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    Research DataOpen Access
    Zusätzliche Daten Doktorarbeit M_Dollinger
    (2025-10-14) Dollinger, Manfred
    Untersuchung des Verbrauchs und der Treibhausgasemissionen von batterieelektrischen und Brennstoffzellen-betriebenen Personen- und Lastkraftwagen mit modellgestützten Prognosen bis 2050 Anhang 01 Fahrzeugdaten der Modelle Anhang 02 Fahrroutendetails eigene Tests Anhang 03 Fahrzyklen Anhang 04 Trägheitsmomente der rotierenden Massen Anhang 05 E-Motor Kennlinienfeld Anhang 06 H2-Speicherung Anhang 07 Antriebstechn. Daten der Modelle Anhang 08 Gewichtskonfiguration im Detail Anhang 09 Simulator komplett Anhang 10 FCEV/FCET-Antriebsmodell Anhang 11 ICEV/ICET-Antriebsmodell Anhang 12 Zubehör-Berechnungsbeispiel_1 Anhang 13 Truck_Data Anhang 14 Zubehör-Berechnungsbeispiel_2_lKW Anhang 15 Truck_Modelldaten Anhang 16 FC_Characteristics Anhang 17 THG-Faktoren Anhang 18 Bevölkerungsstatistik Anhang 19 Ergebnisse PkW groß Details Anhang 20 Energieanteile für Vorheizen Batterie und FC Anhang 21 Vergleichstabelle ADAC/Hersteller Anhang 22 Verbrauchsanteile LkW Anhang 23 Transportrouten Anhang 24 Hoekstra_Papier
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    Research DataOpen Access
    Design strategies for stack-based piezoelectric energy harvesters near bridge bearings
    (2025-06-24) Mattauch, Philipp; Schneider, Oliver; Fischerauer, Gerhard
    FE data contains the Ansys Mechanical APDL input files (*.txt) and the recorded time series results for the different load cases and stiffnesses of the COMBIN14 element representing the piezoelectric Energy harvesting system (*.csv). A "," is used as a delimiter between the individual columns and a "." is used as the decimal separator. In addition the simulation results used to generate the figures 10, 12, 13 and 14 are provided as MATLAB *.mat files containing input Parameters, time series data and the converted energy. The traffic data is available from the Bundesamt für Strassenwesen (BaSt) online: https://www.bast.de/DE/ 352 Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Aktuell/zaehl_aktuell_node.html?cms_detail=9237&cms_map=0 (accessed on 5 May 2025).
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    Research DataOpen Access
    Data from: Greenhouse gas fluxes from two drained pond sediments: a mesocosm study
    (2025-02-14) Borken, Werner
    Ponds can store large amounts of organic matter (OM) in their sediments, often accumulated over long periods of time. Sediment OM is largely protected from aerobic mineralization under water saturated conditions but are vulnerable when exposed to oxygen during periods of drought. As climate change progresses, drought periods are likely to occur more frequently and may affect OM mineralization, and thus the release of greenhouse gases (GHGs) such as carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from pond ecosystems. Therefore, we aimed to test how GHG emissions and concentrations in the sediment respond to drought by gradually decreasing water levels to below the sediment surface. To this end, undisturbed sediment cores from two small ponds with distinct watershed and water chemistry characteristics were incubated in mesocosms for 118 days at 20°C. Water levels were sequentially tested at 3 cm above the sediment surface (Phase I) and at the level of the sediment surface (Phase II). In Phase III, water levels were continuously lowered either by evaporation or by active drainage including evaporation. Mean CH4 fluxes of both ponds were high (21 and 87 mmol m-2 d-1), contributing 90 and 96% to the GHG budget over the three phases. The highest CH4 fluxes occurred in Phase II, while active drainage strongly reduced CH4 fluxes in Phase III. A multivariate analysis suggests that dissolved organic carbon and sulphate were important drivers of CH4 fluxes in Phase III. CO2 and N2O fluxes also responded to declining water levels, but their contribution to the GHG budget was rather small. Both gases were primarily produced in the upper sediment layer as indicated by highest concentrations at 5 cm sediment depth. Compaction of sediment cores by water level lowering increased bulk density and maintained high water contents. This side effect, retarding the drying of the sediment surface, was possibly relevant for the GHG net emission of the sediments in Phase II and III. Overall, GHG fluxes from the sediments exhibited high sensitivity to falling water levels. This study suggests that drying pond sediments have great potential to emit large amounts of GHGs to the atmosphere in the event of drought, representing hot spots of GHGs in the landscape.
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    Research DataOpen Access
    Determination of the bentonite content in molding sands using AI-enhanced electrical impedance spectroscopy
    (2024) Ma, Xiaohu; Fischerauer, Alice; Haacke, Sebatian; Fischauer, Gerhard
    MUT-InfoMo data contains the measured raw impedance data recorded by Agilent E4980A LCR meter in a frequency range from 20 Hz to 1 MHz, along with water content and sample density. The measuring cell consists of two opposing electrodes embedded within the inner wall of a cylindrical chamber with a diameter of 5 cm. The electrodes, made from C45 steel, are insulated from the chamber walls using retaining rings fabricated from polyetheretherketone (PEEK). The measurements were performed at laboratory conditions. Columns from 1:201 are the real components of Impedance spectrum. Columns from 202:402 are the negative imaginary component of Impedance spectrum. Column 403 is the water content, column 404 is the density, column 405 is the target variable bentonite content. Finally, an excel data files was stored, with which the FCNNs were trained and tested.
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    Research DataOpen Access
    MRT_2024_09_12 Medium-duty road freight transport - Investigation on battery electric and fuel cell trucks with a prediction until 2050 - Supplemetary Materials
    (2024-09-26) Dollinger, Manfred
    MRT_2024_09_12 contains the following data: Table S1: Drive_data_BET_FCET_short_tour_details Table S2: Drive_data_BET_FCET_long_tour_details Table S3: Truck_weight_short_tour_details Table S4: Truck_weight_long_tour_details Table S5: Truck_accessories_details Figure S6: Consumption_shares_deatils Figure S7: Profile_data_short_tour_confidential Figure S8: Profile_data_long_tour_confidential Table S9: Simulation_Data_long_tour Table S10: Simulation data_short_tour Table S11: DAF_XF_480_data_sheet Table S12: DAF_XG_480_truck_data_sheet