Design Accelerator Technology: Accelerating the Pace of Innovative Response

Design Accelerator Technology: Accelerating the Pace of Innovative Response

* During the ASME Turbo Expo 2024 Turbomachinery Technical Conference & Exposition, taking place on June 24 in London, we will be showcasing two design accelerators that we have been developing:

(1) Ecofuelmaximizer: Accelerating Mega-Powered Fuel Cell (ID: 120934)

(2) Accelerating Sustainable Propulsion System (ID: 128278).

Design accelerator technology, integrated with generative pre-trained transformer (GPT) technologies, is an innovative approach to concept product design and manufacturing that aims to accelerate the pace of innovation and address sustainability goals. By utilizing a cloud-based platform and collaboration among users, designers, and stakeholders, design accelerators gather inputs and feedback through various mediums and use them to create concept design models. These models are developed on a digital twin powered by a neural network, trained and validated against physical surrogates, and continuously updated using real-time data. The use of digital twins, combined with data-driven techniques and simulation models, enhances the accuracy and reliability of engineering analysis and simulations. Integration with quantum simulators during the training and tuning phase provides additional insights and scenarios for exploration. However, the choice of modeling methods and the validation and calibration against physical surrogates are critical in ensuring the effectiveness of these models. The application of design accelerators in industries such as aerospace, automotive, and marine power contributes to the development of sustainable and efficient products. Ongoing efforts are focused on improving the accuracy and capabilities of quantum simulators to further enhance the design and optimization process.

Abstract

Design accelerator technology seeks to enhance engineering methods for concept product design and manufacturing, while also addressing sustainability goals. By integrating design technology accelerators with Generative Pre-trained Transformer (GPT) technologies, collaboration among users, designers, and stakeholders is enabled. This technology utilizes a chatbot framework to gather inputs and feedback from users through audio, text, and visual mediums. The information obtained is then used to create concept design models using featured product APIs on a multi-architectural platform. The design accelerators are managed by the cloud-based aLL-i ?Engineering open-source platform. The objective is to expedite the concept design and manufacturing process while prioritizing sustainability, carbon neutrality, efficiency, and circularity.

Summary

A design accelerator is a concept product design and prototyping technology integrated on a multi-architectural platform. It operates on a cloud-based system and involves collaboration with users using GPT (Generative Pre-trained Transformer) technologies. Users and designers input text, audio, and visual information, which is processed through featured product APIs to empower concept design. Engineering expertise and cutting-edge technologies are utilized to develop concept models on a digital twin powered by a neural network. These models are trained, tuned, tested, optimized, and validated against physical surrogates and models in real-time throughout the product lifecycle, and the digital twin is updated using mixed reality in the engineering metaverse.

The application of neural network-based digital twin modelling in the engineering metaverse combines data-driven techniques with simulation models. The accuracy and reliability of these models depend on factors such as the quality of training data, the architecture and training process of the neural network, and the integration of the digital twin with real-time data. Proper testing, validation, and calibration against physical surrogates can enhance the accuracy and reliability of digital twin models. The choice of method depends on factors such as system complexity, data availability, resources, and time constraints. Integrating different modeling approaches, such as numerical models with data-driven techniques or experimental validation, can provide robust and accurate insights for engineering analysis and simulations.

In addition, some design accelerators integrate quantum simulators into the training process. These simulators generate simulated quantum data that can be used as training data for the neural network. By using quantum simulators during the training and tuning phase, designers can efficiently generate data and explore different scenarios in a controlled environment. The insights gained from these simulations can guide the development of more accurate models on the digital twin.

In summary, design accelerators leverage advanced technologies, collaborative platforms, and data-driven techniques to enhance the accuracy and reliability of engineering analysis and simulations. By integrating various modelling approaches and leveraging the capabilities of quantum simulators, designers can develop more accurate models on digital twins, enabling improved product design and optimization in the pursuit of efficient and sustainable solutions.

Part-1.Featured Accelerators: aLL-i X Design Accelerator Technology

?The aLL-i X design accelerator aims to revolutionize the concept design of advanced aerospace vehicles by focusing on alternative, high-powered all-electric propulsion system concepts, as well as innovative fuel cell and battery designs. Using GPT technologies, engineering expertise, and cutting-edge technologies on a multi-architectural platform within the cloud, our objective is to develop innovative design technology that accelerates high-performance all-electric propulsion and power systems. We place special emphasis on enhancing fuel cells and advancing battery technology to create powerful all-electric propulsion systems and sustainable power sources for aviation and aerospace applications.

1.1. EcoFuelmaximizer API. Design of mega-powered fuel cells

One of the key components of the aLL-i X design accelerator is the EcoFuelmaximizer API. This API focuses on accelerating the design of mega-powered fuel cells for hybrid power combinations. By incorporating stakeholder feedback and leveraging advanced technologies such as quantum computing, our accelerator aims to achieve sustainable and efficient fuel cell designs. The API also facilitates collaboration through integration with platforms like GitHub, allowing for seamless sharing and collaboration on fuel cell designs. Additionally, a digital twin and live monitoring system ensure continuous optimization and validation of the fuel cell design, ensuring it meets the required performance and sustainability standards.

1.2.InnovXCell: Reinventing Massless Electrical Storage Design

Another featured accelerator within the aLL-i X platform is InnovXCell. This accelerator is dedicated to advancing the design of massless electrical storage by integrating composites into advanced concept aerospace vehicles and future cars. The focus is on developing cutting-edge structural batteries that not only provide efficient energy storage but also contribute to the mechanical integrity and lightweight design of vehicles and devices. By leveraging advanced technologies, collaboration capabilities, and data-driven optimization techniques, InnovXCell aims to revolutionize the integration of batteries into devices, vehicles, and infrastructure.

1.3 . EcoXPropulse: Revolutionizing Sustainable Propulsion System Design

The EcoXPropulse accelerator is dedicated to revolutionizing the design of sustainable propulsion systems integrated into advanced vehicle concept designs. The objective of EcoXPropulse is to develop emission-free, efficient, carbon-neutral, and circular propulsion systems for the aerospace industry. By incorporating innovative designs, thermal management optimization techniques, fault-tolerant designs, and digital twin technologies, EcoXPropulse aims to pave the way for a greener and more efficient future in aviation and aerospace.

1.4.QxSim: Exploring Quantum Insights

?Within the aLL-i X design accelerator platform, we also feature QxSim. This accelerator utilizes quantum simulators and advanced battery models to create alternative and efficient energy models. By transforming these models into the quantum domain, QxSim enables designers to explore different scenarios and test their feasibility. This integration of quantum computing technology allows for the efficient generation and exploration of various possibilities, leading to more informed design decisions.

?1.5. MN-Kapota X: all-electric light aircraft design for our bid to RAeS 2024 light aircraft competition.

In addition to these design accelerators, we have a special project called MN-Kapota X. This charitable initiative involves the design of a two-seater, all-electric light aircraft specifically designed for firefighting and delivery purposes. The main objective of this project is to raise donations for Royal Aeronautical Society charities and promote young talents in the aerospace industry. By combining innovation, sustainability, and social responsibility, MN-Kapota X seeks to make a positive impact on the community and inspire future generations in the field of aerospace.

Part-2 Enhancing Accuracy and Reliability in Engineering Analysis and Simulations through Digital Twins

The use of digital twins and quantum simulators to enhance accuracy and reliability in engineering analysis and simulations. Digital twins, powered by machine learning algorithms and neural networks, combine data-driven techniques with simulation models to create accurate representations of physical systems. The quality of training data, neural network architecture, and integration with real-time data play crucial roles in the accuracy and reliability of digital twin models. Validation and calibration against physical surrogates further improve their effectiveness.

Different modeling approaches, such as numerical models, data-driven engineering models, models based on first principles, and experimental models, offer varying levels of accuracy and reliability based on system complexity, data availability, resources, and time constraints. The combination of these approaches can provide robust and accurate insights for engineering analysis and simulations.?

Quantum simulators are utilized in the training and tuning phase to efficiently generate data and explore various scenarios. However, it is important to note that the accuracy and capability of quantum simulators are not yet at the level of physical quantum systems. Hence, rigorous validation and calibration against experimental or theoretical benchmarks are necessary before applying simulated insights to the development of final digital twin models.?

In conclusion, the use of neural network-based digital twin modeling in the engineering metaverse shows promise in combining data-driven techniques with simulation models. The accuracy and reliability of these models rely on factors such as the quality of training data, the architecture of neural networks, and the integration of real-time data. Proper testing, validation, and calibration against physical surrogates can enhance the accuracy and reliability of digital twin models.

It's important to recognize that there is no one-size-fits-all approach to achieving the highest accuracy and reliability in engineering scenarios. The choice of method depends on factors like system complexity, data availability, resources, and time constraints. Integrating numerical models with data-driven techniques or experimental validation can provide robust and accurate insights for engineering analysis and simulations.

Accuracy and reliability can also be improved through validation and verification, sensitivity analysis, uncertainty analysis, cross-validation, and benchmarking. These methods help assess the quality of models, analysis, and simulations.

Quantum simulators offer efficient data generation and exploration of different scenarios in a controlled environment. They can provide valuable insights for developing more accurate digital twin models. However, it's essential to validate and calibrate simulation results against experimental or theoretical benchmarks due to the current limitations and restrictions of quantum simulators.

2.1.Models and datasets enhanced by Featured APIs.

The application of neural network-based digital twin modeling in the engineering metaverse is a promising approach that combines data-driven techniques with simulation models. The accuracy and reliability of these models depend on the quality and representativeness of the training data, the architecture and training process of the neural network, and the integration of the digital twin with real-time data. Proper testing, validation, and calibration against physical surrogates can enhance the accuracy and reliability of digital twin models.

It is important to note that there is no one-size-fits-all method that provides the highest accuracy and reliability for all engineering scenarios. The choice of method depends on various factors such as system complexity, data availability, resources, and time constraints. A combination of different modeling approaches, such as integrating numerical models with data such as numerical models, data-driven techniques, and experimental validation can offer robust and accurate insights for engineering analysis and simulations.

The aim of design accelerators is to improve the accuracy and reliability of concept product design, prototyping, and manufacturing processes, while achieving ambitious engineering targets on sustainability, carbon neutrality, efficiency, and circularity throughout the product life cycle. These accelerators are utilized in industries such as aerospace, automotive, and marine power to develop advanced products and vehicles with zero emissions.

In the training and tuning phase, quantum simulators are used to efficiently generate data and explore different scenarios in a controlled environment. The insights gained from these simulations can guide the development and design of more accurate models on the digital twin.

To ensure reliability and accuracy in engineering analysis and simulations, it is essential to validate and verify the models. This involves comparing model predictions or simulation results against experimental data or known theoretical solutions. Sensitivity analysis, uncertainty analysis, cross-validation, and benchmarking against established models or industry standards are also effective methods for assessing the accuracy and reliability of models.

2.2.Modeling Approaches and Integration with Quantum Simulators for Reliable and Accurate Data in Digital Twin Development?

Integrating various modeling approaches with quantum simulators can enhance the development of reliable and accurate data for building surrogates in digital twin modeling. By carefully considering the pros, cons, and limitations of different modeling approaches and quantum simulators, engineers and researchers can empower digital twin development and improve engineering analysis and simulations. However, it is important to note that there is no one-size-fits-all method for achieving the highest accuracy and reliability in all engineering scenarios. The choice of method depends on factors such as system complexity, data availability, resources, and time constraints. Thus, a combination of different modeling approaches can offer robust and accurate insights for engineering analysis and simulations.

2.2.1. Numerical Models:

Numerical models utilize computational techniques to solve mathematical equations and simulate system behavior. These models can simulate complex systems, are cost-effective, scalable, and can explore various scenarios. However, they rely on accurate input data, require expertise in numerical methods, and may have limitations in handling certain phenomena.

2.2.2. Data-Driven Engineering Models:

Data-driven models use historical data to infer relationships and patterns for making predictions or simulating behavior. They can capture complex relationships, adapt to changing data, and handle large datasets. However, they require comprehensive and representative data, may struggle with extrapolation outside the data range, and may not provide detailed insights into underlying physical mechanisms.

2.2.3. Models Based on First Principles:

First principles models are developed based on mathematical equations derived from fundamental physics or chemistry. They capture fundamental physics and chemistry, are flexible to model changes, and can provide detailed insights. However, they rely on accurate input parameters, can be computationally expensive, and require expertise in model development.

2.2.4. Experimental Models:

Experimental models involve physical testing and measurement to understand and predict system behavior. They provide real-world insights, can test specific hypotheses, and validate numerical models. However, they are expensive and time-consuming, have limited scalability, and may have constraints on exploring a wider range of scenarios.

Neural Network-based Digital Twin Modeling:

Neural network-based digital twin modeling in the engineering metaverse is a promising approach that combines data-driven techniques with simulation models. The accuracy and reliability of such models depend on the quality and representativeness of the training data, the architecture and training process of the neural network, and the integration of the digital twin with real-time data. Proper testing, validation, and calibration against physical surrogates can enhance the accuracy and reliability of digital twin models.

Ensuring Reliability and Accuracy of Engineering Analysis and Simulations:

To assess the quality of models, analysis, and simulations, several approaches can be employed:

1. Validation and Verification: Comparing model predictions or simulation results against experimental data or known theoretical solutions allows for quantifying the differences and assessing the accuracy of the model.

2. Sensitivity Analysis: Varying input parameters within reasonable ranges and observing the resulting changes in the output provides insights into the model's sensitivity and helps assess its reliability.

3. Uncertainty Analysis: Examining uncertainties in input parameters and propagating them through the model allows for estimating uncertainties in the output. This analysis helps understand the reliability of the model's predictions.

4. Cross-Validation: For data-driven models, cross-validation is used to evaluate their predictive performance. The model is trained on a subset of the data and then tested on the remaining data to assess its accuracy and reliability.

5. Benchmarking: Comparing the performance of a model or simulation against established models or industry standards provides an indication of its accuracy and reliability.

Integration of Quantum Simulators:

Using quantum simulators in the training and tuning phase allows for efficient data generation and exploration of different scenarios. However, it is important to note that the accuracy and capability of quantum simulators are not yet at the level of physical quantum systems. Therefore, it is crucial to carefully validate and calibrate the results obtained from simulations against experimental or theoretical benchmarks to ensure their reliability and accuracy when developing the final model on a digital twin.

Limitations and Restrictions of Quantum Simulators:?

1. Quantum Complexity: Simulating large-scale quantum systems can be challenging due to the exponential increase in computational resources required. This limitation restricts quantum simulators to simulating relatively small or simple systems.

2. Quantum Error and Noise: Quantum systems are inherently noisy, and accurately simulating these noise sources can be difficult. The accuracy of quantum simulators can be limited by their ability to capture and model these errors.

3. Computational Resources: Simulating complex quantum systems requires significant computational resources. The limitations of available resources, such as memory and processing power, can restrict the size and accuracy of quantum simulations.

4. Approximate Methods: Due to the complexity of simulating large quantum systems, approximate methods and approximations are often used. These approximations may introduce errors and limitations in accurately representing the quantum system and its dynamics.

5. Algorithmic Limitations: The algorithms used in quantum simulators may have inherent limitations or assumptions that constrain their applicability to certain types of quantum systems or specific aspects of the simulation.

6. Validation Challenges: Validating the accuracy of quantum simulators can be challenging since direct experimental measurements on large-scale quantum systems are often limited. Validation typically involves comparing simulation results with established theoretical models or smaller-scale experimental data, which may not fully capture the complexity of the system.

Integrating various modeling approaches with quantum simulators can enhance the development of reliable and accurate data for building surrogates in digital twin modeling. However, it is crucial to carefully consider the pros, cons, and limitations of different modeling approaches and quantum simulators. There is no one-size-fits-all method for achieving the highest accuracy and reliability in all engineering scenarios. The choice of method depends on various factors such as system complexity, data availability, resources, and time constraints. By combining different modeling approaches and employing validation techniques, engineers and researchers can empower digital twin development and improve engineering analysis and simulations. Despite the limitations and restrictions of quantum simulators, they provide valuable insights into quantum systems and contribute to advancements in quantum computing applications. Ongoing efforts to improve the accuracy and capabilities of quantum simulators will further enhance their reliability and accuracy.

3.Conclusion

Design accelerator technology and its potential to accelerate innovation and response in product design and manufacturing. The integration of design technology accelerators with GPT technologies enables collaboration among users, designers, and stakeholders through a chatbot framework. These accelerators leverage advanced technologies, collaborative platforms, and data-driven techniques to enhance the accuracy and reliability of engineering analysis and simulations.

The use of digital twins, powered by machine learning algorithms and neural networks, to create accurate representations of physical systems. The quality of training data, neural network architecture, and integration with real-time data play crucial roles in the accuracy and reliability of digital twin models. The use of quantum simulators in the training and tuning phase to efficiently generate data and explore different scenarios.

However, it is important to note that the accuracy and capability of quantum simulators are still not at the level of physical quantum systems. As a result, rigorous validation and calibration against experimental or theoretical benchmarks are necessary before applying simulated insights to the development of final digital twin models.

Design accelerators aim to improve the accuracy and reliability of concept product design, prototyping, and manufacturing processes while prioritizing sustainability, carbon neutrality, efficiency, and circularity throughout the product life cycle. The design accelerators are utilized in industries such as aerospace, automotive, and marine power to develop advanced products and vehicles with zero emissions.

In summary, design accelerator technology holds great promise in accelerating the pace of innovation in product design and manufacturing. By leveraging advanced technologies, collaborative platforms, and data-driven techniques, design accelerators can enhance the accuracy and reliability of engineering analysis and simulations. The integration of digital twins and quantum simulators further enhances the capabilities of these accelerators. However, further research and development are required to improve the accuracy and capabilities of quantum simulators. With ongoing advancements, design accelerators have the potential to revolutionize the way we design and manufacture products, paving the way for a more efficient and sustainable future.

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