Single Set Of Parameters Research Articles

Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627

Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al. (2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1) the coarser spatial resolution of ORCHIDEE; (2) the higher temporal resolution of ORCHIDEE; and (3) the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.

A decomposition-based many-objective evolutionary algorithm with Q-learning guide weight vectors update

When dealing with regular, simple Pareto fronts (PFs), the decomposition-based multi-objective optimization algorithm (MOEA/D) performs well by presetting a set of uniformly distributed weight vectors. However, its performance declines when faced with complex and irregular PFs. Many algorithms address this problem by periodically adjusting the distribution of the weight vectors, but these methods do not take into account the performance of the population and are likely to update the weight vectors at the wrong time. In addition, for the SBX crossover operator, the setting of its distribution index will largely affect the exploration and convergence ability of the algorithm, so a single parameter setting will have negative impacts. To tackle these challenges, this paper proposes a method to simultaneously adaptively update weight vectors and optimize SBX parameter via Q-learning(RL-MaOEA/D). In order to make the strategies made by Q-learning more accurate, Two different metrics (CD and NCD) are proposed that capture diversity and convergence of individual and population respectively. RL-MaOEA/D is compared with seven state-of-the-art algorithms on different problems, and the simulation results reflect that the proposed algorithm has better performance.

Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications

Abstract For offshore wind turbines, an accurate description of the wave conditions is crucial for their design, construction, installation, maintenance, and operation. Typical sea states in the open ocean are often a combination of wind-sea and swell systems which cannot be fully described by a single set of parameters. In such cases the wave conditions are better defined by separating and quantifying the individual wave systems. In this paper, the wave age method has been revisited, and a modification has been proposed to add more functionality to better describe the wind/wave alignment of the wind-sea. A qualitative analysis was performed using offshore spectral energy data from a global wave model. The analysis was carried out for four different geographical locations: the Philippine Sea, the North Sea, the Atlantic Ocean, and the Pacific Ocean. The analysis compares the wind-sea and swell partitions separated by the standard and modified wave age methods. It shows an improved description of the wind-sea compared to the standard equation, especially in the case of high wind speed events and where the wind-sea partition is expected to dominate most of the spectral energy.

ESR-thermochronometry of the MIZ1 borehole, Tono, Japan

Previous studies have shown that the low-relief Tono region (Japan) underwent Quaternary exhumation at rates of <1 mm yr-1. Here we explore whether electron spin resonance (ESR) signals of quartz from six samples from the MIZ1 borehole can resolve such low rates of exhumation, or whether the samples are in thermal equilibrium with ambient borehole temperature. ESR thermochronometry requires that both sample-specific signal saturation and thermal decay are constrained in the laboratory, which makes measurements highly time-consuming. To overcome this, the development of a standardised growth curve (SGC) was explored, which allowed more rapid constraint of the trapped-charge concentrations of each of the samples. Thermal kinetic parameters were determined using an isothermal decay experiment for each individual sample and except for sample MIZ1-01, it was possible to fit all the isothermal decay data together to yield a single set of kinetic parameters that successfully described the dataset. Using a single set of kinetic parameters also allows faster measurement. The ESR thermochronometry results show that the MIZ1 samples are not in thermal equilibrium, but rather reflect ongoing exhumation in this region. Exhumation rates determined from the Al-centre are consistent with existing thermochronometric data and indicate total exhumation of 385 ± 220 m over the past 1 Myr. In contrast the different Ti-centre options (A, B, D) yield a total exhumation of ∼1 km over the same period. The cause of this discrepancy is unclear but may relate to sub-linearity of Ti-centre dose response, that lead to underestimation of the trapped-charge concentration and hence an overestimation of exhumation rates. Our results indicate that ESR-thermochronometry can be successfully applied in low-relief zones undergoing exhumation at rates <1 mm yr-1, and that an SGC and common thermal kinetic parameters may be used to expediate sample measurements.

Analysis of Liquefaction in Tailings Deposits by Fem Modeling of Undrained Cyclic Triaxial

In this article, a numerical calibration procedure for undrained cyclic triaxial tests is presented to evaluate the liquefaction potential in sand and silt samples from mining tailings in northern Chile. The numerical modeling of an axisymmetric specimen involved two stages: isotropic consolidation using the Hardening Soil Small (HSS) model and a cycling phase employing the UBC3D-PLM model to simulate the onset of liquefaction using the criterion that the excess pore pressure ratio Ru should exceed 0.8. The results demonstrate that the UBC3D-PLM modeling calibrated with experimental data from cyclic triaxial tests effectively represents the excess pore pressure in both sandy and silty soils from mining tailings. The accuracy of the modeling decreases when a single set of parameters is applied to the same soil at different cyclic stress ratios (CSR), highlighting the need for specific calibrations for each loading.

A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world.

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Individuals with anxiety and depression use atypical decision strategies in an uncertain world

Previous studies on reinforcement learning have identified three prominent phenomena: (1) individuals with anxiety or depression exhibit a reduced learning rate compared to healthy subjects; (2) learning rates may increase or decrease in environments with rapidly changing (i.e. volatile) or stable feedback conditions, a phenomenon termed learning rate adaptation; and (3) reduced learning rate adaptation is associated with several psychiatric disorders. In other words, multiple learning rate parameters are needed to account for behavioral differences across participant populations and volatility contexts in this flexible learning rate (FLR) model. Here, we propose an alternative explanation, suggesting that behavioral variation across participant populations and volatile contexts arises from the use of mixed decision strategies. To test this hypothesis, we constructed a mixture-of-strategies (MOS) model and used it to analyze the behaviors of 54 healthy controls and 32 patients with anxiety and depression in volatile reversal learning tasks. Compared to the FLR model, the MOS model can reproduce the three classic phenomena by using a single set of strategy preference parameters without introducing any learning rate differences. In addition, the MOS model can successfully account for several novel behavioral patterns that cannot be explained by the FLR model. Preferences for different strategies also predict individual variations in symptom severity. These findings underscore the importance of considering mixed strategy use in human learning and decision-making and suggest atypical strategy preference as a potential mechanism for learning deficits in psychiatric disorders.

Evaluating and identifying changes in the rate of unplanned discharge among Department of Veterans Affairs (VA) facilities providing substance use disorder residential care

IntroductionQuality improvement (QI) methods play a critical role in ensuring that patients receive high-quality and timely care. Healthcare systems should use valid and reliable measures to inform QI efforts. Mental health settings including substance use disorder (SUD) residential programs have been slow to develop and incorporate quality measurement into routine practice. Unplanned discharge is of particular concern because this event is associated with harm including suicide. Healthcare systems require criteria that they can use to operationalize unplanned discharge as a quality measure in SUD residential programs. MethodsThe study included all discharges from the Department of Veterans Affairs (VA) residential SUD programs between 2018 and 2022. The study calculated crude and adjusted rates of irregular discharge. The study used the first two years of observation (2018–2019) in a logistic regression model to determine the parameter estimates for three important covariates, age, risk for homelessness, and principal diagnosis. The study tested permutations of bin size (N) and days (D) per bin to identify a single set of parameters to enable small and large facilities to have sufficient power to detect out-of-control processes (i.e., significant worsening or improvement in rates). Aligned with standard nomenclature, the study calculated the control limits based on three standard deviations (SD). Values that fell above or below three SD were statistically significant. ResultsThe cohort included 56 facilities (26,361 discharges). Irregular discharge was associated with younger age (18–40 years) and a principal diagnosis of a drug use disorder. Testing parameter values of 100 discharges (N) over 120 days (D) would yield enough power to detect modest relative changes to the irregular discharge rate for small and large facilities while testing frequently enough to make the evaluations temporally relevant. Because secular trends such as staff changes over time will impact results, the quality control method should allow for real-time feedback to those most proximal to the event. ConclusionsThe study created a set of parameters and a methodology that residential SUD programs can use to operationalize unplanned discharge locally. These data could assist programs in conducting QI work to address unplanned discharge and related harms.

Establishing a process route for additive manufacturing of NiCu-based Alloy 400: an alignment of gas atomization, laser powder bed fusion, and design of experiments

Alloy 400 is a corrosion-resistant, NiCu-based material which is used in numerous industrial applications, especially in marine environments and the high-temperature chemical industry. As conventional manufacturing limits geometrical complexity, additive manufacturing (AM) of the present alloy system promises great potential. For this purpose, a robust process chain, consisting of powder production via gas atomization and a design of experiment (DoE) approach for laser powder bed fusion (LPBF), was developed. With a narrow particle size distribution, powders were found to be spherical, flowable, consistent in chemical composition, and, hence, generally applicable to the LPBF process. Copper segregations at grain boundaries were clearly detected in powders. For printed parts instead, low-intensity micro-segregations at cell walls were discovered, being correlated with the iterative thermal stress applied to solidified melt-pool-near grains during layer-by-layer manufacturing. For the production of nearly defect-free LPBF structures, DoE suggested a single optimum parameter set instead of a broad energy density range. The latter key figure was found to be misleading in terms of part densities, making it an outdated tool in modern, software-based process parameter optimization. On the microscale, printed parts showed an orientation of melt pools along the build direction with a slight crystallographic [101] texture. Micro-dendritic structures were detected on the nanoscale being intersected by a high number of dislocations. Checked against hot-extruded reference material, the LPBF variant performed better in terms of strength while lacking in ductility, being attributed to a finer grain structure and residual porosity, respectively.

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. Results: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.

High-pressure phase equilibrium for carbon dioxide solubility with biofuels: Experimental and thermodynamic insights in 2,5-dimethylfuran and methyl levulinate

The solubility of carbon dioxide in two solvents, 2,5-dimethylfuran and methyl levulinate, were measured to high pressure, up to 9.1 MPa, at three different temperatures (283.15, 303.15 and 323.15) K. The new data were measured using both the isothermal synthetic technique and the variable volume synthetic method. Two methods were utilised to provide a level of data validation. The uncertainties in the measured data were critically estimated. The experimental phase equilibrium data were modeled using the Peng-Robinson equation of state and the Wong-Sandler mixing rule with a single set of binary interaction parameters for each system. The data indicated that the solubility of carbon dioxide in both 2,5-dimethylfuran and methyl levulinate at high pressures was relatively low, suggesting a low capacity of these biofuels to dissolve carbon dioxide at high pressure.

(Invited) Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth

We present a battery lifetime model that predicts Irreversible battery thickness change under constant pressure operation as the battery ages, along with capacity loss and resistance growth. These metrics are very important for predicting lithium-ion battery state of health (SOH) and remaining useful life due to their interdependence with battery packaging and safety. To design and operate battery storage systems safely, we must be able to predict and detect changes in all three SOH metrics over life accurately and simultaneously. Traditionally, these three SOH metrics have been modeled separately. However, we developed a single model that simultaneously predicts battery capacity, resistance, and expansion over its lifetime.In this work, battery degradation is modeled using the single particle model (SPM) framework, including mechanical damage in the electrodes, which results in loss of active material (LAM), and the side reactions for SEI growth and Li plating, which result in loss of lithium inventory (LLI). We introduce an equivalent stress and concentration dependence of stress to the mechanical damage model. Commonly used fatigue models assume symmetric cycles (zero-mean stress) [1]. To account for cycling conditions with different currents on charge and discharge, an equivalent stress that adjusts for non-zero mean stress is needed. The concentration dependency of strain and the resulting stress allows us to capture different degradation rates for cells cycled at different depths of discharge (DOD). Lithium plating at the separator interface is often described as a spatially non-uniform process using the pseudo-2D (P2D) model. This phenomenon can be captured in the SPM framework using a current-dependent dynamic term to account for spatial-nonuniformity of electrolyte dynamics at higher currents.Experimental data from cells cycling with periodic reference performance tests were used to tune the degradation model. In addition to the standard I, V, and T measurements, continuous measurements of the thickness change of the battery pouch cells were recorded. From this data, we extracted the capacity, electrode state of health (eSOH), resistance, and reversible and irreversible expansion. The test conditions were designed to excite different dominant degradation mechanisms (e.g. mechanical damage) by cycling the cells at different conditions (C-rate, temperature, preload pressure, and DOD). The tuning, which uses accelerated aging simulations [2] (to speed up the tuning process), results in a single set of parameters that predicts capacity loss, resistance growth, and irreversible thickness change as the battery ages, even for conditions that were not used for parameter tuning.The tuning is performed in two sequential steps. First, the parameters related to the electrochemical and mechanical degradation rates, which result in LLI and LAM, are tuned to match the extracted eSOH variables at each Reference Performance Test (RPT). The first tuning gives us the amount of plated Li, SEI growth, and mechanical damage in the particles. We then relate these quantities to irreversible expansion using a linear relationship in the case of SEI and mechanical damage and quadratic in the case of Li plating [3]. The same relationship is used for all cells, and this represents the 2nd tuning step. Our data set and calibration are unique in that the dominant degradation mode is the loss of lithium inventory due to the loss of active anode material. Most prior work on lifetime predictive models relied on SEI growth as the dominant degradation mode. Figure 2 shows the tuned model prediction of the three performance metrics over the cycle life of cells for various cycling conditions. The markers represent the measurement at each reference performance test, and the lines correspond to the model.

Influence of molecular parameters on the representativeness of interfacial properties of simple fluids.

New parameterizations for the Lennard-Jones 12/6 potential capable of reproducing the vapor pressure and surface tension with sufficient precision, but not the liquid-vapor equilibrium densities for the case of simple fluids that include Ar, Kr, Xe, Ne, and CH4 are presented in this work. These results are compared with those derived from the family of Mie(n, 6) potentials, which adequately reproduce the coexistence curve and the vapor pressure, leaving aside the surface tension. In addition, a detailed analysis is presented on different parameterizations and methodologies, which have been developed in recent decades to estimate the interfacial properties of interest here for simple fluids, such as argon, which is a molecule that is, in principle, "simple" to study but that clearly reveals the enormous discrepancy between the results reported in the literature throughout these years. These facts undoubtedly reveal one of the fundamental problems in the context of molecular thermodynamics of fluids: reproducing different thermodynamic properties with sufficient precision from a single set of free parameters for some interaction potential. In order to show the scope of the parameterizations presented for the Lennard-Jones model, they were successfully applied to the case of binary mixtures, which included Ar-Kr, Ar-CH4, and Xe-Kr. Finally, and with the aim of showing a possible solution to the problem posed in this research, results of the same interfacial properties above mentioned for argon and methane were presented in this work by using a set of molecular interactions, called ANC2s, whose flexibility allowed to reproduce the experimental evidence with just one parameterization. The results reported in this work were generated using molecular dynamics simulations.

Transfer learning relaxation, electronic structure and continuum model for twisted bilayer MoTe2

Large-scale moiré systems are extraordinarily sensitive, with even minute atomic shifts leading to significant changes in electronic structures. Here, we investigate the lattice relaxation effect on moiré band structures in twisted bilayer MoTe2 with two approaches: (a) large-scale plane-wave basis first principle calculation down to 2.88°, (b) transfer learning structure relaxation + local-basis first principles calculation down to 1.1°. We use two types of van der Waals corrections: the D2 method of Grimme and the density-dependent energy correction, and find that the density-dependent energy correction yields a continuous evolution of bandwidth with twist angles. Based on the above results. we develop a complete continuum model with a single set of parameters for a wide range of twist angles, and perform many-body simulations at ν = −1, −2/3, −1/3.

Temperature and strain rate-dependent compression properties of 3D-printed PLA: an experimental and modeling analysis

PurposeThis study aims to investigate the compression characteristics of the 3D-printed polylactic acid (PLA) samples at temperatures below the glass transition temperature (Tg) with varying strain rates and develop a thermo-mechanical viscoplastic constitutive model to predict the finite strain compression response using a single set of material parameters. Also, the micro-mechanical damage processes are linked to the global stress–strain response at varied strain rates and temperatures through scanning electron microscopy (SEM).Design/methodology/approachTg of PLA was determined using a dynamic mechanical analyzer. Compression experiments were conducted at strain rates of 2 × 10–3/s and 2 × 10–2/s at 25°C, 40°C and 50°C. The failure mechanisms were examined using SEM. A finite strain thermo-mechanical viscoplastic constitutive model was developed to analyze the deformations at the considered strain rates and temperatures.FindingsTg of PLA was determined as 55°C. While the yield and post-yield stresses drop with increasing temperature, their trend reverses with an increased strain rate. SEM imaging indicated plasticizing effects at higher temperatures, while filament fragmentation and twisting at higher strain rates were identified as the dominant failure mechanisms. Using a non-linear regression analysis to predict the experimental data, an overall R2 value of 0.98 was achieved between experimental and model prediction, implying the robustness of the model’s calibration.Originality/valueIn this study, a viscoplastic constitutive model was developed that considers the combined effect of temperature and strain rate for FDM-printed PLA experiencing extensive compression. Using appropriate temperature-dependent modulus and flow rate properties, a single set of model parameters predicted the rise in the gap between yield stress and degree of softening as strain rates and temperatures increased.

Safety and human health: The landscape of an effective UPLC-MS/MS method for the identification and quantification of N-Nitroso Hydrochlorothiazide impurity in Hydrochlorothiazide

N-nitrosamines (NSAs) and Nitrosamine Drug Substance Related Impurities (NDSRIs) are class of compounds that contains the nitroso functional group. These compounds are carcinogens and mutagens for both humans and animals. As a result, all global regulatory agenesis are requiring risk assessments of NSAs in each pharmaceutical product. The present research aims to provide a single pre-packaged mass spectrometry method parameter set, which includes Ultra performance liquid chromatography and a triple quadrupole mass spectrometer (UPLC-MS-MS) configuration technique for qualifying and quantifying NNHCT in HCT. The analysis was performed on a YMC ODS-A liquid chromatography column with an oven temperature of 35 °C. In this method, mobile phase was prepared by mixing 10 mM ammonium formate buffer and acetonitrile was mixed in the ratio of 60:40 (v/v) with an isocratic program and a flow rate of 0.8 mL/min. The mass detector was used along with electrospray ionization (ESI) Negative mode with multiple reaction monitoring (MRM). The method has been validated in accordance with the current regulatory guidelines and shown the ability to detect NNHCT with a LOQ of 0.01 ppm and LOD of 0.005 ppm.

DEVELOPMENT OF AN ALGORITHM FOR CALCULATING COMPONENT COMPOSITION TO IMPROVE THE DEVELOPMENT OF OFFSHORE OIL FIELDS IN THE NORTH CASPIAN BASIN

The paper presents an algorithm to reconstruct the component composition of the mixture based on the results of production modeling in the black oil format and a fixed set of tables with the results of phase equilibrium calculations in a given pressure range. The approach is based on analyzing the ratio of flow rates for each phase and the ratio between average reservoir pressure and saturation pressure. The algorithm takes into account the possibility of oil degassing when saturation pressure decreases, as well as gas breakthrough of gas caps to the bottoms of producing wells.Implementation of the method is based on a unified thermodynamic model of multilayer reservoir, built on the principles of a single set of HC mixture components and uniform parameters of the equation of state on a set of fluid samples for all modeling stages: reservoir model, model of the gathering system and site facilities. In addition to steady-state flow calculations performed together with reservoir modeling, the unified model is planned to be used for dynamic flow calculations in the corresponding simulator (well start/stop), which will allow solving production problems in the process of operation of wells with complex completions.The conversion algorithm is automated using modern high-level tools: Python and data automation components from MS Excel, which eliminates the need for manual data input at all stages and makes it possible to obtain the estimated component composition of produced fluid for any set of wells and for any time step within the forecasting horizon.The obtained compositions are used to verify the reproduction of the main controlled parameters (oil density and gas oil ratio) and can be used for subsequent calculations of fluid transportation and treatment systems.The implemented algorithm has been tested for stability at different ratios of gas and oil flow rates by wells and meets the practical requirements of engineering calculations — the error of oil density is not higher than 2 % and gas factor is not higher than 10 %.

Iteration-Free quantum approximate optimization algorithm using neural networks

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the optimization steps, which require many executions of the quantum circuit. Therefore, there is active research focusing on finding better initial circuit parameters, which would reduce the number of required iterations and hence the overall execution time. While existing methods for parameter initialization have shown great success, they often offer a single set of parameters for all problem instances. We propose a practical method that uses a simple, fully connected neural network that leverages previous executions of QAOA to find better initialization parameters tailored to a new given problem instance. We benchmark state-of-the-art initialization methods for solving the MaxCut problem of Erdős-Rényi graphs using QAOA and show that our method is consistently the fastest to converge while also yielding the best final result. Furthermore, the parameters predicted by the neural network are shown to match very well with the fully optimized parameters, to the extent that no iterative steps are required, thereby effectively realizing an iteration-free QAOA scheme.

Exploring the use of averaged thermal kinetic parameters in luminescence thermochronometry

In luminescence thermochronometry, the thermal stability of feldspar minerals is conventionally constrained from isothermal decay experiments. However, despite recent refinement of the measurement protocol, measurements take several days and are routinely done for each individual sample. Following that most other thermochronometric methods usually use only a single reference set of thermal kinetic parameters, and that recent studies on direct physical probing of feldspar sample properties have shown that trap depth and band-tail width are broadly similar despite large variations in chemical composition, we sought to optimise luminescence thermochronometry measurements by exploring whether a single set of thermal kinetic parameters can describe luminescence thermal decay in feldspar. We explored the effect of using averaged thermal kinetic parameters rather than sample-specific thermal kinetic parameters to model luminescence signal accumulation under different thermal conditions. A set of K- and Na-feldspar minerals extracted from all over the world were analysed after being measured with a multi-elevated temperature protocol, comprising four different IRSL signals at 50, 100, 150, and 225 °C. Comparisons were done between the thermal kinetic parameters of each IRSL signal depending on different variables such as geographic region, transect, lithology, or mineralogy of the analysed feldspar grains. Even though it is not possible to generalise the thermal kinetic parameters between IRSL signals measured at different temperatures, the variance between the thermal kinetic parameters of different samples measured at the same IRSL temperature is consistent with the uncertainties on the individual parameters (i.e., <2–10%), suggesting that averaged, rather than sample-specific values may be appropriate. We then explored the effect of using these averaged parameters to model luminescence signal accumulation under different synthetic and natural thermal conditions. For our dataset, results show minimal impact on the obtained cooling histories and exhumation rates. We therefore propose the use of averaged rather than sample-specific thermal kinetic parameters for rapid investigation of luminescence thermochronometry samples. Based on careful initial characterisation of a few samples to verify the validity of using averaged thermal kinetic parameters, this would reduce measurement times by ca. 50% (i.e., 3–4 days per sample), allowing higher resolution sampling and measurement.