We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains $27{,}714$ diverse air vehicle designs - the largest corpus of designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design, and more generally, in the emerging field of deep learning for scientific design. AircraftVerse is accompanied by a datasheet as suggested in the recent literature, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset with baseline models are hosted at http://doi.org/10.5281/zenodo.6525446, code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at https://uavdesignverse.onrender.com/.

Cyber-Physical Systems (CPS) are establishing heterogeneous engineering domains leading to engineering processes that span multiple design disciplines with separate modeling approaches, design flows and supporting tool suites. One of the challenges of design automation in CPS is the deep integration of models, tools and design flows such that design trade-offs across traditionally isolated design disciplines is facilitated. In this paper we overview experience and results gained along the implementation of an experimental design automation tool suite, OpenMETA, created for a complex CPS design challenge in the ground vehicle domain. The focus of the paper is domain agnostic methods and tools providing infrastructure for the model- and tool- integration platforms in OpenMETA. We present the arguments leading to the creation of the integration platforms instead of pursuing ad-hoc integration of heterogeneous tools and provide details on facilitating semantic integration.

Cyber-Physical Systems (CPS) are used in many applications where they must perform complex tasks with a high degree of autonomy in uncertain environments. Traditional design flows based on domain knowledge and analytical models are often impractical for tasks such as perception, planning in uncertain environments, control with ill-defined objectives, etc. Machine learning based techniques have demonstrated good performance for such difficult tasks, leading to the introduction of Learning-Enabled Components (LEC) in CPS. Model based design techniques have been successful in the development of traditional CPS, and toolchains which apply these techniques to CPS with LECs are being actively developed. As LECs are critically dependent on training and data, one of the key challenges is to build design automation for them. In this paper, we examine the development of an autonomous Unmanned Underwater Vehicle (UUV) using the Assurance-based Learning-enabled Cyber-physical systems (ALC) Toolchain. Each stage of the development cycle is described including architectural modeling, data collection, LEC training, LEC evaluation and verification, and system-level assurance.

Design methods and tools evolved to support the principle of "separation of concerns" in order to manage engineering complexity. Accordingly, most engineering tool suites are vertically integrated but have limited support for integration across disciplinary boundaries. Cyber–physical systems (CPSs) challenge these established boundaries between disciplines, and thus, the status quo on the tools market. The question is how to create the foundations and technologies for semantically precise model and tool integration that enable reuse of existing commercial and open source tools in domain-specific design flows. In this paper, we describe the lessons learned in the design and implementation of an experimental design automation tool suite, OpenMETA, for complex CPS in the vehicle domain. The conceptual foundation for the integration approach is platform-based design: OpenMETA is architected by introducing two key platforms: the model integration platform and the tool integration platform. The model integration platform includes methods and tools for the precise representation of semantic interfaces among modeling domains. The key new components of the model integration platform are model integration languages and the mathematical framework and tool for the compositional specification of their semantics. The tool integration platform is designed for executing highly automated design-space exploration. Key components of the platform are tools for constructing design spaces and model composers for analytics workflows. The paper concludes with describing experience and lessons learned by using OpenMETA in drivetrain design and by adapting OpenMETA to substantially different CPS application domains.

Design automation tools evolved to support the principle of "separation of concerns" to manage engineering complexity. Accordingly, we find tool suites that are vertically integrated with limited support (even intention) for horizontal integratability (i.e. integration across disciplinary boundaries). CPS challenges these established boundaries and with this - market conditions. The question is how to facilitate reorganization and create the foundation and technologies for composable CPS design tool chains that enables reuse of existing commercial and open source tools? In this paper we describe some of the lessons learned in the design and implementation of a design automation tool suite for complex cyber-physical systems (CPS) in the vehicle domain. The tool suite followed a model- and component-based design approach to match the significant increase in design productivity experienced in several narrowly focused homogeneous domains, such as signal processing, control and aspects of electronic design. The primary challenge in the undertaking was the tremendous heterogeneity of complex cyber-physical systems (CPS), where such as vehicles has not yet been achieved. This paper describes some of the challenges addressed and solution approaches to building a comprehensive design tool suite for complex CPS.

System design typically involves the specification of requirements and evaluation of the design with respect to those requirements. The requirements describe and quantify the desired physical and software properties of the system. Evaluating requirements often involves performing domain-specific analysis. Domain-specific analyses are spread across a wide range of domains and tools, e.g., geometric properties vs. dynamics behavior of the system. Different analysis types require different tools, where each tool targets a narrow range of domains or a single domain. For large system designs, the requirements could be too complex to be evaluated by a single analysis tool. In such cases, the coupling of multiple domains and analysis tools is inevitable, and managing these interactions can prove to be difficult, often leading to wasted efforts. In this paper we present an analysis-driven rapid design process for Cyber-Physical Systems that spans multiple domain models and various analysis tools from a wide range of domains, and helps to reduce the design time through the following: (1) revealing and tracking instances of cross-domain coupling, thereby reducing design time; (2) disqualifying non-viable design configurations; and (3) using analysis templates for continuous design evolution with respect to the requirements; minor adjustments to requirements can be done seamlessly, without a complete redesign of the existing reusable analysis templates. Furthermore, we present a case study for an automotive driveline design to demonstrate an implementation of this process.

The paper presents a smartphone-based shooter localization system. As muzzle blasts are difficult to detect at longer distances and consequently present higher false detection rates, the system relies on shockwaves only. Each sensor uses four microphones to detect the Angle of Arrival and the length of the shockwave. This information, along with the sensor's own GPS coordinates, are shared among nearby smartphones. Assuming a known weapon type, it then proceeds to estimate the two possible projectile trajectory candidates for each sensor that are consistent with the observations in the horizontal plane of the sensors. A simple clustering algorithm identifies the correct projectile trajectory relying on as few as two sensors. The trajectory is then used to estimate the bearing to the shooter relative to each sensor. The paper presents the overall system architecture, the design of the sensor node that interfaces with the smartphone, the trajectory and bearing estimation algorithms, and the evaluation of the system based on a field experiment.

High-energy physics experiments require an extraordinary amount of real-time computation, and the computers implementing the online data processing must be very reliable because of the large cost associated with operating the facilities and the potential for loss of irreplaceable data. Conventional redundancy-based fault tolerance and adaptive approaches are not appropriate because of the tremendous system cost (fault tolerance is limited to a maximum of 10% overhead). In this work, we developed a framework for building robust embedded systems, which utilizes an autonomic reflex-healing approach to achieve fault tolerance. Components of the framework implement user-defined failure adaptation strategies within the context of a large-scale embedded environment. The tools embrace a model-based approach combining design specification and code-generation for both simulation and system implementation. In this paper we present the concepts and entities of the reflex and healing framework

The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple-view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operates in, and capture the requirements and the design of the application, simultaneously. Models are descriptive , in the sense that they allow the formal analysis, verification, and validation of the embedded system at design time. Models are also generative, in the sense that they carry enough information for automatically generating embedded systems using the techniques of program generators. Because of the widely varying nature of embedded systems, a single modeling language may not be suitable for all domains; thus, modeling languages are often domain-specific. To decrease the cost of defining and integrating domain-specific modeling languages and corresponding analysis and synthesis tools, the model-integrated approach is applied in a metamodeling architecture, where formal models of domain-specific modeling languages-called metamodels-play a key role in customizing and connecting components of tool chains. This paper discusses the principles and techniques of model-integrated embedded software development in detail, as well as the capabilities of the tools supporting the process. Examples in terms of real systems will be given that illustrate how the model-integrated approach addresses the physical nature, the assurance issues, and the dynamic structure of embedded software.

No abstract available.

The design of modern high-performance embedded systems is challenging. Power and size constraints limit hardware size, while performance requirements demand algorithm-specific architectures. A model-integrated approach can be used in the design capture and synthesis of these systems. A domain-specific graphical system design environment allows the capture of system requirements, design information and alternatives, and of available processing resources in the form of models. A model interpretation process generates architecture specifications and compilable code. A typical first step in designing complex systems is to develop a simulation-based prototype. After functional verification of the prototype, system components are implemented, each tailored to a particular target platform. Only after all components are implemented can system integration be addressed, often uncovering inconsistencies between components and forcing costly redesign. This paper describes an extension of a model-integrated design environment and runtime system. Simulation-based components are included in system models. A simulation engine is interfaced to a runtime environment, allowing simulation components to run in the loop with nonsimulation based components. The extended environment allows prototype synthesis from system models and automates the integration of implemented components into the system. Further, the extended environment provides the designer with a powerful visualization and debugging tool.

The design, implementation and deployment of computer applications tightly integrated with complex, changing environments is a difficult task. This paper presents the Multigraph Architecture (MGA) developed for building complex embedded systems. The MGA is a meta-level architecture which includes tools and methods to create domain specific model integrated program synthesis environments. These environments support the integrated modeling of systems independently from their implementation, include tools for model analysis and application specific model interpreters for the synthesis of executable programs.