Parameter Space Research Articles

CloudFlex: A Flexible Parametric Model for the Small-scale Structure of the Circumgalactic Medium

We present CloudFlex, an open-source tool for predicting absorption-line signatures of cool gas in galaxy halos with small-scale structure. Motivated by analyses of ∼104 K material in hydrodynamical simulations of turbulent, multiphase media, we model cool gas structures as complexes of cloudlets sampled from a power-law distribution of mass ∝mcl−α with velocities drawn from a turbulent velocity field. The user may specify α, the lower limit of the cloudlet mass distribution ( mcl,min ), and several other parameters that set the mass, size, and velocity distribution of the complex. This permits investigation of the relation between these parameters and absorption-line observables. As a proof-of-concept, we calculate the Mg ii λ2796 absorption induced by the cloudlets in background quasi-stellar object (QSO) spectra. We demonstrate that, at fixed metallicity, the covering fraction of sight lines with equivalent widths W 2796 < 0.3 Å increases significantly with decreasing mcl,min , cloudlet number density (n cl), and complex size. We then use this framework to predict the halo-scale W 2796 distribution around ∼L * galaxies. We show that the observed incidences of W 2796 > 0.3 Å sight lines with impact parameters 10 kpc < R ⊥ < 50 kpc in projected QSO–galaxy studies are consistent with our model over much of parameter space. However, they are underpredicted by models with mcl,min≥100M⊙ and n cl ≥ 0.03 cm−3, in keeping with a picture in which the inner cool circumgalactic medium (CGM) is dominated by numerous low-mass cloudlets (m cl ≲ 100M ⊙) with a volume filling factor ≲1%. When used to model absorption-line data sets built from multi-sight line and/or spatially extended background probes, CloudFlex enables detailed constraints on the size and velocity distributions of structures comprising the photoionized CGM.

An effective model for the tidal disruption of satellites undergoing minor mergers with axisymmetric primaries

According to the hierarchical formation paradigm, galaxies form through mergers of smaller entities and massive black holes (MBHs), if present at their centers, migrate to the nucleus of the newly formed galaxy, where they form binary systems. The formation and evolution of MBH binaries, and in particular their coalescence timescale, is highly relevant for current and future facilities aimed at detecting the gravitational wave signal produced by the MBHs close to coalescence. While most of the studies targeting this process are based on hydrodynamic simulations, the high computational cost makes a complete parameter space exploration prohibitive. Semianalytic approaches represent a valid alternative, but they require ad hoc prescriptions for the mass loss of the merging galaxies in minor mergers due to tidal stripping, which is not commonly considered or is at best modelled assuming very idealised geometries. In this work we propose a novel, effective model for the tidal stripping in axisymmetric potentials, to be implemented in semi-analytic models. We validated our semi-analytic approach against N-body simulations considering different galaxy sizes, inclinations, and eccentricities, finding only a moderate dependence on the orbit eccentricity. In particular, we find that, for almost circular orbits, our model mildly overestimates the mass loss, and this is due to the adjustment of the stellar distribution after the mass is removed. Nonetheless, the model exhibits a very good agreement with simulations in all the considered conditions, and thus represents an extremely powerful addition to semi-analytic calculations.

The GAPS Programme at TNG

Context. Due to observational biases, a large fraction of known exoplanets are short-period objects. However, the search for planets began more than 20 yr ago, and so it is already possible – with the use of a suitable dataset – to begin exploring a wider range of the parameter space, such as that encompassing long-period planets. Aims. The aim of this paper is to investigate the presence of long-period giant companions in two systems where one or more planets are already known and for which a long-term trend in the radial velocities (RVs) was noted in previous works. Methods. Over the last 11 yr, we have collected 122 spectra of HD 75898 and 72 spectra of HD 11506 with the High Accuracy Radial velocity Planet Searcher for the Northern hemisphere (HARPS-N) in the framework of the Global Architecture of Planetary Systems (GAPS) project, from which we derived precise RV and activity indicator measurements. Additional RV data from the High-Resolution Echelle Spectrometer (HIRES) are also used here to increase the total time span. For our RV analysis, we used PyORBIT, an advanced Python tool for the simultaneous Bayesian analysis of RVs and stellar activity indicators. In addition, we used astrometric (Gaia DR3) and imaging archive data to complete our analysis. In particular, we combined RVs and astrometry to better constrain the mass and period of the new long-period companions. Results. We find evidence for one additional long-period companion (gas giant planet or brown dwarf) in both systems considered. The new candidate for HD 75898 has a period of roughly 18 yr and a true mass of around 8.5 Mj. For HD 11506, we confirm the new object (planet d) recently announced using HIRES data but we find that the period and true mass are both almost double the values based on HIRES results (Pd ~ 72 yr, Md ~ 13 Mj). In addition, for HD 75898, we also find evidence of an activity cycle affecting RVs with a period of one order of magnitude lower than found in the literature.

Gauss-Bonnet Cosmology: large-temperature behaviour and bounds from Gravitational Waves

We provide a transparent discussion of the high temperature asymptotic behaviour of Cosmology in a dilaton-Einstein-Gauss-Bonnet (dEGB) scenario of modified gravity with vanishing scalar potential. In particular, we show that it has a clear interpretation in terms of only three attractors (stable critical points) of a set of autonomous differential equations: w = -1/3, w = 1 and 1 < w < 7/3, where w ≡ p/ρ is the equation of state, defined as the ratio of the total pressure and the total energy density. All the possible different high-temperature evolution histories of the model are exhausted by only eight paths in the flow of the set of the autonomous differential equations. Our discussion clearly explains why five out of them are characterized by a swift transition of the system toward the attractor, while the remaining three show a more convoluted evolution, where the system follows a meta-stable equation of state at intermediate temperatures before eventually jumping to the real attractor at higher temperatures. Compared to standard Cosmology, the regions of the dEGB parameter space with w = -1/3 show a strong enhancement of the expected Gravitational Wave stochastic background produced by the primordial plasma of relativistic particles of the Standard Model. This is due to the very peculiar fact that dEGB allows to have an epoch when the energy density ρ rad of the relativistic plasma dominates the energy of the Universe while at the same time the rate of dilution with T of the total energy density is slower than what usually expected during radiation dominance. This allows to use the bound from Big Bang Nucleosynthesis (BBN) to put in dEGB a constraint T RH ≲ (108 – 109) GeV on the reheating temperature of the Universe T RH. Such BBN bound is complementary to late-time constraints from compact binary mergers.

Cautionary Tales on Heating-rate Prescriptions in Kilonovae

A major ingredient for kilonova lightcurves is the radioactive heating rate and its dependence on the electron fraction and velocity of the ejecta and, in principle, on the nuclear mass formula. Heating-rate formulae commonly used as the basis for kilonova models previously employed in the literature produce substantially different outputs for high electron fractions (Y e ≳ 0.3) and at late times (t ≳ 1 day) compared to newer prescriptions. Here, we employ standard semianalytical models for kilonovae with better heating rate prescriptions valid for the full parameter space of kilonova velocities and electron fractions to explore the impact of the heating rate on kilonova lightcurves. We show the dangers of using inappropriate heating rate estimates by simulating realistic observations and inferring the kilonova parameters via a misspecified heating-rate prescription. While providing great fits to the photometry, an incorrect heating-rate prescription fails to recover the input ejecta masses with a bias significantly larger than the typical statistical uncertainty. This bias from an incorrect prescription has significant consequences for interpreting kilonovae, their use as additional components in gamma-ray burst afterglows, and understanding their role in cosmic chemical evolution or for multimessenger constraints on the nuclear equation of state. We showcase a framework and tool to better determine the impact of different modeling assumptions and uncertainties on inferences into kilonova properties.

Underwater blade-free triboelectric nanogenerator for harvesting current energy in low-speed current

Ocean current energy bears the characteristics of wide distribution and high energy density. Harvesting current energy as the in-situ power for distributed ocean sensors will greatly enhance the sustainability of ocean sensing. In many parts of the ocean, the current speed is not desirable (low or unstable) for conventional current energy harvesting devices. Here, an underwater blade-free triboelectric nanogenerator (UBF-TENG) is developed to effectively utilize the low-speed currents through the flow-induced vibration (FIV). The power generation unit is fully enclosed inside the shell, effectively reducing the direct impact and underwater corrosion. The study demonstrates that within the established experimental parameters space, the optimized UBF-TENG can facilitate a start-up speed as low as 0.14m/s. The UBF-TENG has achieved a maximum open-circuit voltage output of 250V and a maximum short-circuit current output of 2.2μA at 0.76m/s. The charging capacity of the UBF-TENG is tested with various capacitors, demonstrating the application potential of the UBF-TENG as an in-situ power supply underwater.

A new efficient imaging reconstruction method for muon scattering tomography

Muon scattering tomography (MST) is a promising technique in nuclear security inspection, utilizing its multiple scattering property which is sensitive to elements with high atomic numbers, and is also correlated with the material density. A proper imaging reconstruction method is essential for revealing the spatial distribution of materials within an object from measurements. This work proposes a new imaging reconstruction method called “big voxel and angle capping”. The scattering angle is reconstructed using the conventional Point of Closest Approach (PoCA) method. In the new method, reconstructed scattering angles belonging to voxels in an l×m×n matrix are aggregated into the center voxel, termed the “big voxel”. This significantly reduces statistical fluctuations in each big voxel. Subsequently, the influence of large scattering angles on determining material densities in big voxels is mitigated using a capping angle. The proposed algorithm is tested on the experimental data comprising approximately 1.8×104 muon scattering events detected in ∼ 24 h. The mean square error (MSE) and the structural similarity index measure (SSIM) indices are employed to quantitatively assess the imaging quality and optimize the new method. After scanning the two major parameters, the optimal parameter space is achieved with the big voxel size of ∼ 9, and the capping angle of ∼4∘ for tungsten blocks. The imaging quality is more sensitive to the capping angle than the big voxel size. An appropriate capping angle effectively removes large artifacts in reconstructed images caused by muon events with large scattering angles. This method is efficient in terms of both time and storage consumption.

Influence of sinusoidal forcing on the master FitzHugh–Nagumo neuron model and global dynamics of a unidirectionally coupled two-neuron system

In this paper, we investigate a seven-parameter, five-dimensional dynamical system, specifically a unidirectional coupling of two FitzHugh–Nagumo neuron models, with one neuron being sinusoidally driven. This master–slave configuration features neuron N1 as the master, subjected to an external sinusoidal electrical current, and neuron N2 as the slave, interacting with N1 through an electrical force. We report numerical results for three distinct scenarios where N1 operates in (i) periodic, (ii) quasiperiodic, and (iii) chaotic regimes. The primary objective is to explore how the dynamics of the master neuron N1 influence the coupled system’s behavior. To achieve this, we generated cross sections of the seven-dimensional parameter space, known as parameter planes. Our findings reveal that in the periodic regime of N1, the coupled system exhibits period-adding sequences of Arnold tongue-like structures in the parameter planes. Furthermore, regions of multistability can also be identified in these parameter planes of the coupled system. In the quasiperiodic regime, regions of periodic motion are absent, with only regions of quasiperiodic and chaotic dynamics present. In the chaotic regime of N1, the parameter planes display regions of chaos, hyperchaos, and transient hyperchaos.

Interstellar Glycolaldehyde, Methyl Formate, and Acetic Acid. II. Chemical Modeling of the Bimodal Abundance Pattern in NGC 6334I

Gas-phase abundance ratios between C2H4O2 isomers methyl formate (MF), glycolaldehyde (GA), and acetic acid (AA) are typically on the order of 100:10:1 in star-forming regions. However, an unexplained divergence from this neat relationship was recently observed toward a collection of sources in the massive protocluster NGC 6334I; some sources exhibited extreme MF:GA ratios, producing a bimodal behavior between different sources, while the MF:AA ratio remained stable. Here, we use a three-phase gas-grain hot-core chemical model to study the effects of a large parameter space on the simulated C2H4O2 abundances. A combination of high gas densities and long timescales during ice-mantle desorption (∼125–160 K) appears to be the physical cause of the high MF:GA ratios. The main chemical mechanism for GA destruction occurring under these conditions is the rapid adsorption and reaction of atomic H with GA on the ice surfaces before it has time to desorb. The different binding energies of MF and GA on water ice are crucial to the selectivity of the surface destruction mechanism; individual MF molecules rapidly escape the surface when exposed by water loss, while GA lingers and is destroyed by H. Moderately elevated cosmic-ray ionization rates can increase absolute levels of “complex organic molecule” (COM) production in the ices and increase the MF:GA ratio, but extreme values are destructive for gas-phase COMs. We speculate that the high densities required for extreme MF:GA ratios could be evidence of COM emission dominated by COMs desorbing within a circumstellar disk.

Numerical and experimental investigation of the effect of interstitial liquid viscosity on the collapse of wet granular columns

The collapse of wet granular columns with high- and low-viscosity interstitial liquid is investigated through experiment and numerical simulations. By incorporating both capillary and viscous force models of interstitial liquid in the Discrete Element Method (DEM), simulations have been conducted and reproduced consistent collapse processes with the experiment. The influence of water content, contact angle, particle diameter, liquid surface tension, and viscosity is explored over a large parameter space using DEM. For wet granular columns with low-viscosity interstitial liquid of water, the final profile of the collapsed column can be predicted by Bond number (Bo), a ratio of particle gravity to capillary force. For wet granular columns where inertial effect dominates and both capillary and viscous forces are significant, dimensional analysis is employed to characterize the collapse, indicating that the collapsed profile can be predicted by Bo and Galileo number (Ga). Using water and silicone oils with different viscosities as interstitial liquids, simulations show that the collapsed profile depends on Bo with a low capillary number (Ca ∼ 10−3), Bo and Ga when Ca is intermediate (Ca ∼ 10−1). When Ca is high (Ca > 100), the final profile is solely controlled by Bo and a rolling-collapsed regime is observed.

Predicting the firing behavior of neural network through the bifurcation analysis of derivative mean-field model

The mean-field model is an important method for understanding the complex dynamics of the nervous system at different spatial levels and for simulating and theoretically analyzing the collective dynamic behaviour of large neural populations. In the work, we construct an improved mean-field system of neural networks coupled with quadratic integrate-and-fire neurons and examine the discharge patterns for networks by analyzing such a model, which is a three-dimensional smooth differential system. Bifurcation analysis of the mean-field model is conducted from both theoretical and simulation perspectives, we obtain the bifurcation conditions of some co-dimension-two bifurcations and divide the parameter space into different regimes by simulating two parameters bifurcation diagrams. We find a close correspondence, though with some variance, between the mean-field model and neural network when comparing the firing patterns of the two models in various parameter regimes. In summary, the obtained mean-filed description builds the bridge between the parameters of neurons or networks and that of a mean-field system to ensure we can compare them and understand the connections between them. Specifically, the mean-field model can reflect the dynamics of neural networks from a macroscopic perspective, and its bifurcation can predict the behaviour of neural networks to a certain extent and understand the mechanisms behind them, such as bursting dynamics of neural networks.

Ensemble surrogate modeling of advective-dispersive transport with intraparticle diffusion model for column-leaching test

Column-leaching tests are a common approach for assessing the leaching behavior and resulting environmental risks of contaminated soils and waste materials, which are frequently reused for various construction purposes. The observed breakthrough curves of the contaminants are influenced by the complex dynamics of solute transport and kinetic inter-phase mass transfer. Disentangling these interactions necessitates numerical models. However, inverse modeling and sensitivity analysis can be time-consuming, especially when sorption kinetics are explicitly described by intraparticle diffusion, which requires discretizing the domain both in the flow direction along the column axis and inside the grains. To circumvent the need for such computationally intensive models, we have developed two different ensemble surrogate models. These models employ two separate ensemble methods: random forest stacking and inverse-distance weighted interpolation. Each method is applied to base surrogate models that cover different parts of the parameter space. The base surrogate models use the method of Extremely randomized Trees (ExtraTrees). The defined parameter range is based on the German standard for column-leaching tests. To optimize the base surrogate models, we utilized adaptive-sampling methods based on three distinct infill criteria: maximizing the expected improvement, staying within a certain Mahalanobis distance to the best estimate (both for exploitation), and maximizing the standard deviation (for exploration). The ensemble surrogate model demonstrates excellent performance in emulating the behavior of the original numerical model, with a relative root mean squared error of 0.09. We applied our proposed ensemble surrogate model to estimate the complete posterior parameter distribution using Simulation-Based Inference, specifically Neural Posterior Estimation, to determine the full parameter distribution conditioned on copper-leaching data from two different soils. Samples drawn from the posterior distribution align perfectly with the observed data for both the surrogate and original models.

Exploration toward a new stacking-pressure phase diagram in bilayer AA- and AB-MoS2

The phase diagram serves as a blueprint for designing the structure of a material, offering a comprehensive representation of its different phases under specific conditions, such as temperature and pressure. In the realm of two-dimensional (2D) materials, stacking order can play a crucial role in controlling and inducing phase transitions. However, in studying phase diagrams for 2D materials, the exploration of stacking degree of freedom has largely been overlooked, limiting our understanding and hindering future applications. Here, we experimentally explore the interplay of stacking and pressure degrees of freedom in revealing unique phase transitions in bilayer MoS2 with two different stacking configurations. In AA stacking, interlayer sliding and asymmetric intralayer compressing precede intralayer rotation, while in AB stacking, asymmetric intralayer compressing and intralayer distortion occur simultaneously. Under further elevated pressure, the bilayer system transitions into 1T′ phase before amorphization. Our findings offer valuable insights for creating comprehensive phase diagrams and exploring exotic phases as well as phase transitions of 2D materials in a broader parameter space.

Quasars as standard candles

A sample of quasars has been recently assembled to investigate the non-linear relation between their monochromatic luminosities at 2500 Å and 2 keV and to exploit quasars as a new class of ‘standardized candles’. The use of this technique for cosmological purposes relies on the non-evolution with redshift of the UV-optical spectral properties of quasars, as well as on the absence of possible contaminants such as dust extinction and host galaxy contribution. We address these possible issues by analysing the spectral properties of our cosmological quasar sample. We produced composite spectra in different bins of redshift and accretion parameters (black hole mass, bolometric luminosity), to investigate any possible evolution of the spectral properties of the continuum of the composites with these parameters. We found a remarkable similarity amongst the various stacked spectra. Apart from the well known evolution of the emission lines with luminosity (i.e. the Baldwin effect) and black hole mass (i.e. the virial relation), the overall shape of the continuum, produced by the accretion disc, does not show any statistically significant trend with black-hole mass (MBH), bolometric luminosity (Lbol), or redshift (z). The composite spectrum of our quasar sample is consistent with negligible levels of both intrinsic reddening (with a colour excess E(B − V)≲0.01) and host galaxy emission (less than 10%) in the optical. We tested whether unaccounted dust extinction could explain the discrepancy between our cosmographic fit of the Hubble–Lemaître diagram and the concordance ΛCDM model. The average colour excess required to solve the tension should increase with redshift up to unphysically high values (E(B − V)≃0.1 at z &gt; 3) that would imply that the intrinsic emission of quasars is much bluer and more luminous than ever reported in observed spectra. The similarity of quasar spectra across the parameter space excludes a significant evolution of the average continuum properties with any of the explored parameters, confirming the reliability of our sample for cosmological applications. Lastly, dust reddening cannot account for the observed tension between the Hubble–Lemaître diagram of quasars and the ΛCDM model.

Thermal dark matter with low-temperature reheating

We explore the production of thermal dark matter (DM) candidates (WIMPs, SIMPs, ELDERs and Cannibals) during cosmic reheating. Assuming a general parametrization for the scaling of the inflaton energy density and the standard model (SM) temperature, we study the requirements for kinetic and chemical DM freeze-out in a model-independent way. For each of the mechanisms, up to two solutions that fit the entire observed DM relic density exist, for a given reheating scenario and DM mass. As an example, we assume a simple particle physics model in which DM interacts with itself and with SM through contact interactions. We find that low-temperature reheating can accommodate a wider range of couplings and larger masses than those permitted in the usual instantaneous high-temperature reheating. This results in DM solutions for WIMPs reaching masses as high as 1014 GeV, whereas for SIMPs and ELDERs, we can reach masses of 1013 GeV. Interestingly, current experimental data already constrain the enlarged parameter space of these models with low-reheating temperatures. Next-generation experiments could further probe these scenarios.

A latent space method with maximum entropy deep reinforcement learning for data assimilation

Data assimilation aims to calibrate the uncertain state or parameter vectors of a system by matching simulation results with observations, which is crucial for uncertainty quantification and optimization. Although traditional methods of data assimilation have shown promising results in practical applications, they need well-designed iteration rules (i.e., gradient, covariance, search strategies). Deep reinforcement learning (DRL) can solve data assimilation problems by trial and error, which does not require convoluted iteration rules. However, previous DRL methods cannot tackle high-dimensional data assimilation problems efficiently. In this work, we propose a latent space method with maximum entropy DRL for data assimilation to extend DRL to complicated systems with lots of parameters. Solutions can be found in a reduced space, through the interaction between the agent represented by artificial neural networks and the environment of numerical simulation. The proposed method contains two key points. First, to make the method applicable to high-dimensional problems, we construct a latent space method by integrating a dimensionality reduction method with the state, action, and reward settings for the agent. Second, a maximum entropy DRL algorithm, Soft Actor–Critic (SAC), was employed to efficiently explore the parameter space. The proposed method overcomes the limitation that previous DRL algorithms are only suitable for small-scale cases, and enables DRL to deal with medium-scale or even large-scale data assimilation problems. The performance of the proposed method was validated by comparison with a deep reinforcement learning algorithm and traditional data assimilation algorithms on 2D synthetic and 3D reservoir cases.

Mardigras: A Visualization Tool of Theoretical Mass–Radius Relations in the Context of Planetary Science

Over the past two decades, mass–radius relations have become a crucial tool for inferring the bulk composition of exoplanets using only their measured masses and radii. These relations, often referred to as isocomposition curves, are derived from interior structure models by calculating the theoretical radius as a function of mass for a given fixed planetary composition. Each mass–radius curve can be influenced by a variety of parameters, such as planetary composition, age, and equilibrium temperature. Navigating this parameter space can be cumbersome, particularly when models or their results are not open-source. To address this challenge, I have developed MAss–Radius DIaGRAm with Sliders, a visualization tool that enables simple, fast, and interactive exploration of the parameter space that governs mass–radius relations for any given model.

Autosomal suppression of sex-ratio meiotic drive influences the dynamics of X and Y chromosome coevolution.

Sex-ratio meiotic drivers are selfish genes or gene complexes that bias the transmission of sex chromosomes resulting in skewed sex ratios. Existing theoretical models have suggested the maintenance of a four-chromosome equilibrium (with driving and standard X and suppressing and susceptible Y) in a cyclic dynamic, studies of natural populations have failed to capture this pattern. Although there are several plausible explanations for this lack of cycling, interference from autosomal suppressors has not been studied using a theoretical population genetic framework even though autosomal suppressors and Y-linked suppressors coexist in natural populations of some species. In this study, we use a simulation-based approach to investigate the influence of autosomal suppressors on the cycling of sex chromosomes. Our findings demonstrate that the presence of an autosomal suppressor can hinder the invasion of a Y-linked suppressor under some parameter space, thereby impeding the cyclic dynamics, or even the invasion of Y-linked suppression. Even when a Y-linked suppressor invades, the presence of an autosomal suppressor can prevent cycling. Our study demonstrates the potential role of autosomal suppressors in preventing sex chromosome cycling and provides insights into the conditions and consequences of maintaining both Y-linked and autosomal suppressors.

Seeking a better path for the circular economy of solar panels: Global sensitivity analysis focused on socioeconomic and physical factors

As more solar photovoltaic panels are expected to be introduced globally to promote the stabilization and decarbonization of electricity supply, concerns have been raised about the associated resource constraints and the environmental impacts. To promote circular economy initiatives, this study aims to provide insight into the variables and the conditions that play a key role in the expression of environmental impacts and find the better path for a circular economy of solar panels, which requires analyzing impacts on multiple indicators as a result of various factors and their cross-interactions. To achieve this goal, we constructed a resource circulation simulator that considers factors related to the business environment as well as physical factors. The simulation model was then applied to the largest administrative region in Japan, and the parameter space was explored by combining Regional Sensitivity Analysis, a global sensitivity analysis method, and Random Forest to consider the interactions among these variables. As a result of 30,000 Monte Carlo simulations performed on 26 input variables, it was found that the key parameters in final disposal and resource consumption potential differ between the two purposes; avoiding catastrophic loads and achieving ambitious reductions. In contrast, for greenhouse gas emissions, it is important to maintain or increase the efficiency of photovoltaic panel power generation for both purposes. Additionally, in the case where the key variables were optimized in terms of final disposal/resource consumption potential, it will effectively reduce the two impacts to 0.7 % and 12.6 %, respectively, with a minimal negative impact on greenhouse gas emissions. In contrast, in the case when optimized in terms of greenhouse gas emissions, it has only a small impact on greenhouse gas emissions and large negative impacts on final disposal and resource consumption potential. Further refinement of the parameter space and thresholds in the analysis is a topic for future research.

Oscillatory one-roll and two-roll solutions in laminar viscoelastic Rayleigh-Bénard convection in a square cavity

Rayleigh-Bénard convection in square closed cavities filled with Oldroyd-B fluid was studied using OpenFOAM-based RheoTool. For the RBC in Newtonian fluids, the transition always occurs from conduction to steady state convection with increasing Rayleigh number (Ra). On the other hand, the viscoelastic fluids may also show the transition from conduction to oscillatory convection. Further increase in Ra may result in a steady state convective solutions. It is further noted that the behavior is similar to Newtonian fluids for larger values of viscosity ratio (B). Considering the abovementioned different flow behavior at different values of the parameters, it is noted that there are five different types of solutions possible for the viscoelastic fluids viz. pure conduction (PC), one roll periodic oscillations (ORPO), one roll steady state (ORSS) convection, two roll periodic oscillations (TRPO), simultaneous one and two roll steady state convection. Therefore, a bifurcation diagram in the parametric space of Ra and B is presented, depicting these five regions corresponding to each type of solution. The boundaries of these regions have been identified by numerical simulation. Note that all these regions exist in the laminar flow regime, and the transition to turbulence is not considered here. Interestingly, at low values of B, as one increases Ra, it is seen that the ORSS region is sandwiched between ORPO and TRPO. The likely reason for this interesting behavior is explained. Moreover, representative solutions in each region in terms of isotherms, streamlines, and vector plots have been included to demonstrate the dynamics of each delineated region.