Model Setup Research Articles

PERFORMANCE OF WATER COOLING FOR RADIATION HEAT FLUX FUEL STORAGE TANK

Full-surface fire on fuel storage tank emits high radiation heat transfer. As a fire protection strategy, the water curtain cooling system is activated to reduce the temperature on the adjacent tank surface. Therefore, the present work predicts and analyses the radiation heat flux and the maximum flame temperature of different types of fuels. Further, this analyses the effect of fuel total mass on radiation heat flux and maximum flame temperature and observes the effect of distance between two tanks on radiation heat flux distribution. The relationship between water cooling flow rate and outlet water temperature that absorbed radiation heat flux has been studied. The study has been conducted by using the Consequence modeling software trial version. The modeling setup of the tank is 17 m in height with 65 m inner diameter, and the meteorological data used are 5.4 m/s wind speed with north wind direction at atmospheric pressure in order to imitate the worst-case fire scenario. The results reveal that the gasoline fuel emitted the highest heat flux value of 11.03 kW/m2 and the raw gasoline sample emits the lowest heat flux value of 9.14 kW/m2. Furthermore, the total mass of the fuel shows no effect on the maximum flame temperature of 958.51°C. According to the findings, the critical tank distancing is 36 m and thus the appropriate tank distancing of 40 m is highly recommended by the standard. The result shows that the water cooling rate of 4.1 lpm/m2 is an excellent practice of water cooling to cool down the temperature of the fuel tank which is exposed to radiation heat flux.

Optimization of aerodynamic drag reduction for truck trailer model via machine learning

From advanced fairing designs and spoilers to novel airflow management systems, the quest for drag reduction (DR) in truck trailers is critical for cleaner and more sustainable transport. In this study, a particle swarm optimization (PSO) integrated machine learning approach is developed to optimize the DR performance of a bare truck trailer (BTT) with and without half airfoils and roof fairings. The study aims to reach the optimal truck trailer design with fewer experiments by searching the entire solution space with machine learning methods thanks to the developed approach. For this purpose, the experimental setup was initially designed and the experimental results were obtained. The experimental inputs are Airfoil thickness, Roof Fairing (RF) positions, and Reynolds number, while the experimental output is the drag coefficient. These inputs and outputs were arranged via data preprocessing and then modeled using machine learning methods. Modeling was performed with Artificial Neural Network (ANN) and multiple regression models. In addition to the ANN model setup with appropriate topology, linear and non-linear multiple regression models were established. Comparisons were performed using statistical metrics and it was determined that ANN was the superior model. Using the ANN’s prediction model, the drag coefficients were derived from the whole parameter values of both airfoil thickness and RF positions in the solution space for all Reynolds number and an optimization process was performed by PSO. As a result of the analysis, optimal parameter values were determined as 0.10 and 5.0 mm for the airfoil thickness and RF position. As a result of the experimental validation, it is seen that the BTT_NACA 0010_RF-0.5 model, designed with the optimum parameter values, demonstrated a DR performance of 34.7 %, which is the highest value compared to others. Thus, the heuristic process was experimentally validated and optimized by saving time and cost.

Dynamic Modeling of a Thermochemical System for Solar Fuel Production Based on an Open-Source Framework

This paper describes the dynamic process model for a solar fuels synthesis plant currently being built in Germany. The open-source, equation-based modelling language Modelica is used as the foundation. In this plant, biogas and steam are reformed, producing synthesis gas and subsequently turned into synthetic crude oil. This work contains the necessary model setups for the thermal energy storage, solar-absorbing gas receiver and reforming reactor to consider the thermohydraulic, thermochemical and radiative interactions occurring in the process. The remaining infrastructure is modeled with the Modelica Standard Library. A way to fit these models with experimental data for validation is also outlined.

Development and Validation of NOAA’s 20-year global wave ensemble reforecast

Abstract A new 20-year wave reforecast was generated based on the NOAA Global Ensemble Forecast System, version 12 (GEFSv12). It was produced using the same wave model setup as the NCEP’s operational GEFSv12 wave component, which employs the numerical wave model WAVEWATCH III and utilizes three grids with spatial resolutions of 0.2° and 0.25°. The reforecast comprises 5 members with one cycle per day and forecast range of 16 days. Once a week, it expands to 35 days and 11 members. This paper describes the development of the wave ensemble reforecast, focusing primarily on validation against buoys and altimeters. The statistical analyses demonstrated very good performance in the short range for significant wave height, with correlation coefficients of 0.95-0.96 on day 1, and between 0.86-0.88 within week 1, along with bias close to zero. After day 10, correlation coefficients fall below 0.70. We found that the degradation of predictability and the increase in scatter errors predominantly occur in the forecast lead time between days 4 and 10, in terms of the ensemble mean and individual members, including the control. For week 2 and beyond, a probabilistic spatio-temporal analysis of the ensemble space provides useful forecast guidance. Our results provide a framework for expanding the usefulness of wave ensemble data in operational forecasting applications.

Storm‐Driven Warm Inflow Toward Ice Shelf Cavities—An Idealized Study of the Southern Weddell Sea Continental Shelf System

AbstractSudden peaks in south‐westward wind strength (storms) have been observed to drive pulses of enhanced southward currents on the continental shelf east of the Filchner Trough. However, the link between wind and southward flow is not persistent, and it is uncertain which conditions favor the wind‐driven pulses that typically bring modified Warm Deep Water southward toward the front of the Filchner Ice Shelf. We run a set of experiments in an idealized numerical model setup and find that storms induce a net southward “warm” (Θ > −1.5°C) volume transport in the trough region throughout the year. This is mainly explained by enhanced barotropic circulation on the shelf. The greatest storm‐driven increase in southward heat transport occurs during summer and fall, with an exceptionally large increase in November and December due to storm‐enhanced circulation on the shelf and seasonally varying heat content availability south of the shelf break. Analysis of ERA5‐data shows that the number of storm days (wind speed >10 m s−1) per year in the region co‐vary with SAM. The positive trend in SAM can hence be expected to further enhance the importance of storm‐driven southward heat transport toward the Filchner Ice Shelf cavity, which may have consequences for the basal melting.

The value of simplified models of radionuclide transport for the safety assessment of nuclear waste repositories: A benchmark study

In order to assess sites for a deep geological repository for storing high-level nuclear waste safely in Germany, various numerical models and tools will be in use. For their interaction within one workflow, their reproducibility, and reliability version-controlled open-source solutions and careful documentation of model setups, results and verifications are of special value. However, spatially fully resolved models including all relevant physical and chemical processes are neither computationally feasible for large domains nor is the data typically available to parameterize such models. Thus, simplified models are crucial for the pre-assessment of possible sites to narrow down the list of suitable candidates for which detailed site investigations and fully resolved models will be done at a later stage. Still, the accuracy of these simplified models is of importance as the pre-assessment of suitable sites will be based on them. In this study, we compare the modelling capabilities of TransPyREnd, a one-dimensional transport code based on finite differences, specifically developed for the fast estimation of radionuclide transport by the German federal company for radioactive waste disposal (BGE), with OpenGeoSys, which is a modelling platform based on finite elements in up to three spatial dimensions. Both codes are used in the site selection procedure for the German nuclear waste repository. The comparison of the model results of TransPyREnd and OpenGeoSys is augmented by comparisons with an analytical solution for a homogeneous material. For the purpose of numerical benchmarking, we consider a geological profile located in southern Germany as an example where the hypothetical repository is located in a clay-stone formation. TransPyREnd and OpenGeoSys yield overall similar results. However, both codes use different discretizations which impact is the highest for strongly sorbing compounds, while the difference gets negligible for less sorbing and more diffusive compounds as higher diffusion tends to blur the initial conditions. Overall, the OpenGeoSys model is more exact whereas the TransPyREnd model has considerable faster run times. We found in our example, that significant substance amounts are only leaving the host rock formation, if apparent diffusion is high, for which case both codes give similar results, while relative differences are considerable for strongly sorbing compounds. However, in the latter case no significant substance amount of radionuclides leaves the host-rock formation, thus deeming the differences in the model results minor for the overall safety assessments of sites.

Implications of climate and litter quality for simulations of litterbag decomposition at high latitudes

Abstract. Litter decomposition is a vital part of the carbon cycle and is thoroughly studied both in the field and with models. Although temporally and spatially limited, litterbag decomposition experiments are often used to calibrate and evaluate soil models, coupled to land models, that are intended for use on large scales. We used the microbially explicit soil decomposition model MIMICS+ to replicate two high-latitude litterbag decomposition experiments of different spatial and temporal scales. We investigated how well the model represented observed mass loss in terms of the controlling factors of climate and litter quality and their relative importance with time. In addition to default model forcing, we used measured and site-specific model-derived microclimatic variables (soil moisture and temperature), hypothesizing that this would improve model results. We found that MIMICS+ represented mass loss after 1, 3, and 6 years well across a climatic gradient of Canadian sites but had more variable results for 1-year mass loss across a climate grid in southern Norway. In terms of litter quality, the litter metabolic fraction had more influence on modeled mass loss than the carbon-to-nitrogen ratio of the litter. Using alternative microclimate sources led to up to 23 % more mass remaining and down to 22 % less mass remaining compared to the simulations using default model inputs. None of the input alternatives significantly improved results compared to using the default model setup. We discuss possible causes for our findings and suggest measures to better utilize short-term field experiments to inform microbially explicit decomposition models.

Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables - Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme

High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.

A high-resolution coupled circulation-wave model for regional dynamic downscaling of water levels and wind waves in the western North Atlantic ocean

Reanalysis datasets provide temporally/spatially continuous data for sea level and wind wave climate studies but often lack the resolution to resolve the complex coastal physical processes. To address this issue, a high-resolution coupled circulation-wave model (ADCIRC + SWAN) is applied to dynamically downscale storm tides (tide + surge + wave setup) and waves in the western North Atlantic Ocean. The model is forced at its sea surface boundary with hourly surface pressure and wind fields, and its open boundary with water levels and direction-frequency wave spectrum from the ERA5 reanalysis dataset. The sensitivity of the model is evaluated for different combinations of internal tide energy conversion, quadratic friction coefficients, and wave physics packages. For the best model setup, the results show an RMSE range of 0.114 m–0.295 m for storm tide, 0.176 m–0.298 m for non-tidal residual, 0.143 m–0.39 m for significant wave height, and 0.42 s–1.04 s for mean wave period. Incorporating the direction-frequency wave spectrum as an open boundary condition improves the model's accuracy, reducing the RMSE for significant wave heights by up to 7% and for the mean wave period by up to 30% over a one-month simulation period.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

Spontaneous symmetry breaking of cooperation between species.

In mutualistic associations, two species cooperate by exchanging goods or services with members of another species for their mutual benefit. At the same time, competition for reproduction primarily continues with members of their own species. In intra-species interactions, the prisoner's dilemma is the leading mathematical metaphor to study the evolution of cooperation. Here we consider inter-species interactions in the spatial prisoner's dilemma, where members of each species reside on one lattice layer. Cooperators provide benefits to neighbouring members of the other species at a cost to themselves. Hence, interactions occur across layers but competition remains within layers. We show that rich and complex dynamics unfold when varying the cost-to-benefit ratio of cooperation, r. Four distinct dynamical domains emerge that are separated by critical phase transitions, each characterized by diverging fluctuations in the frequency of cooperation: (i) for large r cooperation is too costly and defection dominates; (ii) for lower r cooperators survive at equal frequencies in both species; (iii) lowering r further results in an intriguing, spontaneous symmetry breaking of cooperation between species with increasing asymmetry for decreasing r; (iv) finally, for small r, bursts of mutual defection appear that increase in size with decreasing r and eventually drive the populations into absorbing states. Typically, one species is cooperating and the other defecting and hence establish perfect asymmetry. Intriguingly and despite the symmetrical model set-up, natural selection can nevertheless favour the spontaneous emergence of asymmetric evolutionary outcomes where, on average, one species exploits the other in a dynamical equilibrium.

Converged ensemble simulations of climate: possible trends in total solar irradiance cannot explain global warming alone

We address the hypothetical question of whether an increasing total solar irradiance (TSI) trend, without anthropogenic contributions, could be sufficient to explain the ongoing global warming. To this end, the intermediate-complexity climate model PlaSim is used. To consider the total internal variability, we present a set of ensemble simulations, with different forcing histories in TSI and CO2 concentration, that have converged sufficiently tightly to the relevant probability distributions to provide a satisfactory bound on any spurious trend possibly arising from a sampling bias; similar bounds on any other unforced contributions to ensemble mean trends are also estimated. A key point is the consideration, among the forcing histories, the steepest increasing trend in TSI that is still consistent with observations according to a recent study; thereby, we essentially revisit corresponding TSI reconstructions, more than 20 years after their last modeling-based evaluation, by improving the analysis through taking care of all possible sources of error or uncertainty and incorporating data that have become available since then. Without any change in CO2 concentration, our TSI trend (i.e., and upper bound on actual TSI trends) is found to be insufficient to produce outcomes compatible with the observational record in global mean surface temperature (GMST) with a nonnegligible probability. We formalize our statement for quantifiers of GMST trends through evaluating their distributions over the ensemble, and we speculate that the hypothesis about the exclusive role of an increasing TSI remains implausible even beyond our particular model setup. At the same time, if we consider a constant TSI, and the observational record in CO2 concentration is applied as forcing, the simulation results and the recorded GMST match well. While we currently need to leave the question of a precise attribution open, we conclude by pointing out that an attribution of the ongoing global warming to an increasing TSI alone could be made plausible only if a bias in the set of land-based instrumental temperature measurements were increasing more rapidly than commonly estimated; an assessment of the latter possibility is out of the scope of our study, as well as addressing solar forcing mechanisms beyond the effect of TSI.

Investigating the sign of stratocumulus adjustments to aerosols in the ICON global storm-resolving model

Abstract. Aerosols can cause brightening of stratocumulus clouds, thereby cooling the climate. Observations and models disagree on the magnitude of this cooling, partly because of the aerosol-induced liquid water path (LWP) adjustment, with climate models predicting an increase in the LWP and satellites observing a weak decrease in response to increasing aerosols. With higher-resolution global climate models, which allow the simulation of mesoscale circulations in which stratocumulus clouds are embedded, there is hope to start bridging this gap. In this study, we present boreal summertime simulations conducted with the ICOsahedral Non-hydrostatic (ICON) global storm-resolving model (GSRM). Compared to geostationary satellite data, ICON produces realistic cloud coverage in the stratocumulus regions; however, these clouds look cumuliform, and the sign of LWP adjustments disagrees with observations. We investigate this disagreement with a causal approach, which combines time series with knowledge of cloud processes, allowing us to diagnose the sources of observation–model discrepancies. The positive ICON LWP adjustment results from a superposition of processes, with an overestimated positive response due to (1) precipitation suppression, (2) a lack of wet scavenging, and (3) cloud deepening under a weak inversion, despite (4) small negative influences from cloud-top entrainment enhancement. We also find that precipitation suppression and entrainment enhancement occur at different intensities during the day and the night, implying that daytime satellite studies suffer from selection bias. This causal methodology can guide modelers on how to modify model parameterizations and setups to reconcile conflicting studies concerning the sign and magnitude of LWP adjustments across different data sources.

Characterisation of the Decoring Behaviour for Furan Resin Bonded Sands Using DEM Simulation

Sand cores and moulds form the inner and outer structures of casting parts with tolerances of up to a few tenths of a millimetre. These must fulfil two complementary characteristics. During casting, they must withstand the high thermal and mechanical loads and subsequently disintegrate without leaving any residue. The production of these mostly organically bonded cores and moulds is done with conventional manufacturing processes, such as core shooting, or what is becoming increasingly more important for foundries, using 3D printing. In order to better understand this complex disintegration behaviour of these different core types and thus minimise the enormous energy input for their removal, a suitable simulation model based on discrete element methods (DEM) is considered as a tool to describe and further analyse the prevailing complex interactions in more detail. This contribution discusses the characterisation of furan resin bonded sand cores/parts, presenting various test apparatuses designed for this purpose, and outlines the foundational setup and definition of bonded-particle models (BPM) to be used as breakable structures in respective DEM simulations.

Multiphysics framework for cost-effective, long-term monitoring of carbon storage in a carbonate reservoir

We evaluate an effective and cost-efficient multiphysics approach for long-term monitoring of carbon storage in carbonate reservoirs. A key focus of our study is to advocate for a cost-effective, long-term monitoring strategy for carbon storage projects through the integration of surface and borehole multiphysics measurements. By leveraging technologies related to electromagnetic (EM) and gravity methods, we present a framework that addresses the monitoring of the dynamic behavior of injected CO2 over an extended period. The modeling setup integrates realistic static reservoir models with simulated dynamic saturation models to provide results as close as possible to real experimental conditions. Results demonstrate the high sensitivity of EM fields to changes in reservoir CO2 saturation, especially when surface-to-reservoir acquisition configurations are adopted. Additionally, gravity simulations, employing three-axis vector surface gravity measurements and borehole vector gravity, effectively capture density variations over time. Our proposed framework highlights the sequential use of seismic imaging for preinjection site characterization and the transition to EM and gravity surveys for continuous monitoring during and after the injection phases. The multiphysics simulation results performed on realistic carbon storage conditions contribute to the evolving landscape of effective geophysical CO2 monitoring technology for reliable long-term reservoir management.

Obtaining Continental‐Scale, High‐Resolution 2‐D Ionospheric Flows and Application to Meso‐Scale Flow Science

AbstractAn approach for creating continental‐scale, multi‐scale plasma convection maps in the nightside high‐latitude ionosphere using the spherical elementary current systems technique has been developed and evaluated. The capability to reconstruct meso‐scale flow channels improved dramatically, and the velocity errors were reduced by ∼30% compared to the spherical harmonic fitting method. Uncertainties of velocity vectors estimated by varying the model setup was also low. Convection maps for a substorm event revealed multiple flow channels in the polar cap, dominating the convection in the quiet time and early growth phase. The meso‐scale flows extended toward the nightside auroral oval and had continuous flow channels over >20° of latitude, and the flow channels dynamically merged and bifurcated. The substorm onset occurred along one of the flow channels, and the azimuthal extent of the enhanced flows coincided with the initial width of the auroral breakup. During the expansion phase, the meso‐scale flows repetitively crossed the oval poleward boundary, and some of them contributed to subauroral polarization streams enhancements. Increased flows extended duskward, along with the westward traveling surge. Then, flows near midnight weakened and evolved to the Harang flow shear. The meso‐scale flow channels had significant (∼10%–40% on average) contributions to the total plasma transport. The meso‐scale flows were highly variable on ∼10 min time scales and their individual maximum contributions reached upto 73%. These results demonstrate the capability of specifying realistic convection patterns, quantifying the contribution of meso‐scale transport, and evaluating the relationship between meso‐scale flows and localized auroral forms.

The investment grade and funds' portfolio allocation in Greek assets

The importance of investment funds for the global economy has increased in the aftermath of the global financial crisis. In the present paper, we focus on the case of Greece and the developments in investment funds’ portfolios, with a special emphasis on the period before the sovereign credit rating upgrade of Greece to investment grade. By means of a differences-in-differences estimator, we find that, in the aftermath of the change in Greece’s sovereign credit rating outlook to positive by the rating agency Standard and Poor’s (S&P), investment funds increased their holdings of Greek sovereign bonds in relation to other comparable euro area sovereign bonds. Next, in a dynamic panel data model setup we find that this increase in investment funds’ positions in Greek government bonds (GGBs) explains about 80% of the reduction in Greek sovereign bond spreads. Our results highlight the strong association between investment funds’ portfolio allocation and the underlying assets’ credit ratings, and provide incentives for continuing reforms that may lead to rating upgrades, as a means of increasing demand for Greek sovereign bonds and controlling the cost of debt. This is especially important when the monetary policy environment becomes tighter and interest rates, as well as the cost of funding, increase.

The Effect of Domain Length and Initialization Noise on Direct Numerical Simulation of Shear Stratified Turbulence

Direct numerical simulation (DNS) has been employed with success in a variety of oceanographic applications, particularly for investigating the internal dynamics of Kelvin–Helmholtz (KH) billows. However, it is difficult to relate these results directly with observations of ocean turbulence due to the significant scale differences involved (ocean shear layers are typically on the order of tens to hundreds of meters in thickness, compared to DNS studies, with layers on the order of one to tens of centimeters). As efforts continue to inform our understanding of geophysical-scale turbulence by extrapolating DNS results, it is important to understand the impact of model setup and initial conditions on the resulting turbulent quantities. Given that geophysical-scale measurements, whether through microstructures or other techniques, can only provide estimates of averaged TKE quantities (e.g., TKE dissipation or buoyancy flux), it may be necessary to compare mean turbulent quantities derived from DNS (i.e., across one or more complete billow evolutions) with ocean measurements. In this study, we analyze the effect of domain length and initial velocity noise on resulting turbulent quantities. Domain length is important, as dimensions that are not integer multiples of the natural KH billow wavelength may compress or stretch the billows and impact their energetics. The addition of random noise in the initial velocity field is often used to trigger turbulence and suppress secondary instabilities; however, the impact of noise on the resulting turbulent energetics is largely unknown. In this study, we conclude that domain lengths on the order of 1.5 times the natural wavelength or less can affect the resulting turbulent energetics by a factor of two or more. We also conclude that increasing the amplitude of random initial velocity noise decreases the resulting turbulent energetics, but that different realizations of the random noise field may have an even greater impact than amplitude. These results should be considered when designing a DNS experiment.

Power to the programmer: Modeller’s perspective on automating the setup of hydrodynamic models for Dutch water authorities

Use of models in decision making, for example in water management, requires confidence in the model and its outputs. Since choices in model setup affect model output, this confidence is affected by the modellers’ professional judgement. Computer programmers can use their expertise in coding to standardise some of the tasks associated with computational modelling. Therefore, centralized automation has the potential to ensure quality of modelling decisions. Since it is the modeller that makes the choices in the model set-up, it is important to understand how modellers perceive automation. To explore their perspectives, we conducted fourteen interviews with modellers at water authorities and consulting companies in the Netherlands. The transcripts were analysed through deductive and inductive content analysis. Our study reveals that automated modelling processes can improve efficiency, transparency and consistency, but only if certain requirements are met, such as good documentation, clear ownership, adequate maintenance, and frequent evaluation. Therefore, managing the risks and benefits of automation requires balancing the power between modellers and programmers.

Deep reinforcement learning for tuning active vibration control on a smart piezoelectric beam

Piezoelectric transducers are used within smart structures to create functions such as energy harvesting, wave propagation or vibration control to prevent human discomfort, material fatigue, and instability. The design of the structure becomes more complex with shape optimization and the integration of multiple transducers. Most active vibration control strategies require the tuning of multiple parameters. In addition, the optimization of control methods has to consider experimental uncertainties and the global effect of local actuation. This paper presents the use of a Deep Reinforcement Learning (DRL) algorithm to tune a pseudo lead-lag controller on an experimental smart cantilever beam. The algorithm is trained to maximize a reward function that represents the objective of vibration mitigation. An experimental model is estimated from measurements to accelerate the DRL’s interaction with the environment. The paper compares DRL tuning strategies with [Formula: see text] and [Formula: see text] norm minimization approaches. It demonstrates the efficiency of DRL tuning by comparing the control performance of the different tuning methods on the model and experimental setup.