Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to testing and tuning device, more specifically, relates to an automobile radiator testing and tuning device based on machine learning techniques.
BACKGROUND OF THE DISCLOSURE
[0002] Automobile radiators play a crucial role in maintaining the engine’s optimal operating temperature by dissipating the heat generated during the engine's operation. Radiators are a vital component in a vehicle’s cooling system, designed to transfer the heat from the engine coolant to the surrounding air. Without a properly functioning radiator, the engine would overheat, leading to potential damage and decreased performance. Their ability to effectively manage heat is essential for the overall performance, fuel efficiency, and environmental compliance of modern vehicles.
[0003] The demand for more efficient and durable radiators has increased, prompting manufacturers to develop radiators that can withstand higher pressures, more extreme temperatures, and offer improved heat dissipation. To ensure that radiators meet these high standards, rigorous testing is required throughout their development and production stages. The test rig integrates advanced technologies to simulate these conditions and provide real-time feedback on the radiator’s performance. It allows for precise and repeatable testing, enabling manufacturers to compare different designs, materials, and configurations.
[0004] Traditional radiator test rigs face several limitations that impact their efficiency and accuracy in evaluating radiator performance. They often struggle to replicate real-world driving conditions, such as varying temperature, pressure, and airflow, leading to less reliable test results. Further, they lack modular design for different nanofluid compositions and radiator types. Furthermore, they require significant manual intervention, which increases the likelihood of human error and inconsistency in data collection. The lack of automation and flexibility further restricts the ability to test radiators under dynamic conditions or across a broad range of parameters. Many conventional test rigs also lack integrated data acquisition and analysis which it difficult to process large volumes of real-time data for performance analysis. Additionally, their limited compatibility with modern technologies, hampers their ability to adapt to the fast-paced nature of automotive innovation. These challenges, combined with high maintenance costs, slow testing cycles, and limited environmental control, make it difficult for traditional test rigs to meet the evolving demands of radiator development in the automotive industry.
[0005] The present invention offers several significant advantages over the prior art by develop an automobile radiator testing and tuning device. It is characterized by its modular design, cost-effectiveness, and ability to simulate real-world vehicle cooling conditions. Further, it integrates advanced automation and real-time data acquisition, allowing for more accurate, efficient, and consistent testing under dynamic, real-world conditions. The automated adjustment of several parameters that ensure the simulation of test rig over a wide range of operational scenarios, improving the reliability of the results. Additionally, the present invention is modular and enables easy access, remote monitoring, and advanced data analytics, which enhances decision-making and reduces manual intervention.
[0006] Further, the integration of modern technologies, in the present invention allows for continuous optimization of test conditions, leading to faster product development cycles and higher-quality radiators. These advancements make the test rig more adaptable, cost-effective, and capable of meeting the evolving demands of the automotive industry, ultimately delivering superior performance and reliability in radiator testing.
[0007] Thus, in light of the above-stated discussion, there exists a need for an automobile radiator testing and tuning device.
SUMMARY OF THE DISCLOSURE
[0008] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0009] According to illustrative embodiments, the present disclosure focuses on an automobile radiator testing and tuning device which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0010] An objective of the present disclosure is to develop an automobile radiator testing and tuning device which is characterized by its modular design, cost-effectiveness, and ability to simulate real-world vehicle cooling conditions.
[0011] Another objective of the present disclosure is to provide a test rig that can accurately simulate real-world driving conditions to ensure more reliable and representative radiator performance testing.
[0012] Another objective of the present disclosure is to integrate automation capabilities that reduce the need for manual intervention, allowing for consistent and repeatable test results.
[0013] Another objective of the present disclosure is to develop a test rig that enables real-time monitoring, data logging, and analysis, facilitating informed decision-making and optimization of radiator designs.
[0014] Another objective of the present disclosure is to reduce testing times and costs by automating processes, providing quicker feedback on radiator performance.
[0015] Yet another objective of the present disclosure is to create a flexible testing platform capable of adapting to different radiator designs, materials, and performance metrics, ensuring that it meets the specific needs of the automotive industry.
[0016] In light of the above, in one aspect of the present disclosure, an automobile radiator testing and tuning device is disclosed herein. The system comprises a framework designed to provide structural support to the device, wherein the framework further comprises a top base with a vertically attached testboard and adapted to provide space to place a radiator to be tested, a lower base placed underneath the top base and adapted to hold essential components of the device, a plurality of arms attached at the corners of the top base, connected to the lower base and adapted to provide stability and support. The device includes a sump placed over the lower base and adapted to store and manage the nanofluid in the device. The device also includes a heat source serves as an electric heating element and adapted to heat the nanofluid. The device also includes a temperature regulator affixed to the heat source and adapted to regulate temperature of the nanofluid. The device also includes a pump placed over the lower base and adapted to moves inlet nanofluid from the sump through the radiator. The device also includes a flow regulator affixed to the pump and adapted to regulate flow rate of the nanofluid. The device also includes a plurality of sensors configured to monitor temperature of the nanofluid, flow rate and heat dissipation. The device also includes a display screen adapted to display the output and status of the automobile radiator testing and tuning. The device also includes a communication network configured to enable communication within the device. The device also includes a microcontroller connected to the temperature regulator, the flow regulator, the display screen and the plurality of sensors via the communication network and configured to control and monitor the automobile radiator testing and tuning, wherein the microcontroller further comprises an input module configured to receive real-time data from the plurality of sensors, a pre-processing module configured to filter and normalize the received data for further analysis, a training and testing module configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset, a feature extraction module configured to identify and extract features from the processed data to support analysis and decision-making, an anomalies detection module configured to compare extracted features with the target parameters to identify deviations from expected performance using machine learning technique, an alert generation module configured to generate alert on the detection of anomalies, a feedback module configured to suggest corrective parameters to be adjusted based on the detected anomalies, an automation module configured to adjust, control and optimize the suggested parameters to improve accuracy and efficiency; and an output module configured to transmit anomalies, alerts and suggested corrective parameters with final test results to the display screen.
[0017] In one embodiment, the device further comprises an inlet pipeline adapted to supply the nanofluid from the sump to a radiator.
[0018] In one embodiment, the device further comprises an outlet pipeline adapted to carry the heated nanofluid out of the radiator.
[0019] In one embodiment, the device further comprises a power supply unit adapted to provide electrical energy to the device.
[0020] In one embodiment, the device further comprises a heat recovery unit adapted to capture and reuse waste heat to improve efficiency and sustainability of the nanofluid.
[0021] In one embodiment, the device further comprises a cloud database configured to store the parameters regarding the best radiator including performance, test conditions, sensor readings, and operating characteristics.
[0022] In one embodiment, the plurality of sensors comprises a temperature sensor, a nanofluid flow sensor, an airflow sensor.
[0023] In one embodiment, the microcontroller further comprises a data acquisition module configured to convert signals received by the input module to digital data.
[0024] In one embodiment, the alert generation module adapted to generate status of the testing process, detected anomalies and the need for user intervention in the tuning process.
[0025] In light of the above, in one aspect of the present disclosure, a method for automobile radiator testing and tuning is disclosed herein. The method includes monitoring temperature of the nanofluid, flow rate and heat dissipation via a plurality of sensors. The method also includes receiving real-time data from the plurality of sensors via an input module. The method also includes filtering and normalizing the received data for further analysis via a pre-processing module. The method also includes splitting the pre-processed data into training and testing datasets and train the transfer learning model on training dataset via a training and testing module. the method also includes identifying and extracting features from the pre-processed data to support analysis and decision-making via a feature extraction module. The method also includes comparing extracted features with the target parameters to identify deviations from expected performance using machine learning technique via an anomalies detection module. The method also includes generating alerts on the detection of anomalies via an alert generation module. The method also includes suggesting corrective parameters to be adjusted based on the detected anomalies via a feedback module. The method also includes adjusting, controlling and optimizing the suggested parameters to improve accuracy and efficiency via an automation module. The method also includes transmitting anomalies, alerts and suggested corrective parameters with final test results to the display screen via an output module.
[0026] These and other advantages will be apparent from the present application of the embodiments described herein.
[0027] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0028] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0029] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0030] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0031] FIG. 1 illustrates a perspective view of an automobile radiator testing and tuning device, in accordance with an embodiment of the present disclosure;
[0032] FIG. 2 illustrates a block diagram of a microcontroller, in accordance with an embodiment of the present disclosure; and
[0033] FIG. 3 illustrates a flow chart of a method, outlining the sequential steps for testing and testing of an automobile radiator, in accordance with an embodiment of the present disclosure.
[0034] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0035] The automobile radiator testing and tuning device is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0036] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure.
[0037] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0038] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0039] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0040] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0041] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a perspective view of the automobile radiator testing and tuning device 100, in accordance with an embodiment of the present disclosure.
[0042] The automobile radiator testing and tuning device 100 propose solving the problem with a modular, cost-effective, and adaptable approach towards testing nanofluids under practical conditions. The interchangeable components of the device 100 help test the different radiator designs, nanofluids, and flow rates. This enables controlled testing, real-time monitoring, and analysis to optimize nanofluid performance, advancing automotive cooling technology.
[0043] The device comprises a framework 102 which further comprises a top base 104, a vertically attached testboard 106, a lower base 108 and a plurality of arms 110. The device may include a sumo 112, a heat source 114, a temperature regulator 116, a pump 118, a flow regulator 120, a plurality of sensors 122, a display screen 124. The device 100 may include a communication network 126. The device 100 may include a microcontroller 200 which further comprises several modules. The device may include an inlet pipeline 128 and an outlet pipeline 130. The device 100 may include a power supply 132 and a heat recovery unit 134.
[0044] The framework 102 designed to provide structural support to the device 100, wherein the framework 102 further comprises the top base 104 with the vertically attached testboard 106 and adapted to provide space to place a radiator to be tested, the lower base 108 placed underneath the top base 104 and adapted to hold essential components of the device 100, the plurality of arms 110 attached at the corners of the top base 104, connected to the lower base 108 and adapted to provide stability and support.
[0045] In one embodiment of the present invention, the framework 102 may also include brackets to hold the radiator in position.
[0046] The sump 112 placed over the lower base 108 and adapted to store and manage the nanofluid in the device 100. It holds the nanofluid that circulate through the radiator during testing. This includes maintaining an adequate fluid level and which is important for a radiator’s performance to achieve high efficiency.
[0047] In one embodiment of the present invention, the nanofluids typically refer to coolants that consist of a base fluid mixed with nanoparticles made from various materials.
[0048] In one embodiment of the present invention, the nanofluids may include water based nanofluids, ethylene glycol based nanofluids, oil based nanofluids, graphene based nanofluids and nanofluids with carbon nanotubes.
[0049] The heat source 114 serves as an electric heating element and adapted to heat the nanofluid. The heat source 114 is attached to the sump 112 and linked with the microcontroller 200. This allows the simulation of real operating conditions by providing the required thermal load to test and tune the radiator's ability for absorption and heat dissipation.
[0050] In one embodiment of the present invention, the heat source 114 is controlled by the microcontroller 200 based on the temperature readings and the target temperature. When the temperature needs to be increased, the microcontroller 200 sends a signal to the heat source to increase the heat output. If the coolant exceeds the desired temperature, the microcontroller 200 instructs the heat source 114 to reduce its power or shut off.
[0051] The temperature regulator 116 affixed to the heat source 114 and adapted to regulate temperature of the nanofluid. It serves as the link between the microcontroller 200 and the heat source 114, ensuring that the heat source operates within the specified range. Further, the temperature regulator 116 uses feedback from temperature sensors 138 to ensure that the coolant temperature stays within the desired range. If the temperature deviates from the set point, the temperature regulator 138 adjusts the heat source 114 accordingly.
[0052] The pump 118 placed over the lower base 108 and adapted to moves inlet nanofluid from the sump 112 through the radiator. The pump 118 is responsible for circulating nanofluids through the radiator. It ensures that the fluid is continuously moving through the radiator to simulate heat absorption and dissipation.
[0053] In one embodiment of the present invention, a motor drives the pump 118, providing the mechanical energy required to circulate the fluid. Which is powered by the power supply unit 132 and is operated at different speeds, depending on the required flow rate for the testing and tuning.
[0054] The flow regulator 120 affixed to the pump 118 and adapted to regulate flow rate of the nanofluid. It is connected with the pump 118 and linked with the microcontroller 200. The microcontroller 200 adjusts the operation of the flow regulator 120 to maintain a precise flow rate.
[0055] In one embodiment of the present invention, the flow regulator 120 may include a motorized valve and a variable-speed pump.
[0056] In one embodiment of the present invention, the device 100 further comprises an inlet pipeline 128 adapted to supply the nanofluid from the sump 112 to a radiator.
[0057] In one embodiment of the present invention, the device 100 further comprises an outlet pipeline 130 adapted to carry the heated nanofluid out of the radiator.
[0058] The plurality of sensors 122 configured to monitor temperature of the nanofluid, flow rate and heat dissipation.
[0059] In one embodiment of the present invention, the plurality of sensors 122 comprises a temperature sensor 138, a nanofluid flow sensor 140, an airflow sensor 142.
[0060] In one embodiment of the present invention, the inlet pipeline 128 and the outlet pipeline 130 include the temperature sensor 138 and the nanofluid flow sensor 140 to monitor the conditions of the nanofluid entering the radiator. The airflow sensor 142 is placed over the brackets to monitor the heat dissipation.
[0061] The display screen 124 adapted to display the output and status of the automobile radiator testing and tuning. The display screen is affixed to the vertically attached testboard 106 and linked with the microcontroller 200 which displays the output generated by the microcontroller 200.
[0062] The communication network 126 configured to enable communication within the device 100. The communication network 126 facilitates the data transmission within the device 100.
[0063] In one embodiment of the present invention, the communication network 126 may include wired and wireless network.
[0064] The microcontroller 200 connected to the temperature regulator 116, the flow regulator 120, the display screen 124 and the plurality of sensors 122 via the communication network 126 and configured to control and monitor the automobile radiator testing and tuning, wherein the microcontroller 200 further comprises an input module 202, a pre-processing module, a training and testing module 208, a feature extraction module 210, an anomalies detection module 212, an alert generation module 214, a feedback module 216, an automation module 218, an output module 220.
[0065] The input module 202 configured to receive real-time data from the plurality of sensors 122. It is responsible for receiving and gathering signals from the plurality of sensors 122 into a structured format.
[0066] In one embodiment of the present invention, the microcontroller 200 further comprises a data acquisition module 204 configured to convert signals received by the input module 202 to digital data. The data acquisition module 204 converts analog signals into digital data using analog to digital convertor.
[0067] The pre-processing module 206 configured to filter and normalize the received data for further analysis. The main purpose of the pre-processing module 206 is to enhance the quality of the data and improve the performance and reliability of the microcontroller 200 decision-making process.
[0068] The training and testing module 208 configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset.
[0069] The feature extraction module 210 configured to identify and extract features from the processed data to support analysis and decision-making. The feature extraction module 210 use prior knowledge, domain expertise, and statistical techniques to select important features from the processed data for further analysis.
[0070] The anomalies detection module 212 configured to compare extracted features with the target parameters to identify deviations from expected performance using machine learning technique. This module 212 is responsible for identifying deviations from expected performance based on extracted features. It compares the extracted features with predefined target parameters that represent normal operation using machine learning techniques such as classification models such as decision trees, support vector machines.
[0071] In one embodiment of the present invention, the anomalies may include sudden temperature spikes, inconsistent flow rates, and unusual airflow and heat dissipation.
[0072] In one embodiment of the present invention, the device 100 further comprises a cloud database 136 configured to store the parameters regarding the best radiator including performance data, test conditions, sensor readings, and operating characteristics. It stores critical performance data, sensor readings and test conditions.
[0073] In one embodiment of the present invention, the performance data may include optimal operational conditions for radiators based on tests conducted in various environments. The test conditions may include external factors like ambient temperature, humidity and specific model characteristics. The sensor readings may include continuous data from temperature, flow rate, and airflow.
[0074] The alert generation module 214 configured to generate alerts on the detection of anomalies. Once the anomalies detection module 212 identifies deviations from expected performance, the alert generation module 214 activates to notify the user. The generated alerts are visible on the display screen 124.
[0075] In one embodiment of the present invention, the alert generation module 214 adapted to generate status of the testing process, detected anomalies and the need for user intervention in the tuning process.
[0076] The feedback module 216 configured to suggest corrective parameters to be adjusted based on the detected anomalies. When an anomaly is detected, the feedback module 216 analyses the root cause and recommends corrective parameters to adjust, such as changing nanofluid flow speed, adjusting the radiator fan speed and altering fluid temperatures. The suggestions are to be informed using machine learning models, which have learned optimal adjustment strategies from historical test results.
[0077] The automation module 218 configured to adjust, control and optimize the suggested parameters to improve accuracy and efficiency. This module 218 allows the device 100 to automatically adjust and optimize parameters to improve accuracy and efficiency based on the feedback provided by the feedback module 216. It instructs the temperature regulator 116 and the flow regulator 120 to operate within optimal parameters, making real-time adjustments as required.
[0078] The output module 220 configured to transmit anomalies, alerts and suggested corrective parameters with final test results to the display screen 124.
[0079] In one embodiment of the present invention, the device 100 further comprises a power supply unit 132 adapted to provide electrical energy to the device 100.
[0080] In one embodiment of the present invention, the device 100 further comprises a heat recovery unit 134 adapted to capture and reuse waste heat to improve efficiency and sustainability of the nanofluid.
[0081] FIG. 2 illustrates a block diagram of the microcontroller 200, in accordance with an embodiment of the present disclosure.
[0082] The automobile radiator testing and tuning device 100 involves the microcontroller 200 to ensure optimal radiator performance. The input module 202 receives signals from the plurality of sensors 122. The data acquisition module 204 collects received signals and convert into the digital data, which then pre-processed to remove noise and standardized for analysis via the pre-processing module 206. The training and testing module 208 splits the pre-processed data into the training and testing datasets. The feature extraction module 210 extract features from the pre-processed data, and the anomaly detection module 212 compares this information with target parameters or historical data stored in the cloud database 136 to identify any deviations. If anomalies are detected, alerts are generated through the alert generation module 214, and the feedback module 216 suggests corrective actions. The automation module 218 adjusts parameters such as nanofluid temperature and flow rate, fan speed to restore optimal conditions. The output module 220 displays test results and anomalies on the display screen 124, ensuring the operator is informed.
[0083] FIG. 3 illustrates a flow chart of the method 300, outlining the sequential steps for testing and testing of the automobile radiator, in accordance with an embodiment of the present disclosure.
[0084] At step 302, the device 100 monitors temperature of the nanofluid, flow rate and heat dissipation via the plurality of sensors 122.
[0085] At step 304, the device 100 receives real-time data from the plurality of sensors 122 via the input module 202.
[0086] At step 306, the device 100 filters and normalizes the received data for further analysis via the pre-processing module 206.
[0087] At step 308, the device 100 splits the pre-processed data into training and testing datasets and train the transfer learning model on training dataset via the training and testing module 208.
[0088] At step 310, the device 100 identify and extract features from the pre-processed data to support analysis and decision-making via the feature extraction module 210.
[0089] At step 312, the device 100 compares extracted features with the target parameters to identify deviations from expected performance using machine learning technique via the anomalies detection module 212.
[0090] At step 314, the device 100 generates alerts on the detection of anomalies via the alert generation module 214.
[0091] At step 316, the device 100 suggests corrective parameters to be adjusted based on the detected anomalies via the feedback module 216.
[0092] At step 318, the device 100 adjusts, control and optimize the suggested parameters to improve accuracy and efficiency via the automation module 218.
[0093] At step 320, the device 100 transmits anomalies, alerts and suggested corrective parameters with final test results to the display screen 124 via the output module 220.
[0094] The best mode of operation of the present invention, the sump 112 containing nanofluids are supplied to the radiator under test through the pump 118, which is connected to the inlet pipeline 128. The temperature and flow rate of the nanofluid are continuously monitored by temperature sensors 138 and flow rate sensors 140 placed within the inlet pipeline 128. Also, the airflow rate is monitored using airflow sensor 140 attached to the brackets of the radiator. The input module 202 receives these signals from the plurality of sensors 122. The data acquisition module 204 collects the collected signals and converts them into digital data which are further processed by the pre-processing module 206 that removes any noise and standardizes the data for analysis. Further, the training and testing module 208 splits it into separate training and testing datasets for model evaluation. The feature extraction module 210 extracts relevant features from the data and are compared with predefined target parameters stored in the cloud database 136 via the anomaly detection module 212 to identify any deviations. If an anomaly is detected, the alert generation module 214 triggers an alert to notify the operator of the issue. In response, the feedback module 216 suggests corrective actions, such as adjusting the nanofluid temperature, flow rate, and fan speed to restore the optimal operating conditions. The automation module 218 takes control to automatically implement these adjustments in real time. Finally, the output module 220 presents the results of the test, including any detected anomalies and corrective actions, on the display screen 124.
[0095] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0096] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0097] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0098] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0099] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An automobile radiator testing and tuning device (100), the device (100) comprising:
a framework (102) designed to provide structural support to the device (100), wherein the framework (102) further comprises:
a top base (104) with a vertically attached testboard (106) and adapted to provide space to place a radiator to be tested;
a lower base (108) placed underneath the top base (104) and adapted to hold essential components of the device (100);
a plurality of arms (110) attached at the corners of the top base (104), connected to the lower base (108) and adapted to provide stability and support;
a sump (112) placed over the lower base (108) and adapted to store and manage the nanofluid in the device (100);
a heat source (114) serves as an electric heating element and adapted to heat the nanofluid;
a temperature regulator (116) affixed to the heat source (114) and adapted to regulate temperature of the nanofluid;
a pump (118) placed over the lower base (108) and adapted to moves inlet nanofluid from the sump (112) through the radiator;
a flow regulator (120) affixed to the pump (118) and adapted to regulate flow rate of the nanofluid;
a plurality of sensors (122) configured to monitor temperature of the nanofluid, flow rate and heat dissipation;
a display screen (124) adapted to display the output and status of the automobile radiator testing and tuning;
a communication network (126) configured to enable communication within the device (100);
a microcontroller (200) connected to the temperature regulator (116), the flow regulator (120), the display screen (124) and the plurality of sensors (122) via the communication network (126) and configured to control and monitor the automobile radiator testing and tuning, wherein the microcontroller (200) further comprises:
an input module (202) configured to receive real-time data from the plurality of sensors (122);
a pre-processing module (206) configured to filter and normalize the received data for further analysis;
a training and testing module (208) configured to split the pre-processed data into training and testing datasets and train the transfer learning model on training dataset;
a feature extraction module (210) configured to identify and extract features from the processed data to support analysis and decision-making;
an anomalies detection module (212) configured to compare extracted features with the target parameters to identify deviations from expected performance using machine learning technique;
an alert generation module (214) configured to generate alerts on the detection of anomalies;
a feedback module (216) configured to suggest corrective parameters to be adjusted based on the detected anomalies;
an automation module (218) configured to adjust, control and optimize the suggested parameters to improve accuracy and efficiency; and
an output module (220) configured to transmit anomalies, alerts and suggested corrective parameters with final test results to the display screen (124).
2. The device (100) as claimed in claim 1, wherein the device (100) further comprises an inlet pipeline (128) adapted to supply the nanofluid from the sump (112) to a radiator.
3. The device (100) as claimed in claim 1, wherein the device (100) further comprises an outlet pipeline (130) adapted to carry the heated nanofluid out of the radiator.
4. The device (100) as claimed in claim 1, wherein the device (100) further comprises a power supply unit (132) adapted to provide electrical energy to the device (100).
5. The device (100) as claimed in claim 1, wherein the device (100) further comprises a heat recovery unit (134) adapted to capture and reuse waste heat to improve efficiency and sustainability of the nanofluid.
6. The device (100) as claimed in claim 1, wherein the device (100) further comprises a cloud database (136) configured to store the parameters regarding the best radiator including performance, test conditions, sensor readings, and operating characteristics.
7. The device (100) as claimed in claim 1, wherein the plurality of sensors (122) comprises a temperature sensor (138), a nanofluid flow sensor (140), an airflow sensor (142).
8. The device (100) as claimed in claim 1, wherein the microcontroller (200) further comprises a data acquisition module (204) configured to convert signals received by the input module (202) to digital data.
9. The device (100) as claimed in claim 1, wherein the alert generation module (214) adapted to generate status of the testing process, detected anomalies and the need for user intervention in the tuning process.
10. A method (300) for automobile radiator testing and tuning, the method (300) comprising:
monitoring temperature of the nanofluid, flow rate and heat dissipation via a plurality of sensors (122);
receiving real-time data from the plurality of sensors (122) via an input module (202);
filtering and normalizing the received data for further analysis via a pre-processing module (206);
splitting the pre-processed data into training and testing datasets and train the transfer learning model on training dataset via a training and testing module (208);
identifying and extracting features from the pre-processed data to support analysis and decision-making via a feature extraction module (210);
comparing extracted features with the target parameters to identify deviations from expected performance using machine learning technique via an anomalies detection module (212);
generating alerts on the detection of anomalies via an alert generation module (214);
suggesting corrective parameters to be adjusted based on the detected anomalies via a feedback module (216);
adjusting, controlling and optimizing the suggested parameters to improve accuracy and efficiency via an automation module (218); and
transmitting anomalies, alerts and suggested corrective parameters with final test results to the display screen (124) via an output module (220).