The trend of sharing concepts is constantly increasing, whether this may be for economic or environmental reasons. Consequently, numerous scientific research works have addressed the subject of sharing concepts. Many of these works have dealt with questions on the topic of sharing concepts itself, however, much less research has been dedicated to the question of how the sharing concept can be developed in the very first place. Thus, the purpose of this work was to systematically use systems engineering methods to develop a sharing concept for heavy-duty agricultural vehicles, while having a strong focus on technical and logistical aspects. Due to the multidisciplinary complexity of the sharing concept, a method from the field of model-based systems engineering, ARCADIA, was chosen. On ARCADIA’s top level, operational analysis was carried out to identify the key stakeholders. The next level, systems analysis, showed that the sharing model can be divided into three main processes: (1) data acquisition and preparation; (2) location planning; (3) and route planning. For these main processes, corresponding methods, algorithms and models were sought and compared against each other in the last level, logical analysis. It can be concluded that the ARCADIA method has provided a framework for evaluating the correlations and interrelationships between methods, algorithms and models at different levels to develop a sharing concept for compost turners from a technical perspective.

The trend of sharing concepts is constantly increasing, whether for economic or environmental reasons. Consequently, there is numerous scientific research work addressing the subject of sharing concepts. Many of these works deal with questions on the topic of sharing concepts itself, however, much less research has been done on the question of how a sharing concept can be developed in the very first place. Regarding the sectors studied, it is noteworthy that while automotive and bike sharing concepts are studied intensively, agriculture and farming remains rather in the background. Objective. The purpose of this publication is to systematically use systems engineering methods to develop a sharing concept for heavy duty agricultural vehicles. Thereby, the focus of this publication is on the consideration of technical aspects. Methods. Due to the multidisciplinary complexity of the sharing concept, a method from the field of Model-Based Systems Engineering, ARCADIA is chosen. Using this method, the development of the Sharing Concept is systematically demonstrated: Starting from the abstract Operational Analysis, stakeholders and their interactions are identified. In the System Analysis, similar to SysML, diagrams are used to represent both structures and behaviour models. In the Logical Architecture, the internal relationships between the system components are analysed.Results. Using the methodical, systematic, and structured procedure according to the ARCADIA method, it was possible to analyse the method's individual levels. On ARCADIA's top, most abstract level, an Operational Analysis was carried out to identify the key stakeholders of the sharing concept. The next level, the Systems Analysis, showed that a sharing model for compost turners has to be divided into three main processes: 1.) Data Acquisition and Data Preparation, 2.) Location Planning and 3.) Route Planning. These processes were examined in detail in the Logical Analysis, paying particular attention to their mutual interaction. For each of these three main processes, corresponding methods, algorithms and models were sought and compared against each other (for details, see figure "Logical Architecture").Conclusion. In summary, it can be concluded that the ARCADIA method has provided a framework for evaluating the correlations and interrelationships between methods, algorithms and models at the different levels to develop a sharing concept for compost turners.

The Physical Internet (PI) envisions a global logistics system that integrates physical, digital and operational connections. This study aims to develop a hyperconnected logistic platform for heavy-duty machinery (HDM) in the composting industry by utilizing a systematic methodology. The proposed architecture consists of four layers: the Domain Model, the MBSE Model, the Information Sharing Model and the Agent-based Simulation Platform. The Domain Model analyzes the current situation and investigates stakeholder viewpoints, and the MBSE Model reduces complexity and describes mutual interactions between requirements and needs. The Information Sharing Model focuses on the information exchange among the main components, and the Agent-based Simulation Platform implements the proposed platform. The feasibility of the proposed architecture is demonstrated through a use case in Styria, Austria. Three simulation-based scenarios are analyzed, starting from the semi-hyperconnected approach up to the hyperconnected approach with PI vision integration. The results indicate that the hyperconnected platform is successful in serving all composting facilities, leveraging underutilized resources and promoting high-quality compost production. Thus, the platform provides support in a local, communal setting, resulting in enhancing the circular economy within the composting sector. Our efforts aim to contribute to the realization of the Physical Internet vision and promote composting to ultimately achieve a more sustainable future.

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future.

Mechanical compost turning produces short-lived greenhouse-gas concentration peaks, yet the physical mechanism linking particle agitation to near-field gas transport remains poorly understood. A two-stage numerical framework combining the discrete element method (DEM) and computational fluid dynamics (CFD) was developed to investigate this process during industrial windrow turning. In Stage 1, a coupled CFD–DEM model resolved rotor-driven particle motion and the resulting mechanically generated airflow. The time-averaged airflow field was then transferred to a Volume-of-Fluid multiphase model to simulate CO₂ and CH₄ transport. The framework was evaluated against field measurements obtained from a commercially operated eWender windrow turner. Although turning produced a visually prominent plume, measured and simulated near-field air velocities remained predominantly below 1 m/s, indicating a low-velocity transport regime rather than strong atmospheric convection. CO₂ and CH₄ concentrations exhibited short transient bursts during active turning, while measured CO₂ concentrations remained predominantly within approximately 1500–4000 ppm. Simulated air velocities and CO₂ concentrations fell within the corresponding measured ranges. The results indicate that mechanically generated airflow contributes mainly to local gas redistribution and near-field concentration enhancement rather than sustained or far-reaching transport. Accordingly, the visually prominent plume and short-duration concentration peaks observed during turning should not be interpreted alone as evidence of proportionally increased total greenhouse-gas emissions.

This preprint presents the current status of research into the development and application of an autonomous, self-driving compost turner. The aim is to overcome challenges in the composting industry, such as adverse working conditions, by automating the composting process. The preprint provides a comprehensive overview of the overall concept of the self-driving compost turner, including the hardware architecture with sensors, navigation module and control module. In addition, the methodical development of the architecture of concepts, models and their subsequent software integration in ROS using model-based systems engineering is described. The validation and verification of the overall system is carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by autonomously following predefined trajectories in the composting plant and performing the required composting tasks. The results show that the autonomous compost turner is capable of performing the required activities. In addition, the compost turner has intelligent processing capabilities for compost data as well as its transmission, visualization and storage in a cloud server. It is important to note that this work is a preprint that represents the current state of research. The authors aim to publish the full paper in a peer-reviewed journal in the near future.