Defect-free metal additive manufacturing

Additive manufacturing (AM) of metal products is achieved by selective laser melting (SLM) at the surface of a metal powder bed. SLM locally fuses the powder to construct a thin object layers (~30μm). The object then sinks in the bed and a wiper recoats the top of the object with an equally thin layer of fresh powder. The laser then adds another layer and the process is repeated until the object is fully formed. To fulfill the need for quality assurance, without tedious post-process quality control, a prediction model using process monitoring data to uncover defects is developed in this use-case. Even though the process is fast, sensing is the easy step for in-process monitoring. The difficulty lies in the development of algorithms to detect, predict, and ultimately prevent defects. Handling massive amounts of data in combination with real-time processing forms a technical challenge, as does the correct interpretation of measured data in predicting the presence of defects and their location. The ambition of CoE RAISE for the AM use case is to upgrade machine learning models to predict defects from shallow to deep models, and to upgrade training data to multiple modalities (sensor fusion) and by at least an order of magnitude in volume. Thereby, the error rate in keyhole porosity prediction is reduced by a factor of two or more and the robustness of the model for different manufacturing parameters is increased. This enables quality certification for porosity count based on build-time measurements only, eliminating a key barrier for increasing AM industrial output of high-value products. The data and model are analyzed to investigate more thoroughly the manufacturing parameter space, and to distill the model into a smaller, real-time capable model. The distilled model supports the real-time control of the process, considerably reducing the defect rate.



The Palabos library is a framework for general-purpose computational fluid dynamics (CFD), with a kernel based on the lattice Boltzmann (LB) method. It is used both as a research and an engineering tool: its programming interface is straightforward and makes it possible to set up fluid flow simulations with relative ease, or, if you are knowledgeable of the lattice Boltzmann method, to extend the library with your own models. Palabos stands for Parallel Lattice Boltzmann Solver. The library’s native programming interface in written in C++. It has practically no external dependencies (only Posix and MPI) and is therefore extremely easy to deploy on various platforms. Additional programming interfaces are available for the Python and Java programming languages, which make it easier to rapidly prototype and develop CFD applications. There exists currently no graphical user interface; some amount of programming is therefore necessary in order to get an application running.

CoE: ComBioMed

Virtual Assay

A user-friendly software to perform in silico drug trials in population of human cardiac models contributing to the uptake of in silico modelling and simulations in industry and regulatory paradigms, and demonstrating accurate and mechanistic predictions of drug-induced cardiac pro-arrhythmic toxicity.

CoE: BioExcel


Sound engineering, in more detail, refers to acoustic and tactile Engineering (ACUTE) being driven forward by the Simulation and Data Lab (SDL) ACUTE in Iceland in collaboration with FZJ in RAISE. There is an essential element of ACUTE in individual 3D spatial auditory displays for immersive virtual environments. 3D sound technologies can provide accurate information about the relationship between a sound source and the surrounding environment, including the listener herself/ himself. This information cannot be substituted by any other modality (e.g. visual or tactile). Nevertheless, today’s spatial representation of audio tends to be simplistic and with poor interaction capabilities, being multimodal systems primarily focused on graphics processing and integrated with basic audio solutions. This use case in RAISE aims to convey environmental information via acoustics using binaural sounds (3D). Typically, binaural audio technologies rely on head-related transfer functions (HRTFs), specific digital filters that capture the human head’s acoustic effects. Obtaining personal HRTF data is only possible with expensive equipment and invasive recording procedures.


Urban Air Pollution Pilot Use Case

The vision of HiDALGO’s urban air pollution application is to create cleaner air in cities by using high performance computing (HPC) and mathematical technologies. To this end, the project will provide policy makers and society with an easy-to-use computational tool as a service that accurately and quickly forecasts air pollution in cities with very high resolution. Furthermore, a traffic control system will be developed as well to minimize air pollution while considering traffic flow constraints. The main part of the project is a HPC-framework for simulating the air flow in cities by taking into account real 3D geographical information of the city, applying highly accurate computational fluid dynamics (CFD) simulation on a highly resolved mesh (1-2 m resolution at street level) and using weather forecasts and reanalysis data as boundary conditions. Emission is computed from the weakly coupled traffic simulations and general emission data of other sources. For the demonstration area, the city of Győr, Hungary, a traffic monitoring sensor network with a plate recognition camera system will be developed. The monitoring system will be completed with affordable air quality sensors as well.


Migration Pilot Use Case

In the last few years, a huge amount of people were forced to leave their homes. One of the major issues hereby is to forecast where these displaced people will arrive, which would allow decision makers and NGOs to allocate humanitarian resources accordingly. To predict possible destinations of refugees coming from conflict regions, we have developed a simulation framework. This framework relies on agent-based simulations, and makes use of real world data from UNHCR, ACLED, and Bing Maps. Applying this simulation framework to three major African conflict regions, we obtain results which consistently predict more than 75% of the refugee destinations correctly after 12 days. In HiDALGO the main goal is to improve our agent-based simulation framework in terms of accuracy, resolution, clarity, and performance, and to incorporate a range of relevant phenomena in its computations. For instance, we are developing models that incorporate precipitation data from ECMWF, and plan to exploit telecommunications data from MOONSTAR to help validate our simulations. In addition, we are establishing new techniques to speed up the construction of our simulations (e.g., by automatically extracting and converting geographical data) and to establish better ways to visually explore our simulation output. Our final goal in HiDALGO is to enable simulations on a large scale to accurately forecast where displaced people, coming from various conflict regions of the world, will eventually arrive to find safety. Our approach could assist in case of a global crisis in a number of crucial ways. Firstly, it could forecast refugee movements when a conflict erupts. Secondly, it could acquire approximate refugee population estimates in regions where existing data is missing or incomplete. Finally, it could investigate how border closures and other policy decisions are likely to affect the movements and destinations of refugees.



HYPERstreamHS inherits the core features of the HYPERstream routing scheme recently presented in the work from Piccolroaz et al. (2016), while improving it by means of a dual-layer MPI framework and the inclusion of explicit modelling of streamflow alterations due to Human Systems (hence, the HS suffix to the model’s name). HYPERstream is a multi-scale streamflow routing method based on the Width Function Instantaneous Unit Hydrograph (WFIUH) approach; this approach has been specifically designed for reliably simulating the relevant horizontal hydrological fluxes preserving the geomorphological dispersion of fluxes and thus being able to perform well at different scales, from a single catchment to the meso-scale

CoE: EoCoE


SHEMAT-Suite is a finite-difference open-source code for simulating coupled flow, heat and species transport in porous media. The code, written in Fortran-95, originates from geoscientific research in the fields of geothermics and hydrogeology. It comprises: (1) a versatile handling of input and output, (2) a modular framework for subsurface parameter modeling, (3) a multi-level OpenMP parallelization, (4) parameter estimation and data assimilation by stochastic approaches (Monte Carlo, Ensemble Kalman filter) and by deterministic Bayesian approaches based on automatic differentiation for calculating exact (truncation error-free) derivatives of the forward code.

CoE: EoCoE


ParFlow is known as a numerical model that simulates the hydrologic cycle from the bedrock to the top of the plant canopy. The original codebase provides an embedded Domain-Specific Language (eDSL) for generic numerical implementations with support for supercomputer environments (distributed memory parallelism), on top of which the hydrologic numerical core has been built. In ParFlow, the newly developed optional GPU acceleration is built directly into the eDSL headers such that, ideally, parallelizing all loops in a single source file requires only a new header file.

CoE: EoCoE