Transport Model Research Articles

Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise.

Coastal change is a complex combination of multi-scale processes (e.g., wave-driven cross-shore and longshore transport; dune, bluff, and cliff erosion; overwash; fluvial and inlet sediment supply; and sea-level-driven recession). Historical sea-level-driven coastal recession on open ocean coasts is often outpaced by wave-driven change. However, future sea-level-driven coastal recession is expected to increase significantly in tandem with accelerating rates of global sea-level rise. Few models of coastal sediment transport can resolve the multitude of coastal-change processes at a given beach, and fewer still are computationally efficient enough to achieve large-scale, long-term simulations, while accounting for historical behavior and uncertainties in future climate. Here, we show that a scalable, data-assimilated shoreline-change model can achieve realistic simulations of long-term coastal change and uncertainty across large coastal regions. As part of the modeling case study of the U.S. South Atlantic Coast (Miami, Florida to Delaware Bay) presented here, we apply historical, satellite-derived observations of shoreline position combined with daily hindcasted and projected wave and sea-level conditions to estimate long-term coastal change by 2100. We find that 63 to 94% of the shorelines on the U.S. South Atlantic Coast are projected to retreat past the present-day extent of sandy beach under 1.0 to 2.0m of sea-level rise, respectively, without large-scale interventions.

Co-designing transport models as a heuristic planning tool.

Recently the transportation sector has witnessed several new technologically driven disruptions that have amplified the complexity of city planning and policymaking. Traditional well-established processes of decision-making in urban planning and transportation are proving insufficient to deal with this degree of complexity and uncertainty. This paper proposes an alternative approach, combining qualitative and normative urban design, with quantitative and predictive transport modelling. This requires urban designers and transport modellers to co-create goal-driven and agile transport models that act as a heuristic tool to guide planning decisions in early design stages. Heuristic modelling is informed by design optioning and vice versa in an iterative loop. A case study is presented to demonstrate how this approach is operationalized to study the impacts of automated vehicles on urban planning. Design workshops are used as a method to elicit responses from stakeholders, which are used to co-create the simulation models. This collaborative process grounds the research in real-world practice and enhances the communication of design proposals and research findings across disciplines. By integrating design thinking methods with agent-based transport simulations, this approach provides a better understanding of emergent effects in complex urban systems and improves stakeholder engagement in the planning process.This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Abstract. We introduce NitroNet, a deep learning model for the prediction of tropospheric NO2 profiles from satellite column measurements. NitroNet is a neural network trained on synthetic NO2 profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives NO2 vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces NO2 concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that NO2 profiles showing strong disagreement (>20 %) with colocated TROPOMI column measurements are discarded. We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the NO2 profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of +6.7 % and R=0.95 for TROPOMI NO2 VCDs, a bias of −10.5 % and R=0.75 for AirBase surface concentrations, and a bias of −34.3 % to +99.6 % with R=0.83–0.99 for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer NO2 lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of NO2 profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric NO2 profiles. Furthermore, in situ measurements of NO2 are known to suffer from biases, often larger than +20 %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.

Hyperon Production in Bi + Bi Collisions at the Nuclotron-Based Ion Collider Facility and Angular Dependence of Hyperon Spin Polarization

The strange baryon production in Bi + Bi collisions at sNN=9.0 GeV is studied using the PHSD transport model. Hyperon and anti-hyperon yields, transverse momentum spectra, and rapidity spectra are calculated, and their centrality dependence and the effect of rapidity and transverse momentum cuts are studied. The rapidity distributions for Λ¯, Ξ, Ξ¯ baryons are found to be systematically narrower than for Λs. The pT slope parameters for anti-hyperons vary more with centrality than those for hyperons. Restricting the accepted rapidity range to |y|<1 increases the slope parameters by 13–30 MeV, depending on the centrality class and the hyperon mass. Hydrodynamic velocity and vorticity fields are calculated, and the formation of two oppositely rotating vortex rings moving in opposite directions along the collision axis is found. The hyperon spin polarization induced by the medium vorticity within the thermodynamic approach is calculated, and the dependence of the polarization on the transverse momentum and rapidity cuts and on the centrality selection is analyzed. The cuts have stronger effect on the polarization of Λ and Ξ hyperons than on the corresponding anti-hyperons. The polarization signal is maximal for the centrality class, 60–70%. We show that, for the considered hyperon polarization mechanism, the structure of the vorticity field makes an imprint on the polarization signal as a function of the azimuthal angle in the transverse momentum plane, ϕH, cosϕH=px/pT. For particles with positive longitudinal momentum, pz>0, the polarization increases with cosϕH, while for particles with pz<0 it decreases.

Mass transport modelling of two partially miscible, multicomponent fluids in nanoporous media

High-pressure fluid transport in nanoporous media such as shale formations requires further understanding because conventional continuum approaches become inadequate due to their ultralow permeability and complexity of transport mechanisms. We propose a species-based approach for modelling two partially miscible, multicomponent fluids in nanoporous media – one that does not rely on conventional bulk fluid transport frameworks but on species movement. We develop a numerical model for species transport of partially miscible, non-ideal fluid mixtures using the chemical potential gradient as the driving force. The model considers the binary friction concept to include the friction between fluid molecules as well as between fluid molecules and pore walls, and incorporates the key multicomponent transport mechanisms – Knudsen, viscous and molecular diffusion. Under single-phase conditions, the system under consideration is quantified by introducing multicomponent Sherwood number (Sh), Péclet number (Pe) and fluid–solid friction modulus (φ). Despite the complexity of fluid transport in nanopores, the steady-state single-phase transport results reveal the contribution of diffusion in nanopores, where all parameters collapse on a set of master curves for the multicomponent Sh with a dependence on multicomponent Pe and φ. Unsteady state, two-phase transport modelling of the codiffusion process shows that light and intermediate alkanes are produced much higher than heavy alkanes when the vapour phase appears. We demonstrate that the pressure gradient is also crucial in promoting CO2 and alkane mixing during counterdiffusion processes. These results stress the need for a paradigm shift from classical bulk flow modelling to species-based transport modelling in nanoporous media.

Abstract 4129036: Air pollution and Cardiovascular Disease Incidence in a Pooled Analysis of 6 U.S. Cohorts

Introduction/Background: Long term air pollution is associated with cardiovascular disease (CVD) incidence, however the effects at low levels in the US are unknown. Research Questions/Hypothesis: To assess the effect of long-term exposure to ambient air pollution CVD incidence (myocardial infarction [MI], stroke, and all CVD) in a pooled analysis of epidemiological cohorts. Materials and Methods: We harmonized data from six cohorts (Cardiovascular Health Study, Nurses’ Health Study II, Multi-Ethnic Study of Atherosclerosis, and Reasons for Geographic And Racial Differences in Stroke Study, Sister Study, and Women’s Health Initiative) with both CVD incidence data and extensive covariate information. We predicted time-varying two-year average concentrations of particulate matter <2.5 microns (PM 2.5 ) and nitrogen dioxide (NO 2 ) from 1990-2019 at participant residential addresses using validated regionalized spatio-temporal models, combining spatio-temporal universal kriging and partial least squares regression with inputs from monitoring data from regulatory and researcher-deployed networks, satellite remote-sensing data and chemical transport modeling. Cox proportional hazards regression models adjusted for individual and area-level information, including smoking status, socio-economic factors, anthropometry, blood pressure, cholesterol, and other clinical variables. Results: Our analysis included over 300,000 participants and 10,229 incident CVD events. The median residential concentration of PM 2.5 was 10.1 µg/m 3 and NO 2 was 9.3 ppb. A 5 µg/m 3 increase in PM 2.5 was not significantly associated with a higher hazard ratio for all outcomes: 1.03 (95%CI: 0.96, 1.10) for CVD incidence, 1.03 (95%CI 0.93, 1.15) for MI, and 1.00 (95%CI 0.90, 1.10) for stroke. We found that a 10ppb increase in NO 2 was associated with a 1.06 (95%CI: 1.01, 1.11) higher hazard ratio for CVD, 1.07 (95%CI: 1.01, 1.11) for MI, and 1.02 (95%CI 0.96, 1.09) for stroke. Conclusions: In this analysis of a large, pooled cohort with excellent exposure, outcome, and covariate information, we found an association of NO 2 exposure—considered a surrogate for traffic-related air pollution—with CVD incidence and MI. Associations with particulate matter were not statistically significant, but could not exclude a small increased risk. We are extending this analysis to include 6 additional cohorts and an additional 700,000 participants to allow for generation of a highly resolved concentration-response function.

Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother

Abstract. Atmospheric inverse modeling is the process of estimating emissions from atmospheric observations by minimizing a cost function, which includes a term describing the difference between simulated and observed concentrations. The minimization of this difference is typically limited by uncertainties in the atmospheric transport model rather than by uncertainties in the observations. In this study, we showcase how a temporally varying, flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation through idealized experiments using an ensemble Kalman smoother system. We use the estimation of European CH4 emissions from the in situ measurement network as an example, but we also demonstrate the additional benefits for trace gases with more localized sources, such as SF6. The uncertainty in flow-dependent transport is determined using meteorological ensemble simulations that are perturbed by physics and driven at the boundaries by an analysis ensemble from a global meteorology and a CH4 simulation. The impact of direct representation of temporally varying transport uncertainties in atmospheric inversions is then investigated in an observation system simulation experiment framework in various setups and for different flux signals. We show that the uncertainty in the transport model varies significantly in space and time and that it is generally highest during nighttime. We apply inversions using only afternoon observations, as is common practice, but also explore the option of assimilating hourly data irrespective of the hour of day using a filter based on transport uncertainty and taking into account the temporal covariances. Our findings indicate that incorporating flow-dependent uncertainties in inversion techniques leads to more accurate estimates of GHG emissions. Differences between estimated and true emissions could be reduced more effectively by 9 % to 82 %, with generally larger improvements for the SF6 inversion problem and for the more challenging setup with small flux signals.

A One-Dimensional Reactive Transport Model for Microbially Induced Calcium Carbonate Precipitation (MICP) in Phreeqc

A One-Dimensional Reactive Transport Model for Microbially Induced Calcium Carbonate Precipitation (MICP) in Phreeqc

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

Numerical Simulations for Calibration Setup for Dynamic Contrast-Enhanced Ultrasonography Imaging Protocol.

This study presents an investigation of an innovative microfluidic flow separator using both numerical and experimental approaches to calibrate contrast-enhanced ultrasound scanners. Numerical simulations were conducted using Lagrangian particles tracking and passive scalar transport methodologies using the OpenFOAM software. The experimental validation confirmed the accuracy of the numerical simulations, particularly at an imposed total pressure of , showing an excellent agreement in particle distributions. The study emphasizes the computational efficiency and modeling of passive scalar transport, providing valuable understanding into the behavior of scalar quantities in microfluidic systems. An optimized diffusion coefficient value of was identified, showing its critical role in achieving accurate simulation results and optimizing the performance of microfluidic flow separators for contrast-enhanced ultrasound scanner calibration.

Analytical Model for Contaminant Transport in the CGCW and Aquifer Dual-Domain System Considering GMB Holes

Composite geomembrane cut-off walls (CGCW) have been widely used for the remediation of polluted sites, especially where the environmental conditions are complex. Accurate predictions of the GMB hole leakage and CGCW performance are essential for engineering design and cost control. This paper establishes empirical equations to predict the leakages through the CGCWs based on the numerical models. Additionally, an analytical solution for contaminant migration through the CGCW is proposed considering the effects of GMB holes. The accuracy of the established equations and analytical solution is verified by the numerical models. The key effects of the GMB thickness (TG), head loss (HG), cut-off wall hydraulic conductivity (kG), hole radius (rG) and shape on the leakage and CGCW performance are investigated. The results show that compared with other hole shapes, the leakage through the circular hole is lowest. This is mainly because the shape factor for the circular hole is 1.15–1.3 times lower than that for other shapes of holes with the same area. Additionally, the effects of the hole geometric properties and head loss on the CGCW performance can be more significant when the cut-off wall hydraulic coefficient is small. For example, the breakthrough time differences between the cases with rG = 0.005 m and 0.05 m are 0.8 and 5.0 years when kG = 10−10 and 10−9 m/s, respectively. This is because the impermeability of the CGCW is good when kG is small. This will weaken the impacts of the hole geometric properties on the leakage. The proposed empirical equations and analytical solution can provide effective suggestions for the design of the CGCW in different GMB hole cases.

Thermal Conductivity in Biphasic Silicon Nanowires.

The work unravels the previously unexplored atomic-scale mechanism involving the interaction of phonons with crystal homointerfaces. Silicon nanowires with engineered isotopic content and crystal phases were chosen for this investigation. Crystal polytypism, manifested by the presence of both diamond cubic and rhombohedral phases within the same nanowire, provided a testbed to study the impact of phase homointerfaces on phonon transport. The lattice thermal conductivity and its temperature response were found to be markedly different in the presence of polytypism. Its origin, however, was not traced to any acoustic mismatch as the polytypic nanowires presented a similar phonon spectrum as their counterparts. Rather, phenomenological modeling and atomistic simulations identified and quantified the role of atomically rough homointerfaces and the subsequent phonon scattering from such homointerfaces in shaping the phonon behavior. This framework provides the inputs necessary to advance the design and modeling of phonon transport in nanoscale semiconductors.

SolarDesign: An online photovoltaic device simulation and design platform

Abstract SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, perovskite, and tandem cells. The platform offers user-updatable libraries of basic photovoltaic materials and devices, device-level multi-physics simulations involving optical-electrical-thermal interactions, and circuit-level compact model simulations based on detailed balance theory. Employing internationally advanced numerical methods, the platform accurately, rapidly, and efficiently solves optical absorption, electrical transport, and compact circuit models. It achieves multi-level photovoltaic simulation technology from “materials to devices to circuits” with fully independent intellectual property rights. Compared to commercial software, the platform achieves high accuracy and improves speed by more than an order of magnitude. Additionally, it can simulate unique electrical transport processes in emerging solar cells, such as quantum tunneling, exciton dissociation, and ion migration.

Integrating Biofilm Growth and Degradation into a Model of Microplastic Transport in the Arctic Ocean

Integrating Biofilm Growth and Degradation into a Model of Microplastic Transport in the Arctic Ocean

Integrating Enzyme-Based Kinetics in Reactive Transport Models to Simulate Spatiotemporal Dynamics of Biomarkers during Chlorinated Ethene Degradation.

Biomarkers such as functional gene mRNA (transcripts) and proteins (enzymes) provide direct proof of metabolic regulation during the reductive dechlorination (RD) of chlorinated ethenes (CEs). Yet, current models to simulate their spatiotemporal variability are not flexible enough to mimic the homologous behavior of RDase functional genes. To this end, we developed new enzyme-based kinetics to model the concentrations of CEs together with the transcript and enzyme levels during RD. First, the model was calibrated to existing microcosm data on RD of cis-DCE. The model mirrored the tceA and vcrA gene expression and the production of their enzymes in Dehalococcoides spp. Considering tceA and vcrA as homologous instead of nonhomologous improved fitting of the mRNA time series. Second, CEs and biomarker patterns were explored as a proof of concept under groundwater flow conditions, considering degraders occurring in immobile and mobile states. Under both microcosm and flow conditions, biomarker-rate relationships were nonlinear hysteretic because tceA and vcrA acted as homologous genes. The mobile biomarkers additionally undergo advective-dispersive transport, which increases the nonlinearity and makes the observed patterns even more challenging to interpret. The model offers a thorough mechanistic description of RD while also allowing simulation of spatiotemporal dynamic patterns of various key biomarkers in aquifers.

Validating a Calibrated Model of a Groundwater Pump-And-Treat System Using Robust Multiple Regression

Validation of groundwater models is relatively challenging due to the need to reserve scarce water level data for calibration targets. In addition, traditional statistical validation metrics are unintuitive for non-technical audiences and do not directly identify model behaviors that require further refinement. We developed a novel model validation method that analyzes rate change events at pump-and-treat wells and statistically compares the water level responses at nearby monitoring wells between the data and model. The method takes advantage of events that occur alongside ambient pumping, unlike parameter estimation techniques that require independent drawdown or recovery events. The ability of the model to match well connections that are evident (or not evident) in the observations is characterized statistically, leading to four decision scenarios: model matches the observed connection (1) or lack thereof (2), model exhibits a connection that is not observed (3), or model over- or understates the observed connection (4). The method is applied to an FEHM-based groundwater flow and transport model that is shown to match 84.5% of the well connections analyzed. The method provides novel perspectives on the influence of calibration targets on the flow field and suggests that although the overall effect of drawdown targets was to improve the model, the choice of target well pairs creates flow pathways that may be inconsequential during normal operational conditions. The model adequately matches the flow over short spatial scales (<800 m) and over-represents the flow over greater distances (300–1200 m), suggesting the need for “null” drawdown targets in subsequent rounds of calibration.

Landscape fire PM2.5 and hospital admissions for cause-specific cardiovascular disease in urban China

There is a growing interest in the health impacts of PM2.5 originating from landscape fires. We conducted a time-series study to investigate the association between daily exposure to landscape fire PM2.5 and hospital admissions for cardiovascular events in 184 major Chinese cities. We developed a machine learning model combining outputs from chemical transport models, meteorological information and observed air pollution data to determine daily concentrations of landscape fire PM2.5. Furthermore, we fitted quasi-Poisson regression to evaluate the link between landscape fire PM2.5 concentrations and cardiovascular hospitalizations in each city, and conducted random-effects meta-analysis to pool the city-specific estimates. Here we show that, on a national scale, a rise of 1-μg/m3 in landscape fire PM2.5 concentrations is positively related to a same-day 0.16% (95% confidence interval: 0.01%–0.32%) increase in hospital admissions for cardiovascular disease, 0.28% (0.12%–0.44%) for ischemic heart disease, and 0.25% (0.02%–0.47%) for ischemic stroke. The associations remain significant even after adjusting for other sources of PM2.5. Our findings indicate that transient elevation in landscape fire PM2.5 levels may increase risk of cardiovascular diseases.

Transport properties of high Mach number hypersonic air plasmas

Abstract The collisional and transport properties of hypersonic plasmas have been simulated by an air plasma chemistry model, which includes collisions with electrons, ions and neutral species, elastic scattering, vibrational relaxation and a transport model. Species and thermal fluxes at the surface of the vehicle have been investigated as a function of post-shock gas temperature and species sticking coefficient for a fixed altitude of 50 km. At temperatures below about 5,000 K, the leading species are vibrationally excited nitrogen molecules, while at higher temperatures the molecular nitrogen and atomic oxygen dominate. Large fluxes of oxygen atoms have been observed in the simulations, on the order of 10^20 cm^-2s^ -1, which may lead to high rates of surface oxidation. Another subject of this study is temperature equilibration. For electrons, equilibration takes tens of microseconds, while the vibrational temperature equilibration time vary widely; from a few microseconds to hundreds of microseconds, depending on the gas temperature. Finally, the adiabatic parameter  was calculated for post-shock gas temperatures 5,000 K and 10,000 K and total gas density 1.2×10^17 cm^(-3). For post-shock gas temperature 5,000 K, only a slight reduction of gamma from the ideal gas value of ~1.4 is observed, while for 10,000 K it drops to ~1.26, which has a considerable impact on plasma properties. It was further established that the vibrational kinetics plays a leading role. While a single excited vibrational level of the N2 molecule captures the salient properties of the plasma, a detailed vibration kinetics is required for high-fidelity simulations.

Experimental investigation on cross-shore profile evolution of reef-fronted beach

Physical experiments on cross-shore profile evolution of the reef-fronted beach are conducted considering various offshore wave conditions and reef settings. Cross-shore beach profile evolution, sediment transport rate, and waves at the beach toe are analyzed. The reef-fronted beach is found to be resilient to erosion induced by offshore sediment transport. In present cases, the beach evolves from a sloping profile to a reflective profile, and onshore sediment transport leads to the formation of a swash berm. Both the shortwaves and infragravity waves at the beach toe play an important role in forming the beach shape. The berm foreshore slope mainly depends on the wave energy density in the infragravity band at the beach toe. Wave energy density in the shortwave band at the beach toe increases with reef submergences, while wave energy density in the infragravity band at the beach toe increases with offshore wave heights. The temporal evolution of sediment transport rate exhibits two modes, implying complex feedbacks occur between swash flows and beach profile evolution. The bulk transport on the reef-fronted beach is parameterized by the relative height of shortwaves and wave steepness of both shortwaves and infragravity waves at the beach toe. A conceptual model of bulk transport on the beach is proposed that the bulk transport increases with the Gourlay number, indicating that reef-fronted beaches with a well-developed reef flat are resilient to increasing wave exposure.

Time-dependent full-radius integrated modeling of the DTT tokamak main plasma scenarios

Abstract We use an integrated modeling workflow with the transport code ASTRA coupled with the quasi-linear transport model TGLF-SAT2, the neoclassical model NCLASS, the FACIT model for the neoclassical impurity transport and the IMEP routines for the pedestal calculations, in order to predict the evolution of the plasma profiles for the DTT tokamak main scenarios. The simulations cover the whole confined plasma radius, up to the separatrix, and the time evolution of the plasma including the early phase in limiter configuration, the whole current ramp-up phase in L-mode divertor configuration, the L-H transition and part of the stationary H-mode phase. Six fields are predicted, i.e. the ion and electron temperatures, the electron density, two impurity densities and the plasma current. The simulations indicate that the main DTT scenarios are within the technical capabilities of the machine. They also indicate that the DTT full power, full current, full field scenario will be able to operate in H-mode with a duration of the flat-top phase of the order of \sim30 seconds, and plasma parameters allowing a core-edge integrated study of the power exhaust, which is the main mission of the device. The simulations show also a strong flexibility of the DTT plasmas, that allows DTT to study reactor-relevant conditions unexplored by existing tokamaks.