Magnonics addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operation in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors, which covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with the Boolean digital data, unconventional approaches, such as neuromorphic computing, and the progress toward magnon-based quantum computing. This article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of current challenges and the outlook of further development for each research direction.

Turbulence Induced Vibrations (TIV) or Turbulence Buffeting in a nuclear reactor is an undesirable side effect of achieving high coolant flow rates. The turbulent pressure field of these flows force the structure to vibrate within the setup. High amplitude vibrations of the fuel rods can lead to structural damage within the reactor and create a safety hazard. TIV is a difficult phenomenon to predict and limited experimental data is available. Therefore, the most pragmatic method of estimating behavior of structures is through computational approach. Fluid-structure interaction problems like these are tackled by partitioned coupling of CFD (Computational Fluid Dynamics) and CSM (Computational Solid Mechanics) solvers. To simulate TIV, it is necessary to capture the turbulent eddies by resolving the fluid flow field up to the smallest scales. Use of high-fidelity CFD solvers such as LES is an ideal approach. However, this modeling approach is very expensive when an FSI simulation is considered. Low-fidelity models such as URANS are able to simulate only the large fluid structures and are inadequate to model the fluctuation at the smaller scales, resulting in an inaccurate approach for TIV. This article proposes the use of Presure Fluctuation Model for FSI simulations to estimate the effects of pressure fluctuations (p⁰) which trigger TIV. In the proposed model, standard URANS (Unsteady Reynolds Averaged Navier-Stokes) models are complemented with this model to calculate p⁰. The calculated p⁰ is summed to the mean pressure (p) to acts as a source of external excitation for the structure in an FSI simulation. The proposed model has an advantage over high fidelity models in terms of reduced computational costs and accurate estimation of TIV. The method is validated against a DNS of a channel flow and the results have shown to be in good agreement. Afterwards, the model is applied to two coupled FSI test cases. The results conclude that the pressure fluctuation model is capable of simulating TIV without requiring an external perturbation to trigger these vibrations.

Natural organic matter (NOM) occurs throughout the hydrologic cycle, varying in both amount and character. In this paper, a description of NOM in surface and drinking water, in groundwater and in seawater is presented. Water samples representing these environments were collected and characterized using multiple NOM characterization techniques, including fluorescence excitation emission matrices (F-EEM) and size exclusion liquid chromatography with organic carbon detection (LC-OCD). The results show that the raw surface water as well as the treated water comprises mainly (>70%) of humic substances. The biopolymers, which are more readily biodegradable, contribute up to 2% of the NOM in the raw water but this is completely removed after treatment. For sea water samples, humic substances represent about 50% of the dissolved organic carbon concentration (DOC), while the fraction with size bigger than 20 kDa (biopolymers) represents about 7%. During soil passage, there was preferential removal of non-humic substances (i.e., biopolymers) from wastewater effluent-impacted surface water while the specific ultraviolet absorbance (SUVA), which reflects the aromatic characteristics of organics in a sample, showed an increasing trend along the depth of the soil column. This is a consequence of the removal of non-humic substances (biopolymers) which results in an increase in aromaticity.