard disk, a CD-ROM, flash memory, optical storage, or magnetic storage devices.

[0104] The example method 700 begins at step / operation 701. At step / operation 701, the processing circuitry (such as, but not limited to, the processing circuitry 107 of the controller component 100 shown above in connection with Figure 1 The processing circuitry receives a flow indication associated with a gaseous substance (e.g., air) in a flow channel / path of an example device / flow sensing apparatus (e.g., from a flow sensing component).

[0105] After step / operation 701, the example method 700 proceeds to step / operation 703. At step / operation 703, the processing circuitry determines whether the flow indication satisfies a zero condition threshold. In other words, the processing circuitry determines whether the detected flow is within a predetermined range / threshold of a zero condition. In some embodiments, the zero condition threshold can be a measure of air flow associated with the gaseous substance in the flow channel / path that is near or equal to 0 (e.g., within a predetermined threshold of 0 SCCM). In some embodiments, an operator of the device can select the zero condition threshold prior to operating the device.

[0106] In some embodiments, the processing circuit can determine that the humidity level indication does not satisfy the zero condition threshold if the detected air flow is equal to or within a predetermined range, such as between -25 SCCM and +25 SCCM. In the above example, if the detected air flow is +50 SCCM, the detected air flow does not satisfy the zero condition threshold because it is outside of the predetermined range of -25 SCCM and +25 SCCM. Thus, the method 700 will proceed to step / operation 707 and the processing circuit will proceed to perform the compensation operation. In the above example, if the detected air flow is -5 SCCM, the detected air flow satisfies the zero condition threshold because it is within the predetermined range of the zero condition threshold.

[0107] After step / operation 703, and in the event that the detected flow satisfies the zero condition threshold, the method 700 proceeds to step / operation 705. At step / operation 705, the processing circuit applies an exponential smoothing function with a variable a value, such as the exponential smoothing function described above with respect to Figure 5

[0108] After applying the exponential smoothing function at step / operation 705, at step / operation 707, the processing circuit performs the compensation operation. Thus, as discussed herein, with the digital front-end filtering technique, noise can be removed from the output signal of a device (e.g., a flow sensing apparatus), which preserves the performance of the device at full scale, while providing a strong filter at / near zero conditions.

[0109] While Figure 7 While the example method 700 is provided, it is noted that the scope of the present disclosure is not limited to the above description. In some examples, example processes in accordance with the present disclosure can include additional and / or alternative steps / operations.

[0110] Those skilled in the art will appreciate many modifications and variations to the illustrative embodiments described herein. It is intended that the present disclosure cover all such modifications and variations, provided they come within the scope of the following claims and their equivalents. Furthermore, although example embodiments are described in the context of certain example combinations of elements and / or functions, one skilled in the art will appreciate that other combinations of elements and / or functions are also possible. In this regard, for example, the methods described herein can be performed in the context of other suitable devices, and using other suitable methods. To the extent not already provided for by the preceding description, the following claims indicate the scope of the disclosure.​

Claims

1. An apparatus, comprising: a flow sensing component configured to detect a flow associated with a flow medium in a flow channel of the apparatus; and a controller component in electronic communication with the flow sensing component, wherein the controller component is configured to: receive an indication of the flow from the flow sensing component, and determine whether the flow indication satisfies a zero condition threshold, wherein the zero condition threshold is a measurement of the flow of the flow associated with the flow medium in the flow channel of the apparatus that is substantially near or equal to zero; adjust an output signal of the apparatus in response to applying an exponential smoothing function with a smoothing factor value (a).

2. The apparatus of claim 1, wherein the controller component is further configured to: perform a compensation operation after applying the exponential smoothing function.

3. The apparatus of claim 1, wherein the zero condition threshold is between -25 and +25 standard cubic centimeters per minute (SCCM).

4. The apparatus of claim 1, wherein the apparatus comprises a mass flow sensor or a liquid flow sensor.

5. The apparatus of claim 1, wherein the exponential smoothing function is applied to a digital front end of the apparatus.

6. The apparatus of claim 1, wherein the controller component comprises an application specific integrated circuit (ASIC).

7. The apparatus of claim 1, wherein the smoothing factor value (a) is 1 outside the zero condition threshold, and wherein the smoothing factor value (a) is 0.3 at the zero condition threshold.

8. A method for filtering noise from an output signal of an apparatus, the method comprising: receiving, by a controller component, a flow indication from a flow sensing component configured to detect a flow of a flow medium in a flow channel of the apparatus; and determining whether the flow indication satisfies a zero condition threshold, wherein the zero condition threshold is a measurement of the flow of the flow associated with the flow medium in the flow channel of the apparatus that is substantially near or equal to zero; adjusting the output signal of the apparatus in response to applying an exponential smoothing function with a smoothing factor value (a).

9. The method of claim 8, further comprising: performing, by the controller component, a compensation operation after applying the exponential smoothing function.

10. The method of claim 9, wherein the zero condition threshold is between -25 SCCM and +25 SCCM.

11. The method of claim 8, wherein the apparatus comprises a mass flow sensor or a liquid flow sensor.

12. The method of claim 8, wherein the exponential smoothing function is applied to a digital front end of the apparatus.

13. The method of claim 8, wherein the controller component comprises an application specific integrated circuit (ASIC).

14. The method of claim 8, wherein the smoothing factor value (a) is 1 outside the zero condition threshold, and wherein the smoothing factor value (a) is 0.3 at the zero condition threshold.

15. A non-transitory computer-readable storage medium for filtering noise from an output signal from a device, the non-transitory computer-readable storage medium having computer-executable program code instructions, the computer-executable program code instructions comprising program code instructions that, when executed, are configured to: receiving a flow indication from a flow sensing component, the flow indication associated with a flow medium in a flow channel of the device; and determine whether the flow indication satisfies a zero condition threshold, wherein the zero condition threshold is a measurement of a flow of the flow substantially near or equal to zero associated with a flow medium in a flow channel of the device; adjust the output signal in response to applying an exponential smoothing function with a smoothing factor value (a).

16. The non-transitory computer-readable storage medium of claim 11, wherein the instructions are further configured to: perform a compensation operation after applying the exponential smoothing function.

17. The non-transitory computer-readable storage medium of claim 11, wherein the zero condition threshold is between -25 and +25 standard cubic centimeters per minute (SCCM).

18. The non-transitory computer-readable storage medium of claim 11, wherein the device comprises a mass flow sensor or a liquid flow sensor.

19. The non-transitory computer-readable storage medium of claim 11, wherein the exponential smoothing function is applied to a digital front end of the device.

20. The non-transitory computer-readable storage medium of claim 11, wherein the smoothing factor value (a) is 1 outside the zero condition threshold, and wherein the smoothing factor value (a) is 0.3 at the zero condition threshold.

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