Current Projects

ENG: Data-based Software Engineering Methods and Tools


Internet marketplaces such as Amazon or eBay are dominating more and more areas of the retail trade as "digital checkpoints". One exception so far has been the trade with durable, highly varied and customizable consumer goods. The effect of the configurable product on the consumer's own four walls can only be checked after delivery. The ARBAY project brings the consultation and configuration of individualized goods into the living room. Virtual and augmented reality technologies are used in the project to create new digital distribution channels for highly varied goods. The aim is to develop a sales platform that extends the principle of known sales platforms for the sale of these goods. The main focus of the ISSE project is the development of a semantic product model as a basis for the platform.

E-Mail: Dr. Christoph Knieke


The aim of the project BioTope is to develop a basic technology and engineered methodology to facilitate the creation of emergent services that allow for self-adaptive system platforms. The rules of composition are not centrally and statically predetermined by the
Platform, but can be dynamically configured and demand-driven. This should be  achieved and maintained in an Open IoT ecosystem that Integrates data and services to create processes to satisfy user requirements. The main motivation here is user requirements, which drives any new behaviour created in the system. The ecosystem tends to create the balance between user needs and provided data and services. All this is governed by system guarantees that ensures the correct flow of the ecosystem.

E-Mail: Eric Douglas Nyakam Chiadjeu
E-Mail: Christian Schindler


Software ecosystems are complex system groups of interacting, distributed individual systems that require continuous, autonomous optimization. In DevOpt we understand an emergent, distributed system as a three layers architecture: Local IoT ecosystems negotiate their work configuration / resource usage under framework conditions. A control layer can correct local decisions through comprehensive, distributed optimization. A DevOps layer enables analysis, maintenance and further development through human intervention.

DevOpt aims at the development of controlled emergent systems through distributed modelling, local negotiation of device configurations / resource usage, increased development efficiency through model-based design, as well as combination of emergence and DevOps. The demonstration / evaluation takes place as electric grid scenarios using self-learning predictive SmartMeters. The ISSE will implement a component-based emergence framework for the local layer and environments, which enables local optimization and mutual dynamic use of resources. Furthermore, the emergent integration should be monitored and optimized with regard to functional and non-functional requirements.

E-Mail: Mohamed Toufik Ailane
E-Mail: Mohammad Abboush

Timing Analysis and Steering Development

The project "Timing Analysis and Steering Development" researches methods for estimating and safeguarding runtime behavior in embedded control systems with hard real-time requirements. This is done in the context of actual development projects. The focus is on the specification, modeling and measurement of runtime behavior at system level.

E-Mail: Mohammad Abboush
E-Mail: Dr. Christoph Knieke

V-Modell XT Bund

This project is about improvement and maintenance of V-Modell XT Bund. The V-Modell XT Bund is a company-specific adaptation of the V-Modell XT to the federal authorities. In this project it is developed, maintained and maintained according to V-Modell XT.

E-Mail: Dr. Christoph Knieke

V-Modell XT / Digital Projects App (DiPA)

Weit e. V. has commissioned the Institute for Software and Systems Engineering to provide the V-Modell XT in the form of a public REST interface. After the fundamental revision of the V-Modell XT metamodel, an interface will be offered that is specified with OpenAPI and implemented in Java.

First software developments of Weit e. V. implementing the V-Modell XT REST API are the newly developed Web Assistant (formerly Project Assistant) and the Digital Projects App( The revision of this software ecosystem is intended to offer a simple and contemporary entry into project management with V-Modell XT.

In this context, there is research interest in the software life cycle as well as the software architecture with the background that there are already several editions in the form of metamodels and derivations for different user groups of the V-Modell.


E-Mail: Dr. Christoph Knieke

DGT: Digitized Green Tech


The electronic products currently in use are designed for only short-lived use cycles and are contributors to the current increase in global waste volumes and excessive use of raw materials. [1] As a result, electrical and electronic products generate emissions that can be reduced through repair and reuse. However, emissions are also generated during the storage, sale and disposal of equipment. Particularly in view of global warming, these emissions represent a major potential for reduction. The aim of the project Life_TWIN is to investigate smart solutions for condition assessment and to identify opportunities for reuse, thereby reducingCO2 emissions. The project proposal within the funding line "Resource Efficiency in the Context of the Energy Transition" within the 7th non-nuclear funding program of the German Federal Ministry of Economics and Climate Protection (BMWK) is thus in direct line with other research projects to pave the way for the Circular Economy. Research into solutions for improving the condition monitoring of products plays a central role in this, as it enables damage and material fatigue to products to be detected. Digital twins are to be used in the project, which will enable the condition of products to be recorded and controlled.

PROJECT TPARTNERS: IMW TU Clausthal, Bernhard Olbrich Elektroinstallationen-Industrieanlagen GmbH, Robert Bosch GmbH, Hellmann Process Management GmbH & Co. KG

E-Mail: Dominique Briechle

[1] V. Forti, C. P. Balde, R. Kuehr, and G. Bel, "The Global E-waste Monitor 2020: Quantities, flows and the circular economy potential," 2020.


In addition to environmental and health damage caused by emissions such as noise, CO2, and air pollutants, the quality of life is also increasingly restricted by road traffic due to a large amount of land used (e.g., for roads, parking areas, loading/unloading areas). With the progressive growth in transportation, especially in commercial logistics transport, the situation has become even more acute in recent years. The development of technical, urban planning, and social concepts to satisfy society's mobility and logistical needs while also increasing environmental protection and urban quality of life is THE challenge of the next decade.

In the project HitchhikeBox, an intermodal hitchhike logistics system based on AI-based journey planning of self-organizing delivery boxes is to be developed. This is a technological platform for an integrated electric mobility and logistics system that enables reduced private/commercial trips while maintaining mobility and logistics, as well as reduced parking space requirements, by maximizing the efficiency of current vehicles without sacrificing flexibility. The main innovation of the project is bringing forth an automated, decentralized dispatching system for self-organized "intelligent" logistics boxes (HitchhikeBox) that enable intermodal Multi-Hop-Routing “hitchhiking” via local Micro-Hub-Depots. Moreover, it aims to increase the use of all-electric vehicles by participating regional vehicle operators and to take advantage of synergy benefits by bundling individual journeys that can be coordinated. The Institute for Systems and Software Engineering (ISSE) is developing a software solution for the “intelligent” reusable logistics box for its independent and automatic hitchhiking from an initial to the final point. This solution will have intelligent strategies for the execution of a transportation task. Also, the development and conceptual design of this dynamic-adaptive self-organizing system based on smart contracts.

PROJECT PARTNER: MoD Holding GmbH, TU Clausthal (Institute for Software and Systems Engineering), Institute for Enterprise Systems (InES) University of Mannheim, Brehmer GmbH & Co. KG, PrimingCloud GmbH, Blockchain Solutions GmbH, Overath GmbH

E-mail: Nelly Nicaise Nyeck Mbialeu

Digital Innovation Hub "Reallabor DCE"

The increasing tightening of global ecological conditions is pushing more and more for the development of new, green technologies and concepts. The infrastructure platform of the Digitized Circular Economy (DCE) reallab aims to establish innovations and models in the field of sustainable, digital technologies with its partners.

In doing so, the newly conceived model of the Circular Economy is at the center of the research orientation. The various sub-areas of the circular model form the building blocks of the real laboratory. In various sub-projects, it researches the application-oriented solution development of these sub-aspects and thus creates mechanisms for the implementation of the Circular Economy.

Furthermore, the networking of the various actors and the strengthening of their knowledge with regard to digitalization and sustainability is of outstanding importance. This is also made possible in the context of the creation of new business models, as well as the further training of the actors in future technologies. The goal is to create new synergy effects in joint projects and to develop best practice approaches that can be tested in the long term.

The guiding idea in the course of the Reallabor is the transformation of existing economic and production concepts towards the Circular Economy. We have set ourselves the goal of using the tools of digitalization to redefine product life cycles and establish alternative usage concepts in order to reduce the constant burden on the environment caused by the increasing consumption of short-lived products.

All further information about the project can be found on the Pages of the reallab DCE...

PROJECT PACKAGE:You can find the project poster Here as PDF.

Contact person:
E-mail: Dominique Fabio Briechle


In the LifeCycling² project, reconfigurable design concepts and services for the resource-efficient (continued) use of e-cargobikes are being developed. In our subproject, we are researching modular, adaptable software solutions for the use of software components in first and second use, including function updates. The first step is the development of reconfigurable software architectures and control units for the efficient modification of software functions (upgrades, function changes) as well as the development of accompanying services for resource-efficient use. Based on the defined requirements, necessary information services for private and commercial use of e-cargobikes are further defined. The Institute for Systems and Software Engineering (ISSE) is also developing a software solution for objective assessment of technical and economic value, e.g. based on charging cycles and residual capacity.

PROJECT PARTNERS: TU Braunschweig (Institute for Design Engineering, Institute for Social Sciences), baron mobility service GmbH, TU Clausthal (Institute for Software and Systems Engineering), Electrocycling GmbH, Sense4Future GmbH, Stöbich technology GmbH

PROJECT PLATE: You can find the project poster here as PDF.

PROJECTVIDEO: You can find the project video here:

E-Mail: Marit Mathiszig

Effizient Nutzen

New production of electrical and electronic products in low-wage countries is currently often cheaper than repair, refurbishing and remanufacturing processes in high-wage locations such as Germany. Ever shorter innovation cycles are also generating new customer needs. Despite the desire of many people to have the option of buying used equipment or repair solutions for products for which there is no longer a warranty/guarantee, repair or refurbishing is generally not considered even for high-value electrical (electronic) products, or is simply not possible for the layperson even under instruction, e.g. in repair cafés. This is due in particular to the fact that repair or refurbishment is becoming increasingly difficult because, in addition to the complex electronic hardware, software availability and security as well as networking know-how play a decisive role. As a result, end-of-life products are replaced by new products after a limited period of use and, at best, recycled for materials/energy.

The scale of this global issue reached a new high of 44.7 million tons of electronic waste in 2016. This results in considerable environmental impacts and resource losses, which could be avoided by a closed-loop recycling based on optimized cascade use and extended use as well as their targeted recycling in the end-of-life as envisaged in the project. The project EffizientNutzen therefore aims to significantly increase the lifetime and, in particular, the useful life of these products. In further subprojects, digitalization strategies are to be developed to close the information gaps.

PROJECT PARTNERS: The Institute of Automotive Economics and Industrial Production (AIP), Research for Sustainable Development (FONA), IWF, ReziProk, TU Braunschweig

PROJECT PLATE: You can find the project poster here as PDF.

E-Mail: Dominique Briechle

Recycling 4.0


The aim of the project Recycling 4.0 is to improve the recycling process through a targeted and controlled exchange of information. All players, from raw material producers to OEMs and recyclers, are to be networked with each other and form a circular economy. The focus of our subproject is on the development of an information and data marketplace where the different actors can network to exchange information.

The project video "Digitization as the key to the Advanced Circular Economy".

Due to the advancing expansion of electromobility, more and more lithium-ion batteries are being sold. The scarce raw materials they contain, such as lithium and cobalt, make efficient recycling essential. This presents manufacturers and recyclers with new challenges in terms of technology and available information. The ERDF innovation alliance "Recycling 4.0" is looking at possible applications of digitalization in recycling to meet the challenges posed by increasingly complex products and a lack of information. The focus is on the extraction, dissemination and use of information through Industry 4.0 along the entire value chain. To this end, information dissemination will be enabled via an information marketplace as well as direct sharing between actors. The additional information will be used to increase efficiency in recycling, close material cycles, recover critical raw materials and enable decision support. The project thus contributes to sustainable e-mobility, enables resource conservation and reduces Germany's dependence on raw materials.

Click here for the project video:

E-Mail: Marit Mathiszig


DACS: Dependable and Autonomous Cyber-Physical Systems


Der neue 5G-Kommunikationsstandard spielt eine entscheidende Rolle für innovative Anwendungen, insbesondere im Bereich des autonomen Fahrens. Dieses Projekt zielt darauf ab, ein 5G-Campusforschungsnetz zu etablieren, um die Anforderungen des autonomen Fahrens auf Level 3-5 ohne Sicherheitsfahrer an Bord und stattdessen mit einer technischen Remote-Aufsicht (Leitstand) zu erfüllen. Gemäß dem Gesetz zum autonomen Fahren der Bundesrepublik, das am 28.7.2021 in Kraft getreten ist, ist erstmals die Möglichkeit geschaffen worden, autonomes Fahren auf Level 3 ohne Sicherheitsfahrer und mit einer Remote-Aufsicht (im Sinne von einem 5G Remote-Leitstand im Projekt) im Regelbetrieb umzusetzen. Für diesen 5G Remote-Leitstand muss ein 5G-Netzwerk etabliert werden, das die erforderlichen Safety und Security-Eigenschaften des 5G-Funkstandards erweitert, um die Qualität des Funknetzes während der Teleoperation vorherzusagen und das Netz vor Angriffen zu schützen.

Zudem sollen autonome Fahrzeuge in der Lage sein, komplexe Fahrumgebungen zu bewältigen, wie etwa schlecht einsehbare Kreuzungen. 5G-Infrauktursensorik kann hierbei unterstützen, indem sie einen Ersatz für den analogen Verkehrsspiegel bietet, wie er von menschlichen Fahrern genutzt wird. Schließlich muss ein betriebssicheres KI-basiertes Fahrsystem (5G Fail-Operational) mit Shadow-Mode entwickelt und über 5G mit der 5G-Infrastruktursensorik und dem 5G Remote-Leitstand zu einem integrierten 5G-basierten Mobilitäts- und Logistikdienstleistungssystem verbunden werden. Ein autonomer CampusShuttle wird als Demonstrationsbetrieb aufgebaut. Das 5G-Campusforschungsnetz wird als Plattform und Testfeld für laufende und zukünftige Forschungsprojekte genutzt und steht den beteiligten Instituten und Zentren zur Verfügung. Die Projektergebnisse können als Modell für den Aufbau autonomer Shuttles in anderen 5G-Reallaboren und Gebieten mit 5G-Netzversorgung dienen. Das Projekt wurde in der vierten Beiratssitzung des Verbundvorhabens 5G-Reallabor in der Mobilitätsregion Braunschweig-Wolfsburg vorgestellt und einstimmig positiv unterstützt. Dies zeigt die ausgezeichnete Einbettung des Projekts in die 5G Innovationsregion Braunschweig-Wolfsburg.

E-Mail: Meng Zhang


Environmental perception systems of autonomous vehicles are nowdays using extensive AI-based algorithms. Established techniques and methods to proof correctness regarding safety are reaching its limits. Even if a lot of driving scenarios and millions of kilometers are used for testing, an overall safety assurance during design time is not possible. The goal for the project SafeWahr is to duly detect violations of safety critical specifications and uncertainities of AI-based environmental perception systems of autonomous vehicles. In case a violation was detected the autonomous vehicle will then continue its driving task with restricted functionality in a so called fail-operational mode.

One approach for handling situations, which are not known during design time is to partially shift the safety assurance to the operating time. Ultimately, some kind of "operation time certification" is aimed. For this purpose an operation time monitoring architecture for self-diagnosis will be developed in SafeWahr. Within this operation time monitoring architecture three types of monitors will be included: (1) A Situation Monitor, which determines if the current situation was considered during design time, (2) a Validity Monitor, which determines if the AI-based perception is safe regarding its results and (3) a Function Monitor, which determines if the target function acts corretly regarding safety specifications.

Contact person:
E-Mail: Iqra Aslam

Mobility Lab

The Mobility Laboratory is a cross-sectional internal project that aims to connect the current dynamically adaptive software platform with safeguarding mechanisms. The platform to be developed should be used in as many projects as possible. Furthermore, the mobility laboratory is a place where mainly students come together to work on topics of autonomous driving and machine learning. The laboratory has two RaspberryPi vehicles, a 1:8 model vehicle and an indoor positioning system.

E-Mail: Adina Aniculaesei

Methoden zur ODD Beschreibung

Verschiedene Gesetzesinitiativen sehen die Operational Design Domain (ODD) als Ausgangspunkt einer Entwicklung von Automated Driv-Fahrsysteme (ADS). In diesem Zusammenhang wird die ODD von mehreren Automotive OEMs als zentrale Wissensbasis nicht nur für die Systementwicklung und -validierung, sondern auch für sondern auch für die Analyse von Business Cases und für den Systembetrieb. Daher ist es für OEMs eine ODD entwickeln zu können, die in sich konsistent ist, ein zentrales Anliegen. Eine ODD Beschreibung ist in sich konsistent wenn es keine Widersprüche zwischen den ODD Constraints gibt.

Standard-Verifikationswerkzeuge, z.B. Solver für Satisfiability Modulo Theories (SMT), können verwendet werden, um die Konsistenz einer ODD-Beschreibung zu überprüfen Zusätzlich können solche Werkzeuge verwendet werden eine ODD Beschreibung gegenüber konkreten Fahrsituationen zu überprüfen um festzustellen welche Situationen für den Betrieb des ADS sicher sind. Allerdings ist es oft so, dass die Verwendung eines bestimmten Verifikationswerkzeugs umfangreiche Kenntnisse der formalen Spezifikations Sprache, die das jeweilige Werkzeug als Eingabe akzeptiert, erfordert.

Aktuelle Standardisierungsbemühungen, z.B. durch ASAM Arbeitsgruppe, empfehlen die Beschreibung einer ODD durch semi-formale Sprachen, z.B. auf Basis von YAML, um auch Stakeholder anzusprechen die kein Hintergrund in formalen Methoden haben. In diesem Projekt bauen wir ein Konzept für die Beschreibung einer ODD auf, das die Brücke einspannt zwischen den semi-formalen Sprachen, die durch aktuelle Standardisierungsbemühungen empfohlen sind, und der formalen Standardsprache, die für die Interpretation und Auswertung der ODD benötigt wird, einspannt. Dieses Konzept demonstrieren wir anhand von Beispielen aus der Industrie die durch ein Automobilhersteller im Rahmen des Projektes uns zur Verfügung gestellt wurden.

E-Mail: Adina Aniculaesei


Today’s increasing logistic operations constitute increasing challenges for traffic in urban areas. In addition to traffic congestion caused by logistic operations, safety problems are also getting increasingly critical. Aiming to tackle these challenges, the LogiSmile partners are piloting a fully autonomous delivery system in different pilot cities: Esplugues de Llobregat, Spain, Hamburg in Germany, and Debrecen in Hungary. This autonomous delivery system is realized based on the cooperation between an autonomous hub vehicle (AHV) and smaller robots, which are autonomous last-mile delivery devices (ADD). Furthermore, an additional remote back-end control center for managing the communication between the AHV and the ADD, acquiring the data while operating, and optimizing the fleet operations with efficient routing algorithms. As an NFF member, ISSE participates in the development of the remote back-end control center, aiming to provide fail-operational solutions in the case of critical situations that cannot be handled by the AHV autonomously.

E-Mail: Adina Aniculaesei

Future Lab Mobility

The project of “Zukunftslabor Mobilität” (in English: Future Mobility Lab) focuses on the application-oriented research work by using different mobility-related technologies. In four well-defined interdisciplinary fields (Collaborative Research Fields (CRF 1-4)), potential environmentally and socially compatible mobility solutions such as connection of systems, human users and infrastructures relying on the digitalization technology will be investigated and developed, with consideration of concrete use cases about future mobility in Lower Saxony of Germany as well as related methods and approaches for the development of innovative mobility solutions.

In the scope of this project, TU Clausthal is involved together with other project partners in CRF 2, which focuses on relevant topics of smart mobility data handling. As known, the data plays a significantly important role as the basis of autonomous driving (CRF 1) and additionally as the precondition of the development and implementation of appropriate mobility services and corresponding business models (CRF 4). In CRF2, scientists from the research fields of communication technology, information and software engineering work closely together to develop methods to realize a safe and reliable data acquisition, evaluation and fusion of mobility data. For this purpose, the approaches for reliable (safety, security and privacy) data collection and processing, which fulfill the legal framework like DSGVO, will be developed and demonstrated with consideration of both phases for system development and operation. The approaches for the development of a reliable data architecture, methods and approaches for the standardization of data handling and fusion, as well as the assessment and assurance of data quality will be also developed and tested in the project.

Contact: e-mail: Abhishek Buragohain


ETCE: Emerging Technologies for a Circular Economy

The Limits to Growth – Sustainability and the Circular Economy (MOOC)

Project-Website: Link

In research, teaching, and technology transfer, the overarching theme for the Clausthal University of Technology, its guiding principle, is the Circular Economy. While degree-specific courses deal with selective aspects of the Circular Economy, there is a gap with respect to a course for all students that teaches the essential concepts and fundamentals of sustainability, environmental pollution, resource scarcity, and the Circular Economy.
The course “The Limits to Growth – Sustainability and the Circular Economy” aims to close this gap. As a general foundation course, there are no access restrictions. Furthermore, the course can be taken by Bachelor and Master students and PhD students. In order to accommodate a large number of international students, the course is conducted in English.

The Limits to Growth (LTG) course is open to all students of the Clausthal University of Technology as well as other organizations. Therefore, an adapted teaching format and appropriate technical support are required. The LTG course will be implemented as a Massive Open Online Course (MOOC), which scales traditional forms of knowledge transfer (slides, recordings, etc.) via an IT-driven infrastructure and expands them with additional technical features such as forums, quizzes and other forms of (semi-) automated tasks or tasks to be assessed via peer feedback. In addition, corresponding MOOC platforms offer participants interaction and networking opportunities and the possibility of asynchronous learning, in which students study according to their schedule. They are given access to all teaching and learning materials, which are discussed in regular meetings – followed by optional question-and-answer sessions to clarify any questions.

All teaching and learning materials, recordings, homework, tutorials, etc. are published under an open-source licence (CC-BY-SA-4.0) and are thus freely available as directly integrable teaching units or as a basis for further courses.

Prof. Dr. Benjamin Leiding

SoRec – Digitalisation of Sorting Processes for Fine-Grained, Metal-Containing Waste Streams in the Recycling Industry


Closing material and resource cycles is an integral part of the circular economy. Many products (especially electronic devices) are becoming increasingly complex regarding their structure and raw materials. To recover raw materials, the products must be broken down into ever-finier grain sizes and subsequently sorted. In general, dry sorting processes achieve good sorting results in the fine particle size range with lower throughputs. However, when increasing the throughput, the quality of the sorting results decreases considerably. In order to operate profitably, sorting machines must work as close as possible to the tipping point between good quality and maximum throughput while also minimising maintenance downtimes.

The SoRec project focuses on digitalising sorting processes for fine-grained, metal-containing waste streams in the recycling industry. By installing state-of-the-art industrial line cameras and sensors, we are digitizing the conventional sorting method on a moving belt. With the help of advanced AI models and algorithms in deep learning and machine learning, our system can accurately detect materials on the conveyor belt, classify them based on size, shape, and color, and even find their precise edges. With the capability to identify multiple layers of materials, the AI model provides valuable density and volume estimation. To ensure real-time efficiency and control, we have integrated the AI model with powerful computer vision techniques, which handle crucial image processing tasks. This seamless collaboration between AI and computer vision allows us to estimate the belt’s speed and detect any anomalies, ensuring precise sorting and preventing belt misalignments. Our materials, measuring just 1 mm in size, demand meticulous attention to detail, necessitating high-level zoom capabilities for precise annotations. With this innovative AI-driven system, we are taking a significant step towards automating and optimizing the sorting process, enhancing productivity, and elevating the industry to new heights of accuracy and efficiency.

Prof. Dr. Benjamin Leiding

ML4E: Machine Learned Models for Engineers

Sensorentwicklung für Produkte des baulichen Brandschutzes zur Sicherstellung deren Funktion, für Smart Maintenance und I 4.0

Elements of safety-relevant solutions, such as fire doors, differ very significantly from machines in industrial plants in terms of their costs and connection to the energy supply, a variety of challenges need to be tackeld to turn safety-relevant systems into Cyber Physical Systems. The project deals with the necessary developments. These range from the design of specific sensors and actuators, which can also be produced cost-effectively in small quantities, to IT solutions for condition monitoring and automated functional testing of safety-relevant systems, and the establishment of methodological competencies for the development process of such Cyber Physical Systems.

Contact: Stefan Wittek

Matures Ölfeld

Der Einsatz von KI soll dazu dienen, die spezifischen Kosten der Ölförderung zu senken, ohne Kompromisse in der Sicherheit, des Umweltschutzes oder der Integrität einzugehen. Die Studie soll vor diesem Hintergrund aufzeigen, welche Einsatzbereiche für KI in einem maturen Ölfeld bestehen und wie diese wirtschaftlich zu bewerten sind. Die Einsatzmöglichkeiten zur Mehrwertbestimmung künstlicher Intelligenz im maturen Ölfeld sollen in einer Literaturstudie durchgeführt werden. Betrachtungsgegenstand ist hierbei der abgegrenzte Bereich von der Sonde selbst (inkl. Pumpe) bis zur Übergabestation inkl. Pipelinesystem. Hierbei werden die Einsatzmöglichkeiten und Potentiale von KI-Techniken aus den Bereichen Knowledge Discovery in Databases, Predictive Maintenance, Zeitreihenprognose, Modellbildung, modellbasierten und modellfreien Optimalsteuerungen und -regelungen untersucht und beschrieben.

Ansprechpartner: Stefan Wittek

AI-based flood warning system for the city of Goslar

The project "AI-based flood warning system" is a practical application of AI forecasting systems to safety-critical areas. The application domain is the catchment area of Goslar with the existing sensor infrastructure and its historical data. The task of the artificial intelligence is to observe the current status of weather such as precipitation, soil moisture, solar radiation over a past period of time and put it in relation to the current water level in the settlement. A possible correlation is assumed, so that dangerous, sudden peaks can be predicted at an early stage. The forecasting method is thus a core element for the coordination of a series of structural measures for the prevention of sudden flooding events.

Contact: Dimitri Bratzel,Stefan Wittek



Der Einsatz von KI im Kontext der prädiktiven Instandhaltung von Abwasserkanälen birgt Vorteile, sowohl im Sinne der Kostenersparnis als auch der Vorbeugung von Totalausfällen und daraus ggf. resultierenden Verunreinigungen des Grundwassers. Ziel ist es hierbei, bisher analog ausgeführte Tätigkeiten, so beispielsweise die Klassifikation von Schäden am Kanal, in Zukunft automatisiert erfolgen zu lassen. Darauf aufbauend lässt sich insbesondere eine Strategie ableiten, mit der sich der zukünftige Instandhaltungsprozess optimieren lässt. Zum Zwecke der Nutzerfreundlichkeit wird zusätzlich ein 3D-Modell des gesamten Netzes entwickelt, mit der sich sowohl die Klassifikation, als auch die Prognose des Zustandes visuell einsehen lässt. Die geplanten Schritte hierbei reichen von der theoretischen Erarbeitung eines Konzepts, bis hin zu einem einsatzfähigen Prototypen. Die eingesetzten Techniken umfassen klassische statistische Methoden, bis hin zu maschinellen Verfahren im Sinne des Predictive Maintenance und der Klassifikation.

Ansprechpartner: Benjamin Acar

National Research Data Infrastructure for Engineering Sciences (NFDI4Ing)

NFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR – findable, accessible, interoperable, and re-usable.The consortium represents researchers from all engineering disciplines. It offers a unique method-oriented and user-centered approach to make research data FAIR - discoverable, accessible, interoperable and re-usable.
NFDI4Ing defines a total of 7 archetypes for scientists as users of research infrastructure.The ISSE is mainly active in the BETTY archetype: engineering research software.  This includes in particular the code of simulation models, and questions concerning the integration of heterogeneous models, as well as their approximation with the help of AI methods.

Contact: Stefan Wittek

Harz water reservoir

In the north of Germany, the Harz, with its annual precipitation of 1200 mm on the Clausthal plateau and its storage capacity, through the numerous tailings ponds and dams, supplies regions as far away as Bremen with drinking water. Due to this importance in drinking water supply, but also due to the existing underground infrastructure of the mining heritage and due to its central and cross-border location, the Harz region is of outstanding water management importance in Germany. AI-supported water management can help to mitigate the extreme weather events that occur due to climate change, such as heavy rainfall events with flood disasters or long periods of drought. Possible solutions should both show the user options for action and make the use of conventional, numerical models more efficient and effective. The Harz region is to be developed as a digital twin and serve as a basis for the application of complex simulations over a wide area. The focus of the modelling is on an AI-hybrid model that combines both conventional and innovative approaches and thus generates added value in terms of water quality and quantity.

Contact: Benjamin Acar

AKI - Repository research

AKI is a collaborative project, between the Institute of Software and System Engineering, Öko-Institut e.V. and the Federal Office for the Safety of Nuclear Waste Disposal. The Achivement of the Project is to research the use and application of artificial intelligence, i.e., AI, in the selection process in the field of repository research. Foremost, the structuring and evaluation criteria of the possible applications of the AI are to be carried out. This evaluation shall be considered concerning scenarios or geoscientific questions. The result should be a transparent and explainable comparison, classification and evaluation of the possibility to apply and usefulness of machine learning methods in the field of geosciences. Thereby, research gaps in the geosciences shall be identified, and R&D needs shall be named. The essential use of AI will be made in interacting with a so-called expert system. One essential transparency criterion is that artificial intelligence will take over particularly time-consuming and repetitive tasks, but the human decision-maker should make the final decision in the so-called expert system.

Contact: Dimitri Bratzel


This project aims to develop subject-specific and interdisciplinary study and learning content on artificial intelligence, particularly in the field of machine learning, for all status groups in universities and to enable its use by external stakeholders (i.e. the interested public) across universities.

universities and to enable their use by external stakeholders (i.e. interested members of the public). As a decentralized platform for these coordinated development activities, an AI hub will be established, which will enable coordinated development and usage planning as well as the implementation of didactically secured and innovative learning and mediation concepts of methods and application-oriented tools in the context of data-driven modeling, analysis and simulation for different target groups and promote AI-based innovations through participatory formats.

Contact: Stefan Wittek

Trink Extrem

The aim of the research project is to develop methods, precautionary concepts and transferable practical tools for extreme events for water supply companies and the water management administration. In this context, relevant sub-areas of water supply from water extraction and treatment to operation and water use are addressed, as are the different types of raw water: groundwater, spring water and surface water. In representative model regions of the German long-distance and surface water supply, an exemplary implementation takes place. This allows further long-distance and surface water suppliers or local water suppliers to adapt their respective different local requirements in a practical way. Ultimately, the project should also provide impulses for rule-making, e.g. in risk management and for cooperation with state authorities in extreme situations. The project should also raise awareness for the challenges of water supply during extreme events. A modern public relations work shall sensitize water supply companies with comparable challenges as well as interested end customers for the topic of extreme events and show the possible solutions developed in the project.

Contact person: Stefan Wittek


This project is part of the CircularLIB research training group on the cycle-oriented production of lithium-ion batteries. In this program the three universities TU Braunschweig, TU Clausthal and Leibnitz Universität Hannover are cooperating with the Fraunhofer Institute for Surface Engineering and Thin Films IST.

The aim is to research a hybrid AI-based modeling approach for lithium-ion batteries. Recently, machine learning has also been increasingly used in the context of classical engineering disciplines. In particular, deep learning approaches provide fast and relatively accurate modeling approaches based on learning specific tasks from examples. This is of crucial interest especially in the context of high-dimensional complex systems, where the underlying physics is not fully known or the computational effort for simulation with conventional numerical methods is high. Another aspect driving the use of deep learning approaches is the need for fast models that can be used in iterative tasks such as optimization and control. However, data-driven models are typically black-box approaches that are developed based only on data and do not explicitly incorporate physical knowledge or constraints into model development. This can result in such models lacking robustness and accuracy, especially with limited training data.

Recent advances in physically-informed machine learning have led to a number of approaches that are well suited for solving various types of partial differential equations (PDEs). This project aims to develop models based on both data and available physical knowledge for lithium-ion batteries using physically informed machine learning techniques that allow for a hybrid AI-based modeling approach.

Contact: Hamidreza Eivazi Kourabbaslou