Technische Universität Hamburg
  • Hamburg, Hamburg, Germany
Recent publications
Spontaneous imbibition (SI), which is a process of displacing a nonwetting fluid by a wetting fluid in porous media, is of critical importance to hydrocarbon recovery from fractured reservoirs. In the present study, we utilize deep and ensemble learning techniques to predict SI recovery in porous media under different boundary conditions including All-Faces-Open (AFO), One-End-Open (OEO), Two-Ends-Open (TEO), and Two-Ends-Closed (TEC). An extensive experimental dataset reported in literature representing a multiplicity of non-wetting fluid recovery-time curves was used in our analysis. The prepared dataset was used to learn diverse ensemble and deep learning algorithms of Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Voting Regressor (VR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The training procedure provided us with robust models linking the SI recovery to the absolute permeability (k), porosity (ϕ), characteristic length (Lc), interfacial tension (σ), wetting-phase viscosity (μw), non-wetting-phase viscosity (μnw), and imbibition time (t). To evaluate and validate the models’ prediction, we used two well-established approaches: (i) 10-fold cross-validation and (ii) predicting the SI behavior of a set of unseen data excluded from the model training. Our results illustrate an excellent performance of deep and ensemble learning techniques for prediction of SI with the test RMSE values of 4.642, 4.088, 4.524, 3.933, 3.875, 3.975, 4.513, and 4.807 percent for RF, GBM, XGBoost, LightGBM, VR, CNN, LSTM, and GRU models, respectively. The models have significant benefits in terms of accuracy and generality. Furthermore, they alleviate the sophistications associated with tuning the traditional correlation functions. The findings of this study can pave the road toward a more comprehensive characterization of fluid flow in porous materials which is important to a wide range of environmental and energy-related challenges such as contaminant transport, soil remediation, and enhanced oil recovery.
This paper proposes a dimensional optimization procedure for a laboratory scale two-body point absorber wave energy converter (WEC) using the design of experiment (DoE) methodology. Response surface methodology (RSM) is utilized to estimate a second order polynomial function correlating the average absorbed power, as the objective function, to five geometric parameters. Optimum values of parameters correspond to the peak of the surface fitted to the absorbed powers calculated for different sets of input parameters selected by the Box-Behnken design (BBD). The sensitivity of the objective function with respect to each parameter is investigated. The WEC is assumed to operate under regular waves in a specified range of frequency, 0.5–1 Hz. The amplitude and complex-conjugate controls are applied to keep the power take-off (PTO) system in optimum conditions. ANSYS-AQWA is used to calculate hydrodynamic parameters of the WEC required to solve the equations governing the absorbed power.
Natural convection in porous media has received increasing attention in recent years due to its significance in engineering applications. This process is traditionally analyzed by the solution of the classical Darcy-Oberbeck-Boussinesq (DOB) equations. According to the DOB equations, natural convection in porous media is exclusively dependent on the Rayleigh-Darcy number, Ra, while the Sherwood number, Sh, has a linear relationship with Ra at high Rayleigh numbers. However, these predictions conflict with experimental observations. In this study, we have performed a pore-scale resolved direct numerical simulation (DNS) study of natural convection in periodic porous media composed of two-dimensional square and circular obstacles. Based on our analysis, a new correlation of Sh for large Rayleigh numbers (Ra≥1000), low Darcy numbers Da, and high Schmidt numbers Sc (Da/Sc≤2×10−8) has been proposed, expressed as Sh=aRa1−0.2φ2+1, where a=0.011±0.002 is a pore-scale geometric parameter. The new correlation has been validated over a wide range of Rayleigh numbers, porosity values, and pore-scale geometries. Our DNS results also show that, with a decrease of porosity, it becomes more difficult for mega-plumes with low wavenumbers to enter the boundary layer. Low wavenumber motions decay much faster with a decrease of Da than the pore-scale motions near the wall. The volume-averaged dissipation rate nondimensionalized using the pore size εˆi has the scaling law εˆi∼Da in the internal region and εˆi∼Da1/2 in the near-wall region. We expect that these characteristics obtained from DNS also apply to natural convection in porous media with much lower Darcy numbers.
Experiment shows thin films of dealloyed nanoporous gold (NPG) spontaneously detaching from massive gold base layers. NPG can also densify near its external surface. This is naturally reproduced by kinetic Monte Carlo (KMC) simulation of dealloying and coarsening and so appears generic for nanoscale network materials evolving by surface diffusion. Near the porous layer's external surface and near its interface with the base layer, gradients in the depth-profile of a laterally averaged mean surface curvature provide driving forces for diffusion and cause divergences of the net fluxes of matter, leading to accretion/densification or to erosion/disconnection. As a toy model, the morphology evolution of substrate-supported nanopillars by surface diffusion illustrates and confirms our considerations. Contrary to cylindrical nanowires, the ligaments in nanoporous materials exhibit pre-existing gradients in the mean curvature. The Plateau-Rayleigh long-wavelength stability criterion is then not applicable and the disconnection accelerated.
Nanoporous anodic alumina (NAA) fabricated by anodization of aluminum is a versatile platform material with tailorable geometric, optical, and chemical features for specific light‐based technologies and applications. Recent advances in anodization technology have enabled a new generation of NAA‐based photonic crystals (PCs)—periodic dielectric nanoporous structures that selectively allow, forbid, and confine the flow of incoming electromagnetic waves of specific wavelengths across their structure. NAA–PCs provide exciting new opportunities to engineer light–matter interactions with versatility across the broad spectrum, from UV to IR. But despite these fundamental advances, demonstrations of sunlight‐harvesting technologies based on NAA–PCs are still limited. Herein, an up‐to‐date summary of recent advances in NAA–PC technology is provided, and proof‐of‐concept demonstrations and future pathways to propel this versatile platform material across sunlight‐harvesting technologies such as photocatalysis, photoelectrocatalysis, photothermal energy conversion, and solar cells are discussed. Nanoporous anodic alumina is an optical platform material with promising potential for sunlight‐harvesting technologies and applications. Herein, recent advances in dynamic field are summarized and future pathways to propel this versatile material across solar technologies such as photocatalysis, photoelectrocatalysis, photothermal energy conversion, and solar cells are outlined
This paper investigates drivers of the effectiveness of risk assessments in risk workshops dominated by ‘quantitative skepticism’. Moreover, it contrasts our findings with those of previous research that assumed the dominance of ‘quantitative enthusiasm’. Quantitative skepticism is a calculative culture characterized by an attitude that regards risk assessments as learning tools supporting the holistic formation of judgments incorporating difficult-to-quantify information. It contrasts with quantitative enthusiasm, which is a calculative culture that considers risk assessments as fully descriptive of reality. Prior research primarily focused on understanding the effectiveness of risk assessments under a calculative culture of quantitative enthusiasm. To understand what drives the correctness of risk assessment and the time needed to assess risks in workshops under a calculative culture of quantitative skepticism, we use an agent-based model that simulates risk assessment with risk workshops and that models agents’ cognitive processes using ECHO, a constraint satisfaction network (CSN). Our simulations show that, compared to risk workshops under conditions of quantitative enthusiasm, there are often lengthy periods of stagnation in individual and collective risk assessments and a strong path dependency on discussions. Prioritizing concerned participants improves the correct assessment of high risks at the expense of the correct assessment of low risks. Notwithstanding similarities in the drivers of the effectiveness of risk assessment across different calculative cultures, our results show that the predominant calculative culture matters when—to enhance their effectiveness—designing and implementing risk workshops.
As renewable lignin building blocks, hydroxystyrenes are particularly appealing as either a replacement or addition to styrene-based polymer chemistry. These monomers are obtained by decarboxylation of phenolic acids and often subjected to chemical modifications of their phenolic hydroxy groups to improve polymerization behaviour. Despite efforts, a simple, scalable, and purely (chemo)catalytic synthesis of acetylated hydroxystyrenes remains elusive. We thus propose a custom-made chemoenzymatic route that utilizes a phenolic acid decarboxylase (PAD). Our process development strategy encompasses a computational solvent assessment informing about solubilities and viable reactor operation modes, experimental solvent screening, cascade engineering, heterogenization of biocatalyst, tailoring of acetylation conditions, and reaction upscale in a rotating bed reactor. By this means, we established a clean one-pot two-step process that uses the renewable solvent CPME, bio-based phenolic acid educts and reusable immobilised PAD. The overall chemoenzymatic reaction cascade was demonstrated on a 1 L scale to yield 18.3 g 4-acetoxy-3-methoxystyrene in 96% isolated yield.
Tumor perfusion and vascular properties are important determinants of cancer response to therapy and thus various approaches for imaging perfusion are being explored. In particular, Intravoxel Incoherent Motion (IVIM) MRI has been actively researched as an alternative to Dynamic-Contrast-Enhanced (DCE) CT and DCE-MRI as it offers non-ionizing, non-contrast-based perfusion imaging. However, for repetitive treatment assessment in a short time period, high cost, limited access, and inability to scan at the bedside remain disadvantages of IVIM MRI. We propose an analysis framework that may enable 3D DCE Ultrasound (DCE-US) - low cost, bedside imaging with excellent safety record - as an alternative modality to IVIM MRI for the generation of DCE-US based pseudo-diffusivity maps in acoustically accessible anatomy and tumors. Modelling intravascular contrast propagation as a convective-diffusive process, we reconstruct parametric maps of pseudo-diffusivity by solving a large-scale fully coupled inverse problem without any assumptions regarding local constancy of the reconstructed parameters. In a mouse tumor model, we demonstrate that the 3D DCE-US pseudo-diffusivity is repeatable, sensitive to treatment with an antiangiogenic agent, and moderately correlated to histological measures of perfusion and angiogenesis.
Structural equation modeling (SEM) has remained two mutually exclusive domains, factor-based vs. component-based, depending on whether a construct is modeled by either a factor or a compo-nent (i.e., weighted composite of indicators). Research in international management (IM) and inter-national business (IB), however, needs to accommodate a more general model that considers a wide range of constructs from different disciplines at the same time, representing some constructs as factors (e.g., cultural distance and institutional distance) and others as components (e.g., interna-tional experience and export intensity). Integrated generalized structured component analysis (IG-SCA) is a recently developed statistical method for estimating such models with both factors and components. IGSCA can provide overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean squared residual (SRMR). However, the per-formance of these indexes in IGSCA is not yet investigated. Addressing this limitation, we (a) highlight the limitations of the dominantly used SEM approaches, (b) review the use of different SEM approaches in IM/IB research in the last decade, (c) conduct a simulation study, confirming that both GFI and SRMR distinguish well between correct and misspecified models with both fac-tors and components, and (d) we illustrate the indexes’ efficacy using a model concerning the role of personality traits and international experience in shaping cultural intelligence. Based on the re-view and the results of the simulation study and the illustrative example, we also propose rules-of-thumb cutoff criteria for each index in IGSCA.
As people living in remote areas still lack access to indoor lighting, the intervention of appropriate need-based innovations to improve basic indoor lighting facilities are essential. The present work discusses the development of a hybrid lighting device, Micro Solar Dome, that utilizes both the active and passive forms of solar energy to ensure sustainable lighting facilities for rural houses. The work elaborates the design, simulation, and pilot-scale installation of the device in 8 states in India. Based on the performance, a Principal Component Analysis was done, which indicated that more than 93% variation of the performance of the device was due to Global Horizontal Irradiance and Relative Humidity. A novel predictive model utilizing the Random Forest algorithm was developed for major Köppen climatic zones in India to determine the night-time performance of the device and validate the Principal Component Analysis results. As per the prediction, the average operation time of the device is about 3.5hrs at night, with the highest of 4.6 hrs in the Hot Desert Climate and the lowest of 2.4 hrs in the Humid Subtropical climate when 9 Köppen Zones are considered covering more than 95% of the geographic area of the country.
Green hydrogen plays a major role in the net-zero greenhouse gas-reduction strategy of the European Union. To supply hydrogen as cheap as possible, a well-balanced production system is needed to handle fluctuations of solar radiation and wind energy. Thus, this paper investigates the onsite hydrogen supply costs in the European catchment area in 2020, 2030, 2040 and 2050. Furthermore, a subsequent transport per pipeline to one of the projected demand centres in Europe (exemplary Germany) is considered. Also, the sensitivity regarding the additional use of salt caverns as hydrogen storage and less restricting supply profiles is assessed as well as the technical annual supply potential for 2030 and 2050. To do so, the optimal system design for minimized hydrogen supply cost for water electrolysis based on photovoltaic and wind turbines is estimated for a 0.5° x 0.5° grid using a linear optimization model. For the best locations, coastal regions at the North Sea, Western Sahara and parts of Algeria, onsite hydrogen supply cost decreases from 3 €2020/kgH2 in 2030 to 2 €2020/kgH2 in 2050. The technical hydrogen supply potential is tremendous, especially from Northern Africa, and a supply to Central Europe (Germany) via pipeline for around 3 €2020/kgH2 is possible in 2050, while a domestic hydrogen production in Germany covering the projected demand would lead to cost up to 4.5 €2020/kgH2. Furthermore, a large scale hydrogen storage e.g. in salt caverns, can reduce the hydrogen supply costs for regions with high seasonality of solar and wind up to 50% and excess electricity to less than 10%, leading to fewer cost deviations between the sub-regions, resulting in lower import costs from Northern and Western Europe than from Northern Africa or Middle East.
Recent developments in smart sensing technologies have fostered the wide-spread utilization of smart city applications, which rely on Internet of Things (IoT) frameworks to work efficiently. The terms “smart city” and “IoT framework”, however, have been given several definitions, without consensus. Consequently, definitions of the terms “smart city” and “IoT framework” need to be condensed, consolidating concepts and guidelines of smart cities and IoT frameworks, as will be shown in this study. In addition, a systematic survey of IoT frameworks for smart city applications is presented, summarizing and comparing the technologies and architectures of IoT frameworks for smart city applications. As a result of this study, trends in IoT frameworks for smart city applications and a definition of the term “smart city” are provided. Materializing the findings achieved in this study, an abstract IoT framework concept for smart city applications is proposed. It is expected that the definition of the term “smart city” may be used as a basis for a generally accepted formal definition and that the proposed IoT framework concept may provide a strong foundation for successful IoT framework implementations in the context of smart city applications.
Membrane ozonation of bromide-containing, high-color natural organic matter (NOM) containing groundwater was performed using single-tube polydimethylsiloxane (PDMS) and multi-tube polytetrafluoroethylene (PTFE) membrane contactors, and compared to batch ozonation. For membrane ozonation, dissolved ozone concentration, water color (VIS436), ultraviolet light absorption (UV254) and bromate formation were correlated with ozone dose, ozone gas concentration, hydraulic retention time and Hatta number (Ha). NOM color removal of up to 45 % for the single-tube contactor and 17 % for the multi-tube contactor were achieved while containing bromate formation below 10 µg L-1. Higher color removal using higher ozone doses was associated with high bromate formation i.e. >>10 µg L-1. In membrane ozonation, low ozone gas concentrations, long hydraulic retention times and high Ha resulted in low dissolved ozone concentrations due to quenching of ozone by NOM. At specific ozone doses of < 0.5 mg O3/mg DOC and Ha > 1, single-tube ozonation resulted in comparable results to batch ozonation while bromate formation was higher in the single-tube contactor at specific ozone doses > 0.5 mg O3/mg DOC and Ha < 1. At comparable ozone doses and Ha, bromate formation in the multi-tube contactor was always higher compared to single-tube and batch ozonation. This could be associated with the uneven ozone distribution within the multi-tube contactor. Results show that ozone dose is the major driver for selectivity between bromate formation and NOM color removal in both membrane and batch ozonation. Bromate formation in membrane ozonation may be controlled by adjusting gas concentration, Ha and hydraulic retention time. Membrane module design and process parameters of membrane ozonation reactors significantly affect treatment performance and should be optimized for selective target compound removal over by-product formation.
Target proteins in biotechnological applications are highly diverse. Therefore, versatile flexible expression systems for their functional overproduction are required. In order to find the right heterologous gene expression strategy, suitable host-vector systems, which combine different genetic circuits, are useful. In this study, we designed a novel Bacillus subtilis expression toolbox, which allows the overproduction and secretion of potentially toxic enzymes. This toolbox comprises a set of 60 expression vectors, which combine two promoter variants, four strong secretion signals, a translation-enhancing downstream box, and three plasmid backbones. This B. subtilis toolbox is based on a tailor-made, clean deletion mutant strain, which is protease and sporulation deficient and exhibits reduced autolysis and secondary metabolism. The appropriateness of this alternative expression platform was tested for the overproduction of two difficult-to-produce eukaryotic model proteins. These included the sulfhydryl oxidase Sox from Saccharomyces cerevisiae , which forms reactive hydrogen peroxide and undesired cross-linking of functional proteins, and the human interleukin-1β, a pro-inflammatory cytokine. For the best performing Sox and interleukin, overproducing and secreting variants of these new B. subtilis toolbox fermentation strategies were developed and tested. This study demonstrates the suitability of the prokaryotic B. subtilis host-vector system for the extracellular production of two eukaryotic proteins with biotechnological relevance. Key points • Construction of a versatile Bacillus subtilis gene expression toolbox. • Verification of the toolbox by the secretory overproduction of two difficult-to-express proteins. • Fermentation strategy for an acetoin-controlled overproduction of heterologous proteins.
Fatigue behavior of welded joints is significantly influenced by numerous factors, for example, local weld geometry. A representative quantity for the influence of the notch effect created by the local weld geometry is the stress concentration factor (SCF). Thus, SCFs are often used to estimate fatigue failure locations and fatigue strength; however, this simplifies the mutual effect of other influencing factors. Consequently, fatigue strength estimates for welded joints may deviate from experimental results. Machine learning techniques offer an alternative to traditional fatigue assessment approaches based on SCFs. This study presents a comparison of failure location predictions and number of cycles to failure for 621 fatigue tests of small‐scale butt‐welded joints. In addition, an understanding of importance and mutual influence of the factors is desired. We used gradient boosted trees in combination with the SHapley Additive exPlanation framework to identify influential features and their interactions.
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