Modeling Efforts Research Articles

Is “real” effort more real?

AbstractA growing number of studies use “real” effort designs for laboratory experiments where subjects complete an actual task to exert effort rather than using a stylized effort design where subjects simply choose an effort level from a predefined set. The commonly argued reason for real effort is that it makes the results more generalizable and field relevant. We investigate the nature of modeling effort provision by first trying to provide a clear theoretical understanding of the nature of effort costs. We then empirically examine claims about the differences between real effort and stylized effort. A key to our examination is ensuring that we compare the two modes of effort provision keeping effort costs constant, which is a point overlooked in many past examinations. In our data, when controlling for effort costs, we find no differences in behavior between real and stylized effort. Given the importance of effort costs and the lack of a generally accepted way to include them in real effort designs, we provide a simple add-on that any researcher can use with their real effort experiments to incorporate a theoretically appropriate and controlled cost of effort even in a real effort setting. We also discuss ways to better approach modeling effort costs in experiments, whether one is conducting real or stylized designs, to improve inference on research questions.

Characterizing Temperature-Dependent Acoustic and Thermal Tissue Properties for High-Intensity Focused Ultrasound Computational Modeling

High-intensity focused ultrasound (HIFU) thermal therapies utilize concentrated sound waves to ablate diseased tissue at precise locations within the body. Computational simulations of HIFU can assist clinicians by predicting the death of target tissues, identifying sensitive healthy tissues that risk thermal damage, and optimizing acoustic power delivery to minimize treatment times and maximize treatment efficacy. Accurate simulations require accurate inputs, and many computational solvers neglect property changes induced by tissue heating during treatment. Additionally, temperature-dependent tissue property data in the literature are relatively scarce. This study presents methodology for characterizing temperature-dependent acoustic and thermal properties in ex vivo porcine muscle tissue. From 20 – 50 °C, speed of sound is found to increase from approximately 1580 – 1620 m/s. The acoustic attenuation coefficient increases for 20 – 50 °C from 0.09 – 0.24 Np/cm at 0.5 MHz and 0.16 – 0.37 Np/cm at 1.6 MHz. Thermal conductivity and thermal diffusivity increase from 0.52 – 0.55 W/m °C and 0.147 – 0.158 mm2/s, respectively, over 20 – 60 °C. Specific heat capacity increases from approximately 3500 – 3800 J/kg °C, over 20 – 80 °C. Each property is consistent with data found in the literature, extends the literature to a larger temperature range, and, for acoustic properties, extends to unique frequencies. Temperature-dependent predictive models are also developed for each of the five properties. This study’s property measurement methodologies can be used to characterize other biological tissues, and the predictive models developed herein will facilitate future efforts in temperature-dependent modeling and uncertainty quantification of HIFU thermal therapies.

Comparative Study of n-C5 Bond Dissociation Energies and H-Atom Abstraction via H for Alkane, Alcohol, Methyl Ether, Nitroso, Nitro, Nitrite, and Nitrate.

Energetic materials have a wide range of applications, for many of which new materials are constantly developed. For understanding how energetic materials are chemically converted, it is essential to systematically understand how relevant functional groups affect the stability and properties of such materials. Here, the impact of nitroso, nitro, nitrite, and nitrate functional groups on a pentyl moiety is studied theoretically, focusing on bond dissociation energies and H atom abstraction via Ḣ. The nitroso group is found to imply the strongest effects, while the nitro group appears to exert a stabilizing effect on the pentyl moiety. Importantly, the nitrogenated functional groups generally stabilize the pentyl moiety, in contrast to alcohol and methyl ether functional groups. Therefore, it is concluded that using analogies to the chemistries of the latter two functional groups for chemical kinetic modeling of nitrogenated functional groups is not adequate. Based on the presented bond dissociation energies and rate coefficients, it is to be expected that unimolecular bond fissions at the nitrogenated functional groups will dominate over a wide temperature range. Building on the provided data, future detailed chemical kinetic modeling efforts for nitroso, nitro, nitrite, and nitrate compounds can be aided or initiated.

Spatial Distribution in Surface Aerosol Light Absorption Across India

AbstractLight‐absorbing carbonaceous aerosols that dominate atmospheric aerosol warming over India remain poorly characterized. Here, we delve into UV‐visible‐IR spectral aerosol absorption properties at nine PAN‐India COALESCE network sites (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1). Absorption properties were estimated from aerosol‐laden polytetrafluoroethylene filters using a well‐constrained technique incorporating filter‐to‐particle correction factors. The measurements revealed spatiotemporal heterogeneity in spectral intrinsic and extrinsic absorption properties. Absorption analysis at near‐UV wavelengths from carbonaceous aerosols at these regional sites revealed large near‐ultraviolet brown carbon absorption contributions from 21% to 68%—emphasizing the need to include these particles in climate models. Further, satellite‐retrieved column‐integrated absorption was dominated by surface absorption, which opens possibilities of using satellite measurements to model surface‐layer optical properties (limited to specific sites) at a higher spatial resolution. Both the satellite‐modeled and direct in‐situ absorption measurements can aid in validating and constraining climate modeling efforts that suffer from absorption underestimations and high uncertainties in radiative forcing estimates.

Assessing the Impact of Data-resolution On Ocean Frontal Characteristics

AbstractEasy access to and advances in satellite remote sensing data has enabled enhanced analysis of ocean fronts, physical and ecologically important areas where water masses converge. Recent development of higher-resolution satellite imagery to detect ocean fronts provides the potential to better capture patterns and trends of ocean change and improve modelling and prediction efforts. This study examines the relationship between satellite data spatial resolution and its influence on the quantification of frontal characteristics, frontal quantity, length, strength and density. We also examine the relationship between Finite-Size Lyapunov Exponents and image resolution. We found higher spatial resolution leads to increased frontal quantity and decreased frontal length. Also, both strength and spatial density of fronts differ at various resolutions. The Finite-Size Lyapunov Exponent value does not change significantly with resolution. Knowledge of the impact of resolution on the quantification of frontal characteristics is crucial as it enables the exploration of novel experimental design to further facilitate the development of improved parameterization and uncertainties in ocean modelling/studies.

Modeling and design of a separate effects irradiation test targeting fission gas release from Cr-doped UO2

Fission gas release (FGR) from nuclear fuel during operation can diminish heat transfer properties across the pellet-cladding gap and increase the fuel rod internal pressure, thereby posing a concern to fuel reliability and safety during an accident. Enlarging the fuel grain size, which has been shown to improve fission gas retention, can be achieved by doping the fuel feedstock prior to sintering. In this work, the BISON fuel performance code was used to predict FGR from undoped and chromia-doped UO2 (referred to as Cr-doped UO2) fuel specimens with different grain sizes and across various temperatures. The BISON models identified the irradiation conditions for which FGR is most significant, and a separate effects irradiation experiment in the High Flux Isotope Reactor (HFIR) was then developed targeting those conditions. The experiment leveraged the MiniFuel irradiation capability at Oak Ridge National Laboratory and consisted of 12 fuel specimens of varying grain size and Cr content. A coupling scheme between BISON FGR results and the ANSYS finite element thermal model used for experiment design was formulated to predict cumulative FGR from each fuel specimen based on expected irradiation temperature histories. The fuel samples were fabricated and characterized as a part of this work, and the fuel compositions modeled in BISON were representative of the specimens used in the experiment. This combined modeling and experimental effort aims to study the effect of fuel grain size and Cr content on FGR and to provide simulated BISON FGR results that can be used for future model validation activities.

SOLPS-ITER modeling of divertor detachment in MAST-U’s super-X double null configuration and comparison to experiment

The MAST-U tokamak has recently undergone upgrades to investigate any detachment advantages of the “Super-X Double-Null” magnetic configuration, a concept featuring up-down symmetry with two magnetic nulls and elongated divertor legs with strong wall baffling. This study presents SOLPS-ITER simulations of MAST-U Super-X Double Null discharges, in H-mode conditions, compared to initial 2D imaging measurements of divertor detachment characteristics. In the simulations, the Super-X divertor targets achieve detachment at a substantially lower upstream separatrix density compared to a shorter leg, conventional configuration (CD). As the density increases further in the Super-X configuration, the cold detached front moves upstream towards the X-point. Its speed in the poloidal plane, calculated as the change in front location divided by the corresponding increase in upstream plasma density, decreases by a factor of ∼ 3–4 as the front transitions from the baffled divertor chamber to the main plasma chamber, reaching a speed comparable to that observed in the CD configuration. In a chosen experimental Super-X density ramp discharge, the ionization front, identified using D2 Fulcher emission as a proxy, is already displaced from the target at the start of the divertor fueling ramp, whereas modeling of the same initial phase shows the ionization front still close to the target. As divertor fueling increases, both experiment and modeling show the ionization front moving further upstream towards the divertor throat. However, the associated variation in upstream density is ∼ 2 times larger in the modeling compared to the experiment, resulting in a broader detachment window. Comprehensive modeling efforts are underway to address these discrepancies, with several inaccurate modeling assumptions identified as potential causes of the deviation.

Mapping the Sun's coronal magnetic field using the Zeeman effect.

Regular remote sensing of the magnetic field embedded within the million-degree solar corona is severely lacking. This reality impedes fundamental investigations of the nature of coronal heating, the generation of solar and stellar winds, and the impulsive release of energy into the solar system via flares and other eruptive phenomena. Resulting from advancements in large aperture solar coronagraphy, we report unprecedented maps of polarized spectra emitted at 1074 nm by Fe+12 atoms in the active corona. We detect clear signatures of the Zeeman effect that are produced by the coronal magnetic field along the optically thin path length of its formation. Our comparisons with global magnetohydrodynamic models highlight the valuable constraints that these measurements provide for coronal modeling efforts, which are anticipated to yield subsequent benefits for space weather research and forecasting.

An Integrated Experimental and Modeling Approach for Assessing High-Temperature Decomposition Kinetics of Explosives.

We present a new integrated experimental and modeling effort that assesses the intrinsic sensitivity of energetic materials based on their reaction rates. The High Explosive Initiation Time (HEIT) experiment has been developed to provide a rapid assessment of the high-temperature reaction kinetics for the chemical decomposition of explosive materials. This effort is supported theoretically by quantum molecular dynamics (QMD) simulations that depict how different explosives can have vastly different adiabatic induction times at the same temperature. In this work, the ranking of explosive initiation properties between the HEIT experiment and QMD simulations is identical for six different energetic materials, even though they contain a variety of functional groups. We have also determined that the Arrhenius kinetics obtained by QMD simulations for homogeneous explosions connect remarkably well with those obtained from much longer duration one-dimensional time-to-explosion (ODTX) measurements. Kinetic Monte Carlo simulations have been developed to model the coupled heat transport and chemistry of the HEIT experiment to explicitly connect the experimental results with the Arrhenius rates for homogeneous explosions. These results confirm that ignition in the HEIT experiment is heterogeneous, where reactions start at the needle wall and propagate inward at a rate controlled by the thermal diffusivity and energy release. Overall, this work provides the first cohesive experimental and first-principles modeling effort to assess reaction kinetics of explosive chemical decomposition in the subshock regime and will be useful in predictive models needed for safety assessments.

The creative agency of large language models: a philosophical inquiry

AbstractThis paper explores the difficult question of whether Large Language Models (LLMs) are intrinsically creative. Because they can independently create original content, LLMs are often seen as creative agents. Contrary to the belief that LLMs are creative, this paper argues that LLMs are not creative for two reasons. First, LLMs are not creative because they lack an essential component of creativity, which is the first-person experience of the world. Secondly, LLMs are not creative because they are not the principal authors of their creative output, for they lack the subjective awareness and intentionality necessary to be regarded as authors, and their output is a collaborative effort of the AI model, data providers, and other stakeholders. Since they are not full-fledged authors in a traditional sense, they are not creative.

Tailoring the surface energy balance algorithm for land-improved (SEBALI) model using high-resolution land/use land cover for monitoring actual evapotranspiration

Access to accurate near real-time actual evapotranspiration (ETa) data is crucial for monitoring and planning water uses, contributing to the sustainable management and development of water resources. However, ongoing ETa modeling efforts need to be improved when applied to small-sized farmlands in rugged landscapes like in the upper Blue Nile Basin. This study aimed to customize the Google Earth Engine-based Surface Energy Balance for the Algorithm Land–Improved (SEBALIGEE) model using high-resolution land use and land cover data. Additionally, the model, originally based on JavaScript, was updated to Python (SEBALIGEEpy), thereby enhancing its accessibility to a broader range of users and modelers. The ETa simulated with an updated version of the model was validated over croplands using publicly available AmeriFlux eddy covariance data. Furthermore, the SEBALIGEEpy-based ETa was evaluated over the Koga smallholder irrigation scheme in the Upper Blue Nile Basin, Ethiopia, by comparing it with the ETa obtained from the remote sensing-based Water Productivity Open-Access Portal (WaPOR) over seven seasons (2015/16–2022/23). The validation results show that the updated model provides more accurate ETa estimates with fewer missing records, with 9–11 % using the updated model compared to 14–29 % using the original SEBALIGEE model. The customized model has the potential to be used for agricultural water management in areas with fragmented land parcels like the Upper Blue Nile basin and beyond.

Understanding the Variety of Domain Models: Views, Programs, Animations, and Other Models

Humanity has long since used models, in different shapes and forms, to understand, redesign, communicate about, and shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This has resulted in a wide range of modeling practices. When the models as used in such modeling practices have a key role to play in the activities in which these practices are ‘embedded’, the need emerges to consider the effectiveness and efficiency of such processes, and speak about modeling capabilities. In the latter situation, it also becomes relevant to develop a thorough understanding of the artifacts involved in modeling practices/capabilities. One context in which models play (an increasingly) important role is model-driven systems development, including software engineering, information systems engineering, business process engineering, enterprise engineering, and enterprise architecture management. In such a context, we come across a rich variety of modeling related artifacts, such as views, diagrams, programs, animations, specifications, etc. In this paper, which is actually part of an ongoing ‘journey’ in which we aim to gain deeper insights into the foundations of modeling, we take a fundamental look at the variety of modeling related artifacts as used in the context of model-driven (systems) development, while also presenting an associated framework for understanding, synthesizing the insights we obtained during the ‘journey’ so-far. In doing so, we will also argue that the aforementioned artifacts are actually specific kinds of models, albeit for fundamentally different purposes. The provided framework for understanding involves definitions of domain model, the Return on Modeling Effort (RoME), the conceptual fidelity of domain models, as well as views as a mechanism to manage the complexity of domain models.

Testing protocols for smoothing datasets of hydraulic variables acquired during unsteady flows

ABSTRACT Flood wave propagation involves complex flow variable dependencies. Continuous in situ hydrograph peak magnitude and timing data provide the most relevant information for understanding these dependencies. New acoustic instruments can produce experimental evidence by extracting usable signals from noisy datasets. This study presents a new screening protocol to smoothen streamflow data from unwanted influences and noise generated by flow perturbations and observational fluctuations. The protocol combines quantitative (statistical fitness parameters) and qualitative (domain expert judgments) evaluations. It is tested with 18 smoothing methods to identify optimal data conditioning candidates. Sensitivity analyses assess the validity, generality, and scalability of the procedures. The goal of this analysis is to set a mathematical foundation for empirical results that can lead to unified, general conclusions on principles or protocols for unsteady flows propagating in open channels, formulating practical guidance for future data acquisition and processing, and using in situ data to better support data-driven modeling efforts.

Doping position estimation for FeRh-based alloys

FeRh-based alloys have attracted significant attention due to their magnetic phase transition and significant magnetocaloric effects. These properties position them as promising candidates for fundamental research and practical applications, including magnetic cooling and targeted drug delivery. The study of FeRh alloys, particularly those where Rhodium or Iron atoms are substituted with other transition metals, is crucial as certain substitutions preserve the alloy’s magnetocaloric properties. However, even within a specific structural type and without considering competing phases, determining which atom (Fe or Rh) is replaced upon introducing a third element remains unclear. This paper addresses this ambiguity through ab initio calculations. We propose an approach to predict whether a dopant will replace Fe or Rh, offering insights into the electronic and structural factors influencing the substitution. Additionally, we present a dataset of ab initio calculations on doped FeRh alloys, which will support future data-driven modeling efforts. Our findings not only advance the understanding of FeRh-based alloys but also contribute to the design of novel materials for experimental and industrial applications.

Experimental and modeling insights into mixing-limited reactive transport in heterogeneous porous media: Role of stagnant zones

The understanding of mixing-controlled reactive dynamics in heterogeneous porous media remains limited, presenting significant challenges for modeling subsurface contaminant transport processes and for designing cost-effective environmental remedial efforts. The complexity of accurately observing, measuring, and modeling mixing-limited reactive transport has led to inadequate exploration of these critical processes. This study investigates the mixing and reaction kinetics affected by stagnant zones, which are commonly found in alluvial aquifers-aquitards and fracture-matrix systems. By conducting experiments involving conservative and bimolecular reactive transport through porous media within translucent chambers filled with two sizes of glass beads and under varying flow rates, we explored the effects of grain size and hydrodynamic conditions. Using a high-resolution camera, we monitored the concentration changes of conservative and reactive tracers, with subsequent interpretation through three-dimensional numerical simulations. The outcomes revealed the emergence of distinct mixing interfaces within both mobile and stagnant zones, culminating in a bi-peaked plume formation. Notably, the mixing and reaction times in media containing stagnant zones were found to be approximately 10 times longer than in homogeneous media. These findings, through experimental and modeling efforts, advance our understanding of mixing-limited reactive transport phenomena within heterogeneous media, underscoring the significant role of stagnant zones—a topic previously underexplored.

A morpho-viscoelasticity theory for growth in proliferating aggregates.

Despite significant research efforts in the continuum modeling of biological growth, certain aspects have been overlooked. For instance, numerous investigations have examined the influence of morphogenetic cell behaviors, like division and intercalation, on the mechanical response of passive (non-growing) tissues. Yet, their impact on active growth dynamics remains inadequately explored. A key reason for this inadequacy stems from challenges in the continuum treatment of cell-level processes. While some coarse-grained models have been proposed to address these shortcomings, a focus on cell division and cell expansion has been missing, rendering them unusable when it comes to modeling growth. Moreover, existing studies are limited to two-dimensional tissues and are yet to be formally extended to three-dimensional multicellular systems. To address these limitations, we here present a generalized multiscale model for three-dimensional aggregates that accounts for complex morphogenetic movements that include division, expansion, and intercalation. The proposed continuum theory thus allows for a comprehensive exploration into the growth and dissipation mechanics of proliferating aggregates, such as spheroids and organoids.

Can internal range structure predict range shifts?

Poleward and uphill range shifts are a common-but variable-response to climate change. We lack understanding regarding this interspecific variation; for example, functional traits show weak or mixed ability to predict range shifts. Characteristics of species' ranges may enhance prediction of range shifts. However, the explanatory power of many range characteristics-especially within-range abundance patterns-remains untested. Here, we introduce a hypothesis framework for predicting range-limit population trends and range shifts from the internal structure of the geographic range, specifically range edge hardness, defined as abundance within range edges relative to the whole range. The inertia hypothesis predicts that high edge abundance facilitates expansions along the leading range edge but creates inertia (either moreindividuals must disperse or perish) at the trailing range edge such that the trailing edge recedes slowly. In contrast, the limitation hypothesis suggests that hard range edges are the signature of strong limits (e.g. biotic interactions) that force faster contraction of the trailing edge but block expansions at the leading edge of the range. Using a long-term avian monitoring dataset from northern Minnesota, USA, we estimated population trends for 35 trailing-edge species and 18 leading-edge species and modelled their population trends as a function of range edge hardness derived from eBird data. We found limited evidence of associations between range edge hardness and range-limit population trends. Trailing-edge species with harder range edges were slightly more likely to be declining, demonstrating weak support for the limitation hypothesis. In contrast, leading-edge species with harder range edges were slightly more likely to be increasing, demonstrating weak support for the inertia hypothesis. These opposing results for the leading and trailing range edges might suggest that different mechanisms underpin range expansions and contractions, respectively. As data and state-of-the-art modelling efforts continue to proliferate, we will be ever better equipped to map abundance patterns within species' ranges, offering opportunities to anticipate range shifts through the lens of the geographic range.

Influence of Low-Level Shear Orientation and Magnitude on the Evolution and Rotation of Idealized Squall Lines. Part I: Storm Morphology and Bulk Updraft/Mesovortex Attributes

Abstract Observational and modeling efforts have explored the formation and maintenance of mesovortices, which contribute to severe hazards in quasi-linear convective systems (QLCSs). There exists an important interplay between environmental shear and cold-pool-induced circulations which, when balanced, allow for upright QLCS updrafts with maximized lift along storm outflow boundaries. Numerical simulations have primarily tested the sensitivity of squall lines to zonally varying low-level (LL) shear profiles (i.e., purely line-normal, assuming a north–south-oriented system), but observed near-storm environments of mesovortex-producing QLCSs exhibit substantial LL hodograph curvature (i.e., line-parallel shear). Therefore, previous QLCS simulations may fail to capture the full impacts of LL shear variability on mesovortex characteristics. To this end, this study employs an ensemble of idealized QLCS simulations with systematic variations in the orientation and magnitude of the ambient LL shear vector, all while holding 0–3-km line-normal shear constant. This allows for a nuanced examination of how line-parallel shear modulates system structure, as well as mesovortex strength, size, and longevity. Results indicate that hodographs with LL curvature support squall lines with prominent bowing segments and wider, more intense rotating updrafts. Shear orientation also impacts mesovortex characteristics, with curved hodographs favoring cyclonic vortices that are stronger, wider, deeper, and longer-lived than those produced with straight-line wind profiles. These results provide a more complete physical understanding of how LL shear variability influences the generation of rotation in squall lines. Significance Statement Research related to linear storms has largely focused on vertical changes in winds (i.e., shear) oriented perpendicular to squall lines given its ability to balance storm cold pools and keep updrafts upright, thus promoting long-lived storms that presumably can go on to produce rotation. However, squall lines that produce a great deal of rotation often have a component of low-level shear oriented parallel to storms. This study gauges the sensitivity of simulated squall lines to changes in the direction and strength of shear close to the surface. We find that shear oriented parallel to linear storms creates stronger and larger updrafts that in turn support the development of intense and persistent rotation with characteristics supportive of tornadoes. These insights have impacts on both our physical understanding and prediction of the rotation and associated hazards of linear storms.

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.