1. A real-time casson hydromagnetic flow optimization system (100), the system (100) comprising: a user device (102) configured to collect input data from a user through a user interface (104); a communication network (106) configured to establish a communication link for data transmission within the system (100); a processing unit (108) connected to the user device (102) via the communication network (106) and configured to process real-time data for casson hydromagnetic flow optimization, wherein the processing unit (108) further comprises: a data input module (110) configured to receive real-time data from the user device (102); a preprocessing module (112) configured to clean, normalize, and validate the received data to enhance data quality; a feature extraction module (114) configured to extract relevant features from the pre-processed data; a physics calculation module (116) configured to analyse the extracted features and compute fluid flow parameters; a behaviour learning module (118) configured to learn fluid behaviour under varying operating conditions based on the computed fluid flow parameters; a result prediction module (120) configured to predict fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns; an optimization module (122) configured to optimize operating parameters by adjusting control variables based on the predicted results; and an output module (128) configured to transmit optimized flow parameters, predicted flow behaviour, and performance metrics to the user device (102).

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (130) configured to store and manage processed data, simulation outputs, predicted flow behaviour, and optimized parameters for real-time access and analysis.

3. The system (100) as claimed in claim 1, wherein the user device (102) configured to collect input data including fluid and magnetic properties, boundary conditions, performance metrics, and control settings.

4. The system (100) as claimed in claim 1, wherein the feature extraction module (114) configured to extract relevant features including rheological, thermal, magnetic, and geometric characteristics from the pre-processed data.

5. The system (100) as claimed in claim 1, wherein the physics calculation module (116) configured to calculate fluid flow parameters including velocity distribution, pressure distribution, temperature profile, magnetic field intensity, shear stress, and hydromagnetic flow characteristics using computational fluid dynamics.

6. The system (100) as claimed in claim 1, wherein the behaviour learning module (118) configured to learn fluid behaviour under varying operating conditions including changes in velocity distribution, pressure gradients, temperature variations, magnetic field intensity, and boundary conditions using artificial intelligence-based surrogate model.

7. The system (100) as claimed in claim 1, wherein the optimization module (122) configured to optimize operating parameters by adjusting control variables based on the predicted results using at least one optimization technique including reinforcement learning and genetic algorithms.

8. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises a validation module (124) configured to selectively execute high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes.

9. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises the feedback module (126) configured to monitor the system (100) performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance.

10. A method (200) for real-time casson hydromagnetic flow optimization system (100), the method (200) comprising: collecting input data from a user through a user interface (104) via a user device (102); establishing a communication link for data transmission within the system (100) via a communication network (106); processing real-time data for casson hydromagnetic flow optimization via a processing unit (108), comprising several modules; receiving real-time data from the user device (102) via a data input module (110); cleaning, normalizing, and validating the received data to enhance data quality via a preprocessing module (112); extracting relevant features from the pre-processed data via a feature extraction module (114); analysing the extracted features and compute fluid flow parameters via a physics calculation module (116); learning fluid behaviour under varying operating conditions based on the computed fluid flow parameters via a behaviour learning module (118); predicting fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns via a result prediction module (120); optimizing operating parameters by adjusting control variables based on the predicted results via an optimization module (122); executing high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes via a validation module (124); monitoring the system (100) performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance via a feedback module (126); transmitting optimized flow parameters, predicted flow behaviour, and performance metrics to the user device (102) via an output module (128); and displaying optimized flow parameters, predicted flow behaviour, and performance metrics on the user interface (104) of the user device (102).

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to fluid flow simulation and optimization systems, more specifically, relates to a real-time casson hydromagnetic flow optimization system based on artificial intelligence–augmented computational fluid dynamics.
BACKGROUND OF THE DISCLOSURE
[0002] Fluid flow involving non-Newtonian yield-stress materials has significant importance in various industrial and scientific applications. Casson-type fluids, which exhibit a finite yield stress before flow begins, are commonly encountered in polymer processing, metallurgical operations, biomedical systems, food engineering, and coating technologies. When such fluids are subjected to magnetic fields, their flow behaviour becomes more complex due to the interaction between electromagnetic forces and viscous effects. Accurate modelling of such hydromagnetic flows is essential for improving heat transfer efficiency, enhancing process stability, and ensuring optimal system performance in aerospace cooling systems, fusion reactors, material processing units, and biomedical devices.
[0003] Traditional approaches for analysing magnetically influenced non-Newtonian fluid flows predominantly rely on numerical simulation techniques based on discretized solutions of governing conservation equations. Although such simulation-based frameworks are capable of producing physically accurate and high-resolution representations of velocity, pressure, temperature, and electromagnetic field distributions, they are inherently computationally intensive. The resolution of coupled nonlinear equations, particularly in the presence of yield-stress behaviour and electromagnetic interactions, demands significant processing power, memory allocation, and extended computational time. As a result, these methods are typically executed in high-performance computing environments and are not well-suited for rapid or time-sensitive applications. In conventional practice, parameter tuning and performance enhancement are achieved through iterative trial-and-error procedures, where multiple simulation runs are conducted with varying control parameters until acceptable results are obtained. This repetitive process substantially increases computational burden and delays decision-making. Moreover, most commercial computational platforms and Multiphysics environments operate primarily in offline modes, meaning that simulations must be completed before analysis and optimization occurs. Such systems are not inherently designed to adapt dynamically to rapidly changing magnetic fields, thermal gradients, inlet conditions, or geometric variations. Furthermore, existing optimization techniques are commonly implemented in batch-processing formats, requiring manual configuration, post-processing analysis, and human supervision. These workflows lack integrated, automated feedback mechanisms capable of continuously monitoring system performance and adjusting operational parameters in response to evolving conditions. Consequently, traditional systems do not provide real-time responsiveness, autonomous control, or seamless integration between simulation and optimization processes, thereby limiting their effectiveness in dynamic industrial and scientific environments.
[0004] The present invention overcomes the aforementioned limitations by providing an enhanced computational framework designed to improve the efficiency, adaptability, and responsiveness of simulation-based fluid flow analysis. The system enables rapid and accurate prediction of complex hydromagnetic non-Newtonian flow behaviour while substantially reducing computational burden compared to conventional approaches. The invention further facilitates dynamic modification of operational parameters under varying boundary and environmental conditions, thereby supporting responsive and intelligent flow management. Moreover, the integrated architecture enables continuous performance evaluation and adaptive optimization, enhancing reliability, scalability, and operational effectiveness across diverse industrial and scientific applications where timely decision-making and high-performance operation are essential.
SUMMARY OF THE DISCLOSURE
[0005] 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.
[0006] According to illustrative embodiments, the present disclosure focuses on a real-time casson hydromagnetic flow optimization system which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0007] An objective of the present disclosure is to create a system that enables real-time analysis and optimization of hydromagnetic flow of non-Newtonian yield-stress fluids.
[0008] Another objective of the present disclosure is to design a system that reduces computational time and processing overhead associated with conventional simulation-based approaches.
[0009] Another objective of the present disclosure is to introduce a system that provides rapid prediction of key flow parameters including velocity, pressure, temperature, and electromagnetic field distributions.
[0010] Another objective of the present disclosure is to create a system that dynamically adjusts operational parameters in response to changing magnetic, thermal, and boundary conditions.
[0011] Another objective of the present disclosure is to design a system that integrates simulation, prediction, and optimization within a unified and automated computational framework.
[0012] Yet another objective of the present disclosure is to introduce a system that enables continuous performance monitoring and adaptive optimization for improved efficiency, stability, and scalability across industrial and scientific applications.
[0013] In light of the above, in one aspect of the present disclosure, a real-time casson hydromagnetic flow optimization system is disclosed herein. The system comprises a user device configured to collect input data from a user through a user interface. The system includes a communication network configured to establish a communication link for data transmission within the system. The system further includes a processing unit connected to the user device via the communication network and configured to process real-time data for casson hydromagnetic flow optimization, wherein the processing unit further comprises a data input module configured to receive real-time data from the user device, a preprocessing module configured to clean, normalize, and validate the received data to enhance data quality, a feature extraction module configured to extract relevant features from the pre-processed data, a physics calculation module configured to analyse the extracted features and compute fluid flow parameters, a behaviour learning module configured to learn fluid behaviour under varying operating conditions based on the computed fluid flow parameters, a result prediction module configured to predict fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns, an optimization module configured to optimize operating parameters by adjusting control variables based on the predicted results, an output module configured to transmit optimized flow parameters, predicted flow behaviour, and performance metrics to the user device.
[0014] In one embodiment, the system further comprises a cloud database configured to store and manage processed data, simulation outputs, predicted flow behaviour, and optimized parameters for real-time access and analysis.
[0015] In one embodiment, the user device configured to collect input data including fluid and magnetic properties, boundary conditions, performance metrics, and control settings.
[0016] In one embodiment, the feature extraction module configured to extract relevant features including rheological, thermal, magnetic, and geometric characteristics from the pre-processed data.
[0017] In one embodiment, the physics calculation module configured to calculate fluid flow parameters including velocity distribution, pressure distribution, temperature profile, magnetic field intensity, shear stress, and hydromagnetic flow characteristics using computational fluid dynamics.
[0018] In one embodiment, the behaviour learning module configured to learn fluid behaviour under varying operating conditions including changes in velocity distribution, pressure gradients, temperature variations, magnetic field intensity, and boundary conditions using artificial intelligence-based surrogate model.
[0019] In one embodiment, the optimization module configured to optimize operating parameters by adjusting control variables based on the predicted results using at least one optimization technique including reinforcement learning and genetic algorithms.
[0020] In one embodiment, the processing unit further comprises a validation module configured to selectively execute high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes.
[0021] In one embodiment, the processing unit further comprises a feedback module configured to monitor the system performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance.
[0022] In light of the above, in one aspect of the present disclosure, a method for real-time casson hydromagnetic flow optimization system is disclosed herein. The method comprises collecting input data from a user through a user interface via a user device. The method includes establishing a communication link for data transmission within the system via a communication network. The method further includes processing real-time data for casson hydromagnetic flow optimization via a processing unit, comprising several modules. The method also includes receiving real-time data from the user device via a data input module. Furthermore, the method includes cleaning, normalizing, and validating the received data to enhance data quality via a preprocessing module. Moreover, the method includes extracting relevant features from the pre-processed data via a feature extraction module. The method further includes analysing the extracted features and compute fluid flow parameters via a physics calculation module. The method also includes learning fluid behaviour under varying operating conditions based on the computed fluid flow parameters via a behaviour learning module. Furthermore, the method includes predicting fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns via a result prediction module. Moreover, the method includes optimizing operating parameters by adjusting control variables based on the predicted results via an optimization module. The method further includes executing high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes via a validation module. The method also includes monitoring the system performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance via a feedback module. Furthermore, the method includes transmitting optimized flow parameters, predicted flow behaviour, and performance metrics to the user device via an output module. At last, the method includes displaying optimized flow parameters, predicted flow behaviour, and performance metrics on the user interface of the user device.
[0023] These and other advantages will be apparent from the present application of the embodiments described herein.
[0024] 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.
[0025] 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
[0026] 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.
[0027] 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:
[0028] FIG. 1 illustrates a block diagram of a real-time casson hydromagnetic flow optimization system, in accordance with an embodiment of the present disclosure; and
[0029] FIG. 2 illustrates a flow chart of a method, outlining the sequential steps for the real-time casson hydromagnetic flow optimization system, in accordance with an embodiment of the present disclosure.
[0030] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0031] The real-time casson hydromagnetic flow optimization system 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
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0037] Referring now to FIG. 1 and FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of a real-time casson hydromagnetic flow optimization system 100, in accordance with an embodiment of the present disclosure.
[0038] The system 100 may include a user device 102. The system 100 may include a communication network 106. The system 100 may include a processing unit 108, which further comprises a data input module 110, a preprocessing module 112, a feature extraction module 114, a physics calculation module 116, a behaviour learning module 118, a result prediction module 120, an optimization module 122, a validation module 124, a feedback module 126, an output module 128. The system 100 may include a cloud database 130.
[0039] The user device 102 configured to collect input data from a user through a user interface 104. The user device 102 includes but not limited to a smartphone, tablet, computer and any other wearable device capable of data processing and user interaction.
[0040] In one embodiment of the present invention, the user device 102 configured to collect input data including fluid and magnetic properties, boundary conditions, performance metrics, and control settings.
[0041] In one embodiment of the present invention, the user device 102 is configured to serve as the primary interface between the user and the real-time casson hydromagnetic flow optimization system 100. The user interface 104 may include graphical elements such as touch screens and may also provide visualization of predicted fluid flow parameters, performance metrics, and optimized control settings.
[0042] In one embodiment of the present invention, the user device 102 may allow the user to receive feedback and optimized flow parameters from the system 100. This enables interactive control of the system 100 and allows the user to make informed decisions based on real-time predictions and optimization results.
[0043] The communication network 106 configured to establish a communication link for data transmission within the system 100. The communication network 106 may include but not be limited to wired and wireless networks.
[0044] In one embodiment of the present invention, the communication network 106 includes wireless networks including but not limited to Bluetooth and wireless fidelity.
[0045] In one embodiment of the present invention, the system 100 further comprises the cloud database 130 configured to store and manage processed data, simulation outputs, predicted flow behaviour, and optimized parameters for access and analysis. The cloud database 130 supports secure storage, remote access, and easy sharing of data, ensuring that the system 100 operates efficiently and adaptively in real-time across different applications.
[0046] In one embodiment of the present invention, the cloud database 130 allows real-time access to this data by the processing unit 108 and the user device 102, enabling instant retrieval, analysis, and updating of information.
[0047] The processing unit 108 connected to the user device 102 via the communication network 106 and configured to process real-time data for casson hydromagnetic flow optimization.
[0048] In one embodiment of the present invention, the processing unit 108 may include but not be limited to a microcontroller, a microprocessor, a central processing unit, and many more.
[0049] In one embodiment of the present invention, the processing unit 108 acts as the core computational component of the system 100, responsible for real-time analysis, prediction, and optimization of casson hydromagnetic flow.
[0050] In one embodiment of the present invention, the processing unit 108 ensures scalability, flexibility, and high computational efficiency, making the system 100 suitable for applications requiring real-time control and dynamic optimization of complex casson hydromagnetic flows.
[0051] The data input module 110 configured to receive real-time data from the user device 102. The data input module 110 serves as the initial entry point of real-time data from the user device 102 and is responsible for securely receiving, validating, and organizing the data for subsequent processing by downstream modules.
[0052] In one embodiment of the present invention, the data input module 110 also performs basic filtering, timestamping, and prioritization of incoming data streams.
[0053] In one embodiment of the present invention, the data input module 110 ensures the system 100 handles multiple real-time inputs efficiently, allowing accurate and timely analysis for real-time casson hydromagnetic flow optimization.
[0054] The preprocessing module 112 configured to clean, normalize, and validate the received data to enhance data quality. The preprocessing module 112 cleans the data by removing errors, normalizes it to a standard format, and validates it to ensure accuracy.
[0055] In one embodiment of the present invention, the preprocessing module 112 reduces noise, handles missing values, and organizes the data so that downstream modules process it efficiently.
[0056] In one embodiment of the present invention, the preprocessing module 112 ensures that the system 100 works with high-quality, reliable data for real-time casson hydromagnetic flow optimization.
[0057] The feature extraction module 114 configured to extract relevant features from the pre-processed data. The feature extraction module 114 identifies key patterns, relationships, and parameters that are critical for understanding fluid behaviour.
[0058] In one embodiment of the present invention, the feature extraction module 114 configured to extract relevant features including rheological, thermal, magnetic, and geometric characteristics from the pre-processed data.
[0059] In one embodiment of the present invention, the feature extraction module 114 ensures that only the most relevant information is forwarded, reducing computational load and improving the accuracy of real-time casson hydromagnetic flow optimization.
[0060] The physics calculation module 116 configured to analyse the extracted features and compute fluid flow parameters. The physics calculation module 116 takes the features extracted by the feature extraction module 114 and applies high-fidelity numerical simulations to model the behaviour of casson fluids under varying operating conditions.
[0061] In one embodiment of the present invention, the physics calculation module 116 configured to calculate fluid flow parameters including velocity distribution, pressure distribution, temperature profile, magnetic field intensity, shear stress, and hydromagnetic flow characteristics using computational fluid dynamics.
[0062] In one embodiment of the present invention, these computed parameters are then provided to the behaviour learning module 118 and the result prediction module 120, forming a seamless connection between raw input, feature understanding, and predictive analytics for real-time optimization.
[0063] The behaviour learning module 118 configured to learn fluid behaviour under varying operating conditions based on the computed fluid flow parameters.
[0064] In one embodiment of the present invention, the behaviour learning module 118 configured to learn fluid behaviour under varying operating conditions including changes in velocity distribution, pressure gradients, temperature variations, magnetic field intensity, and boundary conditions using artificial intelligence-based surrogate model.
[0065] In one embodiment of the present invention, artificial intelligence-based surrogate model leverages advanced machine learning architectures, including deep neural networks, convolutional neural networks, recurrent neural networks, transformer models, and physics-informed neural networks, to accurately capture the relationships and patterns in the fluid flow data.
[0066] In one embodiment of the present invention, the behaviour learning module 118 enables the system 100 to anticipate the fluid’s response to new operating conditions and provides essential data for the result prediction module 120 and the optimization module 122.
[0067] In one embodiment of the present invention, the behaviour learning module 118 ensures that the system 100 adapts dynamically, maintaining accurate predictions and supporting real-time optimization of casson hydromagnetic flow.
[0068] In one embodiment of the present invention, the behaviour learning module 118 learns flow behaviour in real time using the learned patterns, reducing the need for repetitive high-fidelity simulations while maintaining accuracy and enabling fast, adaptive optimization.
[0069] The result prediction module 120 configured to predict fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns.
[0070] In one embodiment of the present invention, the result prediction module 120 takes the learned relationships between velocity distribution, pressure gradients, temperature variations, magnetic field intensity, and boundary conditions, and forecasts how the fluid will respond to changing operating conditions. These predictions are performed instantly, without needing to run full high-fidelity simulations every time, enabling rapid decision-making.
[0071] In one embodiment of the present invention, the result prediction module 120 ensures that predictions remain accurate and adaptive, supporting efficient and reliable real-time optimization of casson hydromagnetic flows.
[0072] The optimization module 122 configured to optimize operating parameters by adjusting control variables based on the predicted results.
[0073] In one embodiment of the present invention, the optimization module 122 configured to optimize operating parameters by adjusting control variables based on the predicted results using at least one optimization technique including reinforcement learning and genetic algorithms.
[0074] In one embodiment of the present invention, the optimization module 122 ensures that the system 100 maintains efficient and stable casson hydromagnetic flow under dynamically changing conditions.
[0075] In one embodiment of the present invention, the processing unit 108 further comprises the validation module 124 configured to selectively execute high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes.
[0076] In one embodiment of the present invention, the processing unit 108 further comprises the feedback module 126 configured to monitor the system 100 performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance.
[0077] The output module 128 configured to transmit optimized flow parameters, predicted flow behaviour, and performance metrics to the user device 102.
[0078] In one embodiment of the present invention, the output module 128 serves as the final stage in the system 100, ensuring that all relevant analytical outcomes are delivered to the end user in a structured, interpretable, and useful format.
[0079] FIG. 2 illustrates a flow chart of a method, outlining the sequential steps for the real-time casson hydromagnetic flow optimization system, in accordance with an embodiment of the present disclosure.
[0080] At step 202, the input data is collected from a user through a user interface 104 via a user device 102.
[0081] At step 204, the communication link for data transmission is established within the system 100 via a communication network 106.
[0082] At step 206, the real-time data for casson hydromagnetic flow optimization is processed via a processing unit 108, comprising several modules.
[0083] At step 208, the real-time data is received from the user device 102 via a data input module 110.
[0084] At step 210, the received data is cleaned, normalized, and validated to enhance data quality via a preprocessing module 112.
[0085] At step 212, the relevant features are extracted from the pre-processed data via a feature extraction module 114.
[0086] At step 214, the extracted features are analysed and compute fluid flow parameters via a physics calculation module 116.
[0087] At step 216, the fluid behaviour is learned under varying operating conditions based on the computed fluid flow parameters via a behaviour learning module 118.
[0088] At step 218, the fluid flow parameters in real-time is predicted for dynamically changing conditions based on the learned behaviour patterns via a result prediction module 120.
[0089] At step 220, the operating parameters are optimized by adjusting control variables based on the predicted results via an optimization module 122.
[0090] At step 222, the high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes are executed via a validation module 124.
[0091] At step 224, the system 100 performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance is monitored via a feedback module 126.
[0092] At step 226, the optimized flow parameters, predicted flow behaviour, and performance metrics are transmitted to the user device 102 via an output module 128.
[0093] At step 228, the optimized flow parameters, predicted flow behaviour, and performance metrics are displayed on the user interface 104 of the user device 102.
[0094] In the best mode of operation of the present invention, the system 100 is designed for real-time, accurate, and automated optimization of casson hydromagnetic fluid flow. The system 100 utilizes the user device 102, which collects input data including fluid properties, magnetic field strength, temperature conditions, boundary conditions, and performance metrics through the user interface 104. A secure communication link is established via the communication network 106 to facilitate seamless transmission of the input data throughout the system 100. Once data is collected, it is transmitted to the processing unit 108, which comprises several specialized modules. Initially, the data is received by the data input module 110, where it is securely ingested into the system 100. The received data is then forwarded to the preprocessing module 112, which cleans, normalizes, and validates the data to enhance quality and ensure it is suitable for subsequent fluid flow analysis. The processed data is then sent to the feature extraction module 114, which identifies the key features influencing the fluid flow, such as rheological, thermal, magnetic, and geometric characteristics. These extracted features are analyzed by the physics calculation module 116, which computes the detailed fluid flow parameters including velocity distribution, pressure, temperature profile, magnetic field intensity, shear stress, and other hydromagnetic characteristics. The computed fluid flow parameters are then used by the behaviour learning module 118 to understand how the fluid behaves under varying operating conditions. This learned behavior informs the result prediction module 120, which instantly predicts the fluid flow parameters for dynamically changing conditions in real time. The optimization module 122 uses these predictions to determine and adjust the optimal operating parameters by manipulating control variables, such as magnetic field strength, flow rate, temperature, and other relevant process parameters. Simultaneously, the validation module 124 selectively executes high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes to ensure the system 100 predictions remain accurate and reliable. To close the loop, the feedback module 126 continuously monitors the actual system performance, compares it with the predicted results, and updates control variables and model parameters to maintain optimal flow conditions. Finally, the output module 128 transmits the optimized flow parameters, predicted flow behavior, and performance metrics back to the user device 102. These results are visually presented on the user interface 104 for interpretation by the end-user, providing a comprehensive overview. In addition, the system 100 employs the cloud database 130 to store and manage processed data, simulation outputs, predicted flow behaviour, and optimized operating parameters, allowing real-time access and analysis.
[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. A real-time casson hydromagnetic flow optimization system (100), the system (100) comprising:
a user device (102) configured to collect input data from a user through a user interface (104);
a communication network (106) configured to establish a communication link for data transmission within the system (100);
a processing unit (108) connected to the user device (102) via the communication network (106) and configured to process real-time data for casson hydromagnetic flow optimization, wherein the processing unit (108) further comprises:
a data input module (110) configured to receive real-time data from the user device (102);
a preprocessing module (112) configured to clean, normalize, and validate the received data to enhance data quality;
a feature extraction module (114) configured to extract relevant features from the pre-processed data;
a physics calculation module (116) configured to analyse the extracted features and compute fluid flow parameters;
a behaviour learning module (118) configured to learn fluid behaviour under varying operating conditions based on the computed fluid flow parameters;
a result prediction module (120) configured to predict fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns;
an optimization module (122) configured to optimize operating parameters by adjusting control variables based on the predicted results; and
an output module (128) configured to transmit optimized flow parameters, predicted flow behaviour, and performance metrics to the user device (102).
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (130) configured to store and manage processed data, simulation outputs, predicted flow behaviour, and optimized parameters for real-time access and analysis.
3. The system (100) as claimed in claim 1, wherein the user device (102) configured to collect input data including fluid and magnetic properties, boundary conditions, performance metrics, and control settings.
4. The system (100) as claimed in claim 1, wherein the feature extraction module (114) configured to extract relevant features including rheological, thermal, magnetic, and geometric characteristics from the pre-processed data.
5. The system (100) as claimed in claim 1, wherein the physics calculation module (116) configured to calculate fluid flow parameters including velocity distribution, pressure distribution, temperature profile, magnetic field intensity, shear stress, and hydromagnetic flow characteristics using computational fluid dynamics.
6. The system (100) as claimed in claim 1, wherein the behaviour learning module (118) configured to learn fluid behaviour under varying operating conditions including changes in velocity distribution, pressure gradients, temperature variations, magnetic field intensity, and boundary conditions using artificial intelligence-based surrogate model.
7. The system (100) as claimed in claim 1, wherein the optimization module (122) configured to optimize operating parameters by adjusting control variables based on the predicted results using at least one optimization technique including reinforcement learning and genetic algorithms.
8. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises a validation module (124) configured to selectively execute high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes.
9. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises the feedback module (126) configured to monitor the system (100) performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance.
10. A method (200) for real-time casson hydromagnetic flow optimization system (100), the method (200) comprising:
collecting input data from a user through a user interface (104) via a user device (102);
establishing a communication link for data transmission within the system (100) via a communication network (106);
processing real-time data for casson hydromagnetic flow optimization via a processing unit (108), comprising several modules;
receiving real-time data from the user device (102) via a data input module (110);
cleaning, normalizing, and validating the received data to enhance data quality via a preprocessing module (112);
extracting relevant features from the pre-processed data via a feature extraction module (114);
analysing the extracted features and compute fluid flow parameters via a physics calculation module (116);
learning fluid behaviour under varying operating conditions based on the computed fluid flow parameters via a behaviour learning module (118);
predicting fluid flow parameters in real-time for dynamically changing conditions based on the learned behaviour patterns via a result prediction module (120);
optimizing operating parameters by adjusting control variables based on the predicted results via an optimization module (122);
executing high-fidelity simulations for predefined critical scenarios, high-priority operating conditions, and model verification purposes via a validation module (124);
monitoring the system (100) performance, compare predicted results with actual operating conditions, and update control variables and model parameters to maintain optimal flow performance via a feedback module (126);
transmitting optimized flow parameters, predicted flow behaviour, and performance metrics to the user device (102) via an output module (128); and
displaying optimized flow parameters, predicted flow behaviour, and performance metrics on the user interface (104) of the user device (102).