Spatiotemporal Research Articles

(Invited) Plasma Reforming of Liquid Hydrocarbons

Engineering systems that more sustainably use our natural resources is central to both reducing the risks associated with CO2 emissions and making the long-term transition to a more circular, sustainable, and electrified economy. In particular, developing electrically driven technologies to leverage the energy density of liquids to produce clean fuels, e.g., H2, without making CO2, could be game changing. Here, we investigate the potential of directly exciting plasmas in liquid hydrocarbons to create multi-phase reaction environments, i.e., environments where plasma (ionized gas), H2, gaseous and liquid hydrocarbons, and solid carbon are all present simultaneously, to produce H2, unsaturated C2s, and carbon. The ultimate target is direct transformation of liquid hydrocarbons to gaseous H2 and solid, easy-to-separate carbon using electricity that can be provided from any source and/or points of use.This talk will focus on our recent experiments to strike and sustain low and high frequency plasma discharges in liquid hydrocarbon jets to simultaneously generate gaseous H2, C2s, and solid carbon at high rates. Preliminary experiments show that (i) input energy is not wasted in simply vaporizing the liquid - so the system’s specific energy input preferentially drives hydrocarbon cracking; (ii) discharge drive frequency and plasma space time have a big effect on the H2 specific energy requirement (SER) and H2 generation rate, respectively; (iii) feedstock chemistry affects the H2 and C2 products observed; (iv) carbon particulates rapidly form in the liquid hydrocarbon, but they can be separated easily; and (v) the SER for H2 production is 25-30 kWhr/kg, roughly two times less than water electrolysis in practice (60 kWhr/kg). Hydrocarbon conversion, reaction rates, and characterization of the plasma (OES, high speed imaging), gas (MS), liquid (GC/MS), and solid phase (SEM, Raman) products as a function of plasma operating conditions (high and low AC drive frequency; liquid-plasma contact time) and hydrocarbon source will be discussed.

Non-reversible lifts of reversible diffusion processes and relaxation times

AbstractWe propose a new concept of lifts of reversible diffusion processes and show that various well-known non-reversible Markov processes arising in applications are lifts in this sense of simple reversible diffusions. Furthermore, we introduce a concept of non-asymptotic relaxation times and show that these can at most be reduced by a square root through lifting, generalising a related result in discrete time. Finally, we demonstrate how the recently developed approach to quantitative hypocoercivity based on space–time Poincaré inequalities can be rephrased and simplified in the language of lifts and how it can be applied to find optimal lifts.

A novel image denoising technique with Caputo type space–time fractional operators

A novel image denoising model, namely Full Fractional Total Variation (TVFF), based on the Rudin-Osher-Fatemi (ROF) and the fractional total variation models is presented. The leading advantage of TVFF model is that it uses fractional derivatives with length scale parameters instead of ordinary derivatives with respect to both time and spatial variables in the diffusion equation. The Riesz–Caputo fractional derivative operator is used to disperse nonlocal influence throughout all directions, whereas the Caputo fractional derivative concept is employed for time fractional derivatives. Therefore, the influence of neighboring pixels is given greater weight compared to those situated farther away and this reflects the consideration behind denoising process better. Moreover, the numerical approach is constructed, and its stability and convergence properties are thoroughly examined. To show the superiority of our model, the denoised images are subjected to visual and numerical comparisons using metrics such as the Signal-to-Noise Ratio (SNR), the Structural Similarity Index Measure (SSIM) and the Edge-Retention Ratio (ERR). The performance of the TVFF method is evaluated under various types of noise, including Poisson, Speckle, and Salt & Pepper, and the results are compared with those obtained using Gauss and Median Filters. Furthermore, the proposed method is applied to both blind and synthetic images, thereby showcasing its versatility and applicability across diverse datasets. The outcomes showcase the substantial potential of our enhanced model as a versatile and efficient tool for image denoising.

Fusion of Lagrangian drifter data and numerical model outputs for improved assessment of turbulent dispersion

Abstract. Transport and dispersion processes in the ocean are crucial, as they determine the lifetime and fate of biological and chemical quantities drifting with ocean currents. Due to the complexity of the coastal ocean environment, numerical circulation models have difficulties to accurately simulate highly turbulent flows and dispersion processes, especially in highly energetic tidal basins such as the eastern English Channel. A method of improving the results of coastal circulation modeling and tracer dispersion in the Dover Strait is proposed. Surface current velocities derived from Lagrangian drifter measurements in November 2020 and May 2021 were optimally interpolated in time and space to constrain a high-resolution coastal circulation MARS model, with careful attention given to selecting ensemble members composing the model covariance matrix. The space–time velocity covariances derived from model simulations were utilized by the optimal interpolation algorithm to determine the most likely evolution of the velocity field under constraints provided by Lagrangian observations and their error statistics. The accuracy of the velocity field reconstruction was evaluated at each time step. The results of the fusion of model outputs with surface drifter velocity measurements show a significant improvement (by ∼ 50 %) of the model capability to simulate the drift of passive tracers in the Dover Strait. Optimized velocity fields were used to quantify the absolute dispersion in the study area. The implications of these results are important, as they can be used to improve existing decision-making support tool or design new tools for monitoring the transport and dispersion in a coastal ocean environment.

Perimeter Control-Based Coordinated Ramp Metering with Linked Queue Management for Freeway Networks

This paper proposes a macroscopic fundamental diagram (MFD)-based perimeter control approach for coordinated ramp metering, aiming to minimize the total time spent (TTS) of a freeway network (stretch) with multiple bottlenecks. To address problems incurred from limited queue storage space and minimum green time at on-ramps, the proposed method extends previous approaches in two aspects. Firstly, a coordinated ramp control approach is proposed to ensure that the total metered on-ramp flow remains below the value determined by the MFD-based perimeter regulator, thereby preventing network over-saturation. Secondly, a novel queue management approach is developed to prevent on-ramp queue saturation and its associated side effects, for example, spillback or insufficient control actions. This approach determines the quasi-optimal flow of the next upstream (slave) on-ramp, based on the predicted queue of each (master) on-ramp suffering from excessive queues. The proposed perimeter controller was tested using SUMO, a microscopic simulator, on a realistic freeway network, and compared with other existing strategies, including HERO and perimeter controllers with other metered on-ramp flow distribution approaches. The simulation results suggest that the proposed strategy can ( i) effectively mitigate congestion and maintain capacity within the freeway network, resulting in reduced overall TTS, and ( ii) appropriately delay or prevent queue saturation at on-ramps.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection

BackgroundThe government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space–time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.MethodsSpatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.ResultsMalaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010–2011, and with P. vivax incidence in the north and central regions during 2012–2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.ConclusionsMalaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Revealing the journey of molecules and particles in heterogeneous, porous materials for cigarette filters

Molecular dynamics and mass transportation in porous structures provide a basis for us to understand catalysis, energy storage and generation, and biological processes in porous confinements. While conventional methods extract macroscopic information in an ensemble-averaged manner, we intend to follow the journey of individual particles and molecules in porous structures relevant to cigarette filters by tracking the single-object dynamics in real space and real time. Nanoparticles of various sizes are embedded in fibrous frameworks of agarose where small particles (50 nm) can explore pores and their connections, locally mapping out the porous structure, middle-sized particles (100 nm) are trapped in single pores to fluctuate within, and large particles (500 nm) are fully immobilized by surrounding fibers. This model system is relevant to the retention and filtration of tar particles or other kinds of particulate matters by fibrous cellulose frequently used in cigarette filters. A molecular tracer is loaded to zeolite-based porous structures, where the majority are fixated in space by adsorption or micropore trapping, exhibiting localized trajectories within a 10-nm radius, and the minority are mobile to scout macropores. This molecular system may elucidate on how aromatic molecules like PAHs are adsorbed and transported in a matrix of mixed micro-, meso-, and macropores.

STDAN: Deformable Attention Network for Space-Time Video Super-Resolution.

The target of space-time video super-resolution (STVSR) is to increase the spatial-temporal resolution of low-resolution (LR) and low-frame-rate (LFR) videos. Recent approaches based on deep learning have made significant improvements, but most of them only use two adjacent frames, that is, short-term features, to synthesize the missing frame embedding, which cannot fully explore the information flow of consecutive input LR frames. In addition, existing STVSR models hardly exploit the temporal contexts explicitly to assist high-resolution (HR) frame reconstruction. To address these issues, in this article, we propose a deformable attention network called STDAN for STVSR. First, we devise a long short-term feature interpolation (LSTFI) module that is capable of excavating abundant content from more neighboring input frames for the interpolation process through a bidirectional recurrent neural network (RNN) structure. Second, we put forward a spatial-temporal deformable feature aggregation (STDFA) module, in which spatial and temporal contexts in dynamic video frames are adaptively captured and aggregated to enhance SR reconstruction. Experimental results on several datasets demonstrate that our approach outperforms state-of-the-art STVSR methods. The code is available at https://github.com/littlewhitesea/STDAN.

The randomness and determinacy of wall pressure fluctuations in incompressible flow

Wall pressure fluctuations caused by turbulent boundary layers have a significant impact on aircraft structural vibration and cabin noise. This study aims to investigate the mechanism of turbulence-induced pressure fluctuations by focusing on the randomness of wall pressure fluctuations, analyzed in both the time–frequency and spatial-wavenumber domains using measured data obtained from a phase array in a wind tunnel. Three roughness elements were designed and installed upstream of the plate to manipulate the turbulent boundary layer at a specific Mach number. The results of the investigation demonstrate that the disturbance strength induced by the roughness element influences the randomness of wall pressure fluctuations, in addition to the parameters utilized for data analysis. Generally, stronger turbulence fluctuations tend to decrease the randomness of pressure fluctuations. Moreover, wall pressure fluctuations also exhibit certain statistical principles that cannot be precisely calculated using mathematical expressions, highlighting their inherent randomness. Further investigation into randomness in the spatial-wavenumber domain revealed the hydrodynamic modes of turbulence fluctuations with varying convection velocity analyzed through wavenumber maps computed using the beamforming algorithm. These modes with variable convective speed significantly contribute to the generation of randomness in wall pressure fluctuations. Both the time–frequency domain and the spatial-wavenumber domain affect the randomness characteristics of wall pressure fluctuations. However, such effects are not easily discernible through a rudimentary analysis of the space–time correlation of turbulence fluctuations.

Regularization in space–time topology optimization for additive manufacturing

In additive manufacturing, the fabrication sequence has a large influence on the quality of manufactured components. While planning of the fabrication sequence is typically performed after the component has been designed, recent developments have demonstrated the possibility and benefits of simultaneous optimization of both the structural layout and the corresponding fabrication sequence. This is particularly relevant in multi-axis additive manufacturing, where rotational motion offers enhanced flexibility compared to planar fabrication. The simultaneous optimization approach, called space–time topology optimization, introduces a pseudo-time field to encode the manufacturing process order, alongside a pseudo-density field representing the structural layout. To comply with manufacturing principles, the pseudo-time field needs to be monotonic, i.e., free of local minima. However, explicitly formulated constraints proposed in prior work are not always effective, particularly for complex structural layouts that commonly result from topology optimization.In this paper, we introduce a novel method to regularize the pseudo-time field in space–time topology optimization. We conceptualize the monotonic additive manufacturing process as a virtual heat conduction process starting from the surface upon which a component is constructed layer by layer. The virtual temperature field, which shall not be confused with the actual temperature field during manufacturing, serves as an analogy for encoding the fabrication sequence. In this new formulation, we use local virtual heat conductivity coefficients as optimization variables to steer the temperature field and, consequently, the fabrication sequence. The virtual temperature field is inherently free of local minima due to the physics it resembles. We numerically validate the effectiveness of this regularization in space–time topology optimization under process-dependent loads, including gravity and thermomechanical loads.

Accretion mechanism for regular black holes with asymptotically Minkowski Cores and improved Schwarzschild black holes

In this paper, we investigate the fascinating implications of the accretion process for two different kinds of black holes. A detailed analysis of the moving fluid in this accretion process is provided. The radial velocity u(r), the energy density ρ(r), speed of sound a2s and the mass accretion rate Ṁ of the regular black holes and improved Schwarzschild black hole are explored. Interestingly, we consider the most generic black hole space–times with isotropic fluid for this accretion process. The required and necessary physical behaviors around the black holes, such as the radial velocity, the energy density of the moving fluid within the scope of dust, stiff fluid and quintessence, are achieved. Our findings suggest that critical parameters like l in regular black holes, Γ in improved Schwarzschild black holes and the state parameter k are responsible for the accretion mechanism.

Constellation design and performance of future quantum satellite gravity missions

Temporal aliasing is currently the largest error contributor to time-variable satellite gravity field models. Therefore, the evolution of sensor technologies has to be complemented by strategies to reduce temporal aliasing errors. The most straightforward way to improve temporal aliasing is through extended satellite constellations because they improve the observation geometry and increase the achievable temporal resolution. Therefore, strategies to optimize the design of larger satellite constellations are investigated in this contribution. A complete constellation modeling procedure is presented, starting from primary design variables (such as the required targeted resolutions) and concluding with concrete orbital elements for the individual satellites. In parallel, it is evaluated if improved instrument sensitivities based on quantum technologies (cold atom interferometry) can be fully exploited in the case of larger constellations. For this, future quantum satellite gravity missions adopting the gradiometry concept (similar to the GOCE mission) and the low-low satellite-to-satellite tracking concept (similar to GRACE/-FO) are simulated on optimized constellations with up to 6 satellites/pairs. The retrieval performance of a 6-pair mission in terms of the global equivalent water height RMS can be improved by a factor of roughly 3 compared to an inclined double-pair mission. 3D-gradiometry intrinsically has a better de-aliasing behavior but has extremely high accuracy requirements for the gradiometer (about 10 µEotvos) and the attitude reconstruction to be of any benefit. All simulations show that when incorporating improved sensor technologies, such as future quantum sensing instruments in extended constellations, temporal aliasing will remain the dominant error source by far, up to five orders of magnitude larger than the instrument errors. Therefore, improving sensor technologies has to go hand in hand with larger satellite constellations and improved space–time parameterization strategies to further reduce temporal aliasing effects.Graphical

How materialities and space–time travellings in class can breathe new life into Swedish secondary school Natural Science sexuality education

In this study, we investigate the phenomenon of Swedish Natural Science sexuality education. These classes tend to provide factual knowledge, focus on the negative outcomes of sexuality, be heteronormative and include little time for discussion—like much school sexuality education across the world—and this study aims to contribute ideas about new becomings of Natural Science sexuality education. Baradian theorising was used to explore how materialities and space and time travels within the classroom can challenge often-dominant perspectives. Data were produced in a secondary school and consist of teacher-researcher discussions and participatory observations in class. A futuristic case introduced students to spaces, times and materialities that not only helped the group move beyond a medical focus but also made the sexuality education more student centred. The participating teachers suggested imaginary lust-oriented scenarios for the 15–16-year-old students as an entrance to more preventive messages in teaching. A taken-for-granted heterosexual premise was also challenged with gender-neutral words and pronouns, an exercise on how to use both condoms and dental dams, and a time travel into future possibilities for reproduction and parenting. The acknowledgement of spacetimematter intra-activity in teaching thereby enabled new becomings of Swedish Natural Science sexuality education. However, although this study suggests how dominant medical and heterosexual perspectives can be challenged, it also made visible the absence of cultural, religious, asexuality and disability perspectives in Swedish sexuality education.

On the Response of the Transition Region and the Corona to Rapid Excursions in the Chromosphere

Spicules are the thin hair/grass-like structures that are prominently observed at the chromospheric solar limb. It is believed that fibrils and rapid blueshifted and redshifted excursions (RBEs and RREs; collectively referred to as REs) correspond to on-disk counterparts of type I spicules and type II spicules, respectively. Our investigation focuses on observing the response of these REs alongside similar spectral features in the chromosphere, transition region (TR), and corona, utilizing space–time plots derived from coordinated observations from the Swedish 1 m Solar Telescope/Hα, Interface Region Imaging Spectrograph (IRIS), and Solar Dynamics Observatory. Our analysis reveals upflowing REs, promptly reaching temperatures characteristic of the TR and corona, indicating a multithermal nature. Similarly, downflowing features exhibiting similar spectral signatures over the disk display plasma motion from the corona to chromospheric temperatures, demonstrating a multithermal nature. In addition to distinct upflows and downflows, we observe sequential upflow and downflow along the same path, depicting a distinctive parabolic trajectory in space–time plots of observations sampling TR and various coronal passbands. Similar to isolated upflows and downflows, these REs also exhibit a multithermal nature throughout their trajectory. Furthermore, our results reveal a more intricate motion of the REs in which both upflow and downflow coexist at the same spatial location. On a different note, our analysis, utilizing coordinated IRIS spectral observations, shows spatiotemporal redshifts/downflows in both the TR and chromosphere, suggesting that at least subsets of the strong redshifts/downflows observed in TR temperature spectra result from the return from the upper atmosphere flow of plasma in the form of bundles of spicules or features exhibiting similar spectra.

A class of space–time discretizations for the stochastic [formula omitted]-Stokes system

The main objective of the present paper is to construct a new class of space–time discretizations for the stochastic p-Stokes system and analyze its stability and convergence properties. We derive regularity results for the approximation that are similar to the natural regularity of solutions. One of the key arguments relies on discrete extrapolation that allows us to relate lower moments of discrete maximal processes. We show that, if the generic spatial discretization is constraint conforming, then the velocity approximation satisfies a best-approximation property in the natural distance. Moreover, we present an example such that the resulting velocity approximation converges with rate 1/2 in time and 1 in space towards the (unknown) target velocity with respect to the natural distance. The theory is corroborated by numerical experiments.

An ensemble neural network approach for space–time landslide predictive modelling

There is an urgent need for accurate and effective Landslide Early Warning Systems (LEWS). Most LEWS are currently based on temporally-aggregated measures of rainfall derived from either in-situ measurements or satellite-based rainfall estimates. Relying on a summary metric of precipitation may not capture the complexity of the rainfall signal and its dynamics in space and time in triggering landslides. Here, we present a proof-of-concept for constructing a LEWS based on an integrated spatio-temporal modelling framework. Our proposed methodology builds upon a recent approach that uses a daily rainfall time series instead of the traditional scalar aggregation. Specifically, we partition the study area into slope units and use a Gated Recurrent Unit (GRU) to process satellite-derived rainfall time series and combine the output features with a second neural network (NN) tasked with capturing the effect of terrain characteristics. To assess if our approach enhances accuracy, we applied it in Vietnam and benchmarked it against a modelling counterpart where we replaced the rainfall time series with the corresponding scalar representative of the cumulated precipitation. The corresponding duration was set at 14 days as it proved to produce the best performance. Our results show that our protocol leads to better performance in hindcasting landslides when making use of the rainfall as a continuous signal over time. While not tested here, our approach can be extended to rainfall obtained from weather forecasts, potentially leading to actual landslide forecasts.

Visit probability and accessibility within space–time prism of activity program

Space–time prism (STP) is widely applied to measure individuals’ ability to reach opportunities given the resource limitations. The majority of STP models treat prism-based accessibility as binary measures, wherein all locations within a prism are assumed equally accessible while others are deemed inaccessible. Although a few STP models examined heterogeneous interiors by modeling visit probabilities within the STPs, they primarily focused on trip-level analysis and did not explore the application for accessibility measurement. This study proposes a model framework based on multi-state supernetworks for constructing and estimating the probabilistic STPs of daily activity programs. The estimation is implemented with latent class models to account for individual heterogeneities in travel and activity participation. Based on the probabilistic STPs, we suggest a space–time accessibility measurement incorporating visit probabilities. We validate the model framework using mobility trajectory data collected in the Netherlands and demonstrate that the visit probability model can effectively capture the probabilistic STP interiors and the proposed accessibility measurement can provide a comprehensive evaluation of accessibility in the presence of activity chains.

Adjoint-based optimisation of time- and span-periodic flow fields with Space–Time Spectral Method: Application to non-linear instabilities in compressible boundary layer flows

We aim at computing time- and span-periodic flow fields in span-invariant configurations. The streamwise and cross-stream derivatives are discretised with finite volumes while time and the span-direction are handled with pseudo-spectral Fourier-collocation methods. Doing so, we extend the classical Time Spectral Method (TSM) to a Space–Time Spectral Method (S-TSM), by considering non-linear interactions of a finite number of time and span harmonics. For optimisation, we introduce an adjoint-based framework that allows efficient computation of the gradient of any cost functional with respect to a large-dimensional control parameter. Both theoretical and numerical aspects of the methodology are described: evaluation of matrix–vector products with S-TSM Jacobian (or its transpose) by algorithmic differentiation, solution of fixed-points with quasi-Newton method and de-aliasing in time and space, solution of direct and adjoint linear systems by iterative algorithms with a block-circulant preconditioner, performance assessment in CPU time and memory. We illustrate the methodology on the case of 3D instabilities (first Mack mode) triggered within a developing adiabatic boundary layer at M = 4.5. A gradient-ascent method allows to identify a finite-amplitude 3D forcing that triggers a non-linear response exhibiting the strongest time- and span-averaged drag on the flat-plate. In view of flow control, a gradient-descent method finally determines a finite amplitude 2D wall-heat flux that minimises the averaged drag of the plate in presence of the previously determined non-linear optimal forcing.

A low-storage synchronization framework for blockchain systems

The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant amount of blockchain data. Therefore, each node must spend significant amount of storage space and initialization synchronization time. To solve the above problems, in this research, we propose a secure and agile synchronization framework for low storage blockchains. First, we design a K-extreme segment algorithm, which reduces the synchronization time by returning only the first and last k blocks of each block segment at once to the local storage. Next, we decentrally store the block data of the blockchain by IPFS and establish a backup mechanism by IPFS-cluster. Finally, due to use of distributed storage, the nodes must request un-stored block data from IPFS, causing an increase in the throughput of the blockchain network. To avoid network congestion, we propose the working set algorithm to improve the hit ratio of the local storage and reduce the number of requests to decrease throughput. In summary, our experiments demonstrate that the ratio of full nodes to low storage nodes is significantly lower for nodes with higher storage limits compared to those with lower storage limits. In other words, a higher storage limit results in more low storage nodes which can be permitted to ensure that the blockchain network is robust and reliable. Therefore, our proposed framework can provide reliable low storage nodes for the blockchain. The node can reduce the local storage pressure and can still maintain the full functionality of blockchains.

Supply of bioelectricity from sugarcane bagasse in Brazil: a space–time analysis

Bioelectricity generation from sugarcane is significant across Brazil and is related to regional market structure characteristics where the mills are located. To understand the distribution and conjuncture of this sector, this study analyzes the pattern of location, concentration and clustering of the bioelectricity supply from sugarcane bagasse in Brazil, for 2017 and 2022. The data were obtained from the Brazilian National Electric Energy Agency, and the methodology was based on concentration indices and scan statistics. The results showed that the Southeast region presented the most thermoelectric power plants and installed capacity. The Southeast and Midwest regions were highly concentrated in terms of quantity and sugarcane bioelectricity installed capacity. Five clusters were identified for the number of power plants in 2017; for 2022, there were eight clusters. Regarding installed potential, there were 14 clusters in 2017 and 23 clusters in 2022, all statistically significant. The existence of clusters provides information on the competitive advantages in the national market, which can drive new investments in more densified areas or in the neighborhood. Identification of the location and concentration pattern showed that facilities in the state of São Paulo and the Northeast coast were responsible for the most important share of supply. These results indicate to investors the impact of electricity generation on the sector and the most relevant location for installing new thermoelectric plants.