Monte Carlo Simulation Approach Research Articles

Quantifying aquifer heterogeneity using superparamagnetic DNA particles

Identifying and determining hydraulic parameters of physically heterogeneous aquifers is pivotal for flow field analysis, contaminant migration and risk assessment. In this research, we applied a novel uniquely sequenced DNA tagged superparamagnetic silica microparticles (SiDNAmag) to quantify hydraulic parameters and associated uncertainties of a heterogeneous sand tank. In the sand tank with lens shaped heterogeneity, we conducted three sets of multi – point injection experiments in unconsolidated (1) homogeneous (zone 0), (2) heterogeneous with a no-conductivity-zone (zone 1), and (3) heterogeneous with a high-conductive-zone (zone 2). From the breakthrough curves (BTC), we estimated the parameters distributions of hydraulic conductivity (k), effective porosity (ne), longitudinal dispersivity (αL), transverse vertical (αTV), and transverse horizontal dispersivities (αTH) applying Monte Carlo simulation approach for BTC fitting. The estimated parameters and associated uncertainties for each of the heterogeneous sections were further statistically compared (distribution non-specific Mann Whitney U test) these parameter distributions with parameter distributions estimated from the conservative salt tracer. While the time of arrival and time to peak concentration of SiDNAmag and salt in effluent were comparable, peak concentration of SiDNAmag was 1–3 log reduced as compared to the salt tracer due to first order kinetic attachment. Nonetheless, the parameters and associated uncertainty distributions (5 %–95 %) of K, ne, αL, αTV, and αTH, determined from SiDNAmag BTCs were statistically equivalent to the salt tracer in all three experiment systems. Through our experimental and modelling approach, our work demonstrated that in a coarse to very coarse grain sand medium, with lens shaped heterogeneity, the uniquely sequenced SiDNAmag were a promising tool to identify heterogeneity and determine hydraulic parameters and associated uncertainty distributions.

Enhancement of reactive oxygen species production by ultra-short electron pulses

The development of laser-driven accelerators-on-chip has provided an opportunity to miniaturize devices for electron radiotherapy delivery. Laser-driven accelerators produce highly time-compressed electron pulses, on the 100 fs to 1 ps scale. This delivers electrons at high peak power yet low average beam current compared with conventional delivery devices, which generate pulses of approximately 3 µs. The biophysical effects of this time structure, however, are unclear. Here, we use a Monte Carlo simulation approach to explore the effects of the electron beam time structure on the production of reactive oxygen species (ROS) in water. Our results show a power law increase in the generation of hydroxyl ions per deposited electron with decreasing pulse length over the pulse length range of 10 µs to 100 fs. Similar trends were observed for hydrogen peroxide, superoxide, hydroperoxyl, hydronium and solvated electrons. In practical terms, this indicates a fourfold increase in the efficiency of free radical production for sub-picosecond pulses, relative to that of conventional microsecond pulses, for the same number of deposited electrons.

Cost-effective reliability level in 100% renewables-based standalone microgrids considering investment and expected energy not served costs

Loss of load probability (LOLP) and expected energy not served (EENS) are commonly used in electrical power systems to evaluate reliability. LOLP defined as the probability that available generation capacity will be inadequate to supply customer demand. EENS defined as the expected amount of energy not being served to consumers by the system during the period considered due to system capacity shortages or unexpected power outages. Loss of Load Frequency (LOLF) is referred to a number of loss of load (LOL) event happened in the operation life span of the SMG. Loss of Load Reduction (LOLR) is defined as the required reduction in LOLF to obtain a specific reliability level. While power systems are designed to minimize LOLP and EENS, this is constrained by the total cost: investment cost, operation and maintenance cost, and cost of customer interruption (CCI). This research considers Standalone Microgrid (SMG), also known as Autonomous Microgrid which only operates in off-grid mode and cannot be connected to wider electrical power system. When designing a 100 % renewable energy integrated SMGs, it is crucial to determine the cost-effective reliability level (CERL). The CERL occurs when the total cost is minimum. This research proposes an approach to calculate the CERL for a fully renewable SMG. An analytical formulation is proposed to represent the LOLR needed to obtain a specific reliability level as a function of the required size of reliability improvement alternatives. The CCI is evaluated using LOLF and EENS indices. Finally, the total cost of the SMG system is evaluated for each reliability level. Consequently, the total cost of the SMG system is expressed as a function of reliability levels, and the minimum value of total cost and the corresponding reliability level are evaluated. In this research, a Monte Carlo Simulation (MCS) approach is used to find hourly LOLF, considering 25 years (219,000 h) of SMG lifespan, regression analysis is used for an analytical formulation, and mixed integer linear programming (MILP) is used for the investment decision making based on a cost minimisation approach. The result demonstrates that the CERL of the SMG system evaluated in the case study is 98.71 %.

A simplified method for theoretical sum frequency generation spectroscopy calculation and interpretation: The "pop model".

Existing methods to compute theoretical spectra are restricted to the use of time-correlation functions evaluated from accurate atomistic molecular dynamics simulations, often at the abinitio level. The molecular interpretation of the computed spectra requires additional steps to deconvolve the spectroscopic contributions from local water and surface structural populations at the interface. The lack of a standard procedure to do this often hampers rationalization. To overcome these challenges, we rewrite the equations for spectra calculation into a sum of partial contributions from interfacial populations, weighted by their abundance at the interface. We show that SFG signatures from each population can be parameterized into a minimum dataset of reference partial spectra. Accurate spectra can then be predicted by just evaluating the statistics of interfacial populations, which can be done even with force field simulations as well as with analytic models. This approach broadens the range of simulation techniques from which theoretical spectra can be calculated, opening toward non-atomistic and Monte Carlo simulation approaches. Most notably, it allows constructing accurate theoretical spectra for interfacial conditions that cannot even be simulated, as we demonstrate for the pH-dependent SFG spectra of silica/water interfaces.

Study on Photon Beam Imaging Properties of CsPbBr3-PP Scintillator: Monte Carlo Simulation Approach

Perovskite scintillators have garnered significant interest in the realm of gamma-ray imaging in the past few years. Here, a comprehensive investigation into the gamma-ray imaging properties of CsPbBr3-PP composite scintillators is presented, wherein the Monte Carlo simulation approach offered by Geant4 is utilized. The primary focus is on the point spread function and modulation transfer function of the material, elucidating the nuanced interactions between gamma-ray energy, scintillator thickness, and their resultant imaging capabilities. A key aspect of this study is the exploration of the nonlinear and inverse effect of scintillator thickness and the energy of the photon beam on the imaging quality, highlighting the trade-offs between energy deposition and image resolution. This research underscores the importance of optimizing the scintillator design to balance these factors, catering to specific applications in high-energy detection and imaging. This work not only contributes significantly to the field of material sciences and radiographic imaging, but also provides practical insights for the development of more effective scintillator-based detectors. The findings of this study have broad implications for the design and application of perovskite scintillators in various high-tech industries and scientific research.

The effects of mechanical imperfections on the stability behavior of double-layer Scallop domes

Mechanical imperfections are almost inevitable in large-span spatial domes with hundreds of members and joints. The lack of fit in members is the most common form of imperfections in double-layer domes which influences the behavior of these structures under gravitational or seismic loads. In this study, the probabilistic effect of mechanical imperfections (lack of fit) on the stability behavior of double-layer Scallop domes with sinusoidal arching style under symmetric and asymmetric snow-loading is investigated. On some double-layer scallop domes, nonlinear static and dynamic analyses are carried out using Abaqus/CAE and MATLAB. The behavior of the structures with randomly distributed imperfect members is compared with those of the ideally perfect structures. A Monte Carlo simulation approach is used to statistically assess the effects of uncertainties on the response parameters and the ultimate capacity of the structures. In the first approach, named here as the “rigorous probabilistic approach”, the amount and the distribution of imperfect members are all treated as random variables, and in the second approach, named as “concise approach”, a fixed amount of imperfections are applied to a number of randomly selected critical members. No simplification method is used in the rigorous probabilistic approach to achieve a reliable assessment of the effect of uncertain parameters. Results show that the collapse behavior of double-layer Scallop domes is sensitive to random imperfections and a considerable decrease is seen in their load-bearing capacity.

P-graph and Monte Carlo simulation approach for sustainable and risk-managed CDR portfolios

Negative emission technologies (NETs) that perform carbon dioxide removal (CDR) are now part of the decarbonization strategies to reach net-zero emissions. However, NETs will consume resources that will compete with other societal priorities. Thus, NET portfolios that provide a technology mix are better for sustainability and risk management. This study proposes a two-step approach to optimize and check the robustness of NET portfolios, particularly for industrial-scale applications where resource availability fluctuates due to variations in energy, water, and fertilizer supply. The first step involves the process graph (P-graph) framework to generate optimal and near-optimal solution structures at minimum cost. The second step measures the probability of failure of these solutions against resource availability variations through Monte Carlo simulation. By comparing the cost and probability of failure, decision-makers can select a recommended solution that strikes a balance between robustness and cost. The proposed approach is demonstrated in two case studies involving NET portfolios. The first case study investigates the performance of optimal and near-optimal solutions of NET portfolios, while the second case study focuses on bioenergy with carbon capture and storage portfolios using different feedstocks. In both cases, the results show that near-optimal solutions with higher costs but lower probabilities of failure exist, enabling decision-makers to weigh robustness against cost. This study contributes to developing and analyzing robust decarbonization decision support models, addressing the critical need for sustainable and risk-managed NET portfolios.

Empirical Monte Carlo evidence on estimation of timing-of-events models

This article builds on the Empirical Monte Carlo simulation approach to study the estimation of Timing-of-Events (ToE) models. We exploit rich Swedish data of unemployed job seekers with information on participation in a training program to simulate placebo treatment durations. We first use these simulations to examine which covariates are key confounders to be included in dynamic selection models for training participation. The joint inclusion of specific short-term employment history indicators (notably, the share of time spent in employment), together with baseline socio-economic characteristics, regional and inflow timing information, is important to deal with selection bias. Next, we omit subsets of explanatory variables and estimate ToE models with discrete distributions for the ensuing systematic unobserved heterogeneity. In many cases, the ToE approach provides accurate effect estimates, especially if time-varying variation in the unemployment rate of the local labor market is taken into account. However, assuming too many or too few support points for unobserved heterogeneity may lead to large biases. Information criteria, in particular those penalizing parameter abundance, are useful to select the number of support points. A comparison with other duration models shows that a Stratified Cox model performs well with abundant multiple spells but less well when multiple spells are uncommon. The standard Cox regression model performs poorly in all configurations as it is unable to account for unobserved heterogeneity.

Optimizing number of Raman spectra using an artificial neural network guided Monte Carlo simulation approach to analyze human cortical bone

This study presents a novel methodology for optimizing the number of Raman spectra required per sample for human bone compositional analysis. The methodology integrates Artificial Neural Network (ANN) and Monte Carlo Simulation (MCS). We demonstrate the robustness of ANN in enabling prediction of Raman spectroscopy-based bone quality properties even with limited spectral inputs. The ANN algorithms tailored to individual sex and age groups, which enhance the specificity and accuracy of predictions in bone quality properties. In addition, ANN guided MCS systematically explores the variability and uncertainty inherent in different sample sizes and spectral datasets, leading to the identification of an optimal number of spectra per sample for characterizing human bone tissues. The findings suggest that as low as 2 spectra are sufficient for biochemical analysis of bone, with R2 values between real and predicted values of v1/PO4/Amide I and ∼I1670/I1640 ratios, ranging from 0.60 to 0.89. Our results also suggest that up to 8 spectra could be optimal when balancing other factors. This optimized approach streamlines experimental workflows, reduces data and acquisition costs. Additionally, our study highlights the potential for advancing Raman spectroscopy in bone research through the innovative integration of ANN-guided probabilistic modeling techniques. This research could significantly contribute to the broader landscape of bone quality analyses by establishing a precedent for optimizing the number of Raman spectra with sophisticated computational tools. It also sets a novel platform for future optimization studies in Raman spectroscopy applications in biomedical field.

Individualized dose calculation for internal exposure on radionuclide intake: GPU acceleration approach

Objective. The rapid and accurate assessment of internal exposure dose is a crucial safeguard for personnel health and safety. This study aims to investigate a precise and efficient GPU Monte Carlo simulation approach for internal exposure dose calculation. It directly calculates doses from common radioactive nuclides intake, like 60Co for occupational exposure, allowing personalized assessments. Approach. This study developed a GPU-accelerated Monte Carlo program for internal exposure on radionuclide intake, successfully realizing photoelectronic coupled transport, nuclide simulation, and optimized acceleration. The generation of internal irradiation sources and sampling methods were achieved, along with the establishment of a personalized phantom construction process. Three irradiation scenarios were simulated to assess computational accuracy and efficiency, and to investigate the influence of posture variations on internal dose estimations. Main results. Using the International Commission on Radiological Protection (ICRP) voxel-type phantom, the internal dose of radionuclides in individual organs was calculated, exhibiting relative deviation of less than 3% in comparison to organ dose results interpolated by Specific Absorbed Fractions in ICRP Publication 133. Employing the Chinese reference phantom for calculating internal irradiation dose from the intake of various radionuclides, the use of GPU Monte Carlo program significantly shortened the simulation time compared to using CPU programs, by a factor of 150–500. Internal dose estimation utilizing a seated Chinese phantom revealed up to a 75% maximum difference in organ dose compared to the same phantom in a standing posture. Significance. This study presents a rapid GPU-based simulation method for internal irradiation doses, capable of directly simulating dose outcomes from nuclide intake and accommodating individualized phantoms for more realistic and expeditious calculations tailored to specific internal irradiation scenarios. It provides an effective and feasible tool for precisely calculating internal irradiation doses in real-world scenarios.

Analysis of Determining Order Quantity of Spare Parts in Oil and Gas Well Service Activities Using Monte Carlo Simulation

This research aims to explore the analysis of determining the order quantity of spare parts in well service activities in the oil and gas (Migas) industry using the Monte Carlo simulation approach. The Migas industry requires efficient inventory management to maintain operational smoothness, but this need must be balanced with efforts to minimize excessive storage costs. Therefore, it is important to determine the optimal order quantity to meet operational needs while avoiding unnecessary inventory accumulation. The Monte Carlo simulation method was chosen because of its ability to address uncertainty in inventory analysis, allowing for modeling variations in demand, delivery lead times, and other factors affecting inventory management. It is expected that this approach will provide a better understanding of how decisions regarding the order quantity can be made more effectively in the context of well service activities in the Migas industry. The main expectation of this research is the development of a model that can assist Migas companies in optimizing the management of their spare parts inventory. Thus, it is hoped that this research will make a significant contribution to improving operational efficiency, reducing storage costs, and increasing customer service levels in the Migas industry. It is also hoped that the results of this research will pave the way for the development of inventory management strategies that are more adaptive and responsive to dynamic business environments.

Evaluating the Kerma coefficients relevant to various water-equivalent materials at different photon energies

To investigate the water equivalency of some solid phantoms, relevant Kerma coefficients were determined through both Monte Carlo (MC) simulation and analytical approaches over a wide range of photon energies ranging from 1 keV to 20 MeV.MCNPX MC Code was employed to simulate the Kerma coefficients for A-150, PMMA, Polystyrene, Solid Water (RMI 457), Solid Phantom (RW3), Virtual water, Solid Water (WT1), and Water Equivalent (Wte) solid phantoms. Kerma coefficients were also theoretically calculated using the mass energy absorption coefficients at different photon energies ranging from 1 keV to 20 MeV. Obtained Kerma coefficients for considered solid phantoms were also compared with those of water to find which material shows the best water-equivalency.Calculated Kerma coefficients by MC simulation were in a good agreement with those calculated via an analytical method which no statistically significant differences were observed among simulated and analytically calculated Kerma coefficients (p-value>0.05). Solid water (RMI 457) showed the best water-equivalency among studied plastic phantoms for radiology and radiotherapy quality assurance purposes. From the results, the Kerma coefficients for solid phantoms are highly dependent on photon energy.

Health Risk Assessment of Exposure to Heavy Metals in Wheat Flour from Iran Markets: Application of Monte Carlo Simulation Approach.

The significance of flour in the Iranian diet underscores the need to ensure its safety from chemical pollutants. This study aimed to evaluate the potential health risks posed by certain heavy metals, such as Fe, Zn, Cu, Al, Co, Hg, Cr, Ni, Pb, and Cd, in wheat flour available in the Iranian market. A total of 248 flour samples were collected from 11 provinces in Iran during the winter of 2021. The health risks associated with heavy metals in children and adults were evaluated using USEPA health risk assessment guidance for superfund part A and Monte Carlo Simulation. The average concentration of Fe, Zn, Cu, Al, Co, Hg, Cr, Ni, Pb, and Cd, was equal to 30.62 ± 59.24, 4.94 ± 13.64, 1.24 ± 3.08, 2.85 ± 4.98, 0.03 ± 0.01, 0.12 ± 0.03, 1.42 ± 1, 0.23 ± 0.05, 1.71 ± 0.65, and 0.02 ± 0.004mg/kg dry weight, respectively. Analysis of Fe, Cr, Al, Hg and Cr in all flour samples showed that the average concentration of these metals were greater than the standards levels set by the WHO/FAO. The results of the non-carcinogenic risks (HI) showed that the hazard index values (children: 0.969 ± 1.04, adult: 0.837 ± 0.905) of heavy metals through the consumption of flour to both study population were acceptable. The results of the carcinogenic risks (CR) based on Cd, and Pb concentration showed that the CR values from ingestion of flour to the children and adults population were 1.45 × 10-5 ± 5.08 × 10-5 and 1.26 × 10-5 ± 4.40 × 10-5, respectively. The results of Monte Carlo simulation showed that conventional deterministic health risk evaluation could overestimate risk outcomes. Likewise, Cr has 68.8% and 69.1% probability of non-carcinogenic risk to children and adult, respectively, and 80% and 79.8% probability of CR for adults and children respectively for Pb, suggesting that Cr and Pb is a priority control heavy metals. Therefore, it is recommended to continuously monitor the levels of heavy metals in wheat and its derived food products to ensure food safety.

Configuration rearrangement of squalane induced by carbon dioxide dissolution: A molecular simulation study

Squalane has emerged as a desirable absorbent material for carbon capture, utilization, and storage solutions due to its tunable sorption properties, microscopic structures, and cost-effectiveness in response to the increasing demand. Moreover, a thorough understanding of the fundamental characteristics of hydrocarbons and dissolved gases involved in the Fischer–Tropsch industrial synthesis processes is also essential for researching industrial purification and separation. However, these macromolecules may experience varying degrees of induced alterations in their structural rearrangement when absorbing absorbates, which impacts the performance indicators of squalane. This study examined the solubility characteristics of absorbents made from squalane and their interaction with gaseous CO2 molecules. The perturbed chain statistical associated fluid theory (PC–SAFT) was employed to comprehensively evaluate the macroscopic thermodynamic experimental density and solubility data. Simultaneously, the Monte Carlo (MC) simulation approach was utilized to investigate the rearrangement and swelling features of the squalane chain induced by the presence of CO2 gas in a broad spectrum of temperature and pressure conditions. This work indicated that elevated temperatures increased flexibility in the molecular structure of squalane, leading to a more pronounced spatial dispersion, while higher pressures may lead to spatial homogeneity and enhanced capacity for carrying additional CO2 molecules. The examples and methodologies presented demonstrate the effectiveness of molecular simulations in comprehending absorption–based phenomena at an atomistic and molecular level and offer essential guidance in forecasting the potential for separation, storage, or catalytic.

Energy Filtering in Doping Modulated Nanoengineered Thermoelectric Materials: A Monte Carlo Simulation Approach.

Using Monte Carlo electronic transport simulations, coupled self-consistently with the Poisson equation for electrostatics, we explore the thermoelectric power factor of nanoengineered materials. These materials consist of alternating highly doped and intrinsic regions on the scale of several nanometers. This structure enables the creation of potential wells and barriers, implementing a mechanism for filtering carrier energy. Our study demonstrates that by carefully designing the nanostructure, we can significantly enhance its thermoelectric power factor compared to the original pristine material. Importantly, these enhancements stem not only from the energy filtering effect that boosts the Seebeck coefficient but also from the utilization of high-energy carriers within the wells and intrinsic barrier regions to maintain relatively high electronic conductivity. These findings can offer guidance for the design and optimization of new-generation thermoelectric materials through improvements in the power factor.

Decreasing Trend Bumped Probability Distribution: its Properties, Simulations and Application

In this paper, a probability model is proposed which engaged the mathematical combination of Lindley distribution and a trigonometric component known as haversine function〖 δ〗_s. The one parameter model prototype sustains the capacity to forecast multimodal decreasing trend sinusoidal outcomes. By nomenclature, the proposed probability model is called Modified Lindley Trigonometric Distribution (MLTD). Some statistical properties studied include the hazard function, mean residual life function, moments, conditional moments and moment generating function, Bonferroni and Lorenz curve, entropy, asymptotic distribution, order statistics, and parameter estimation; where the hazard function specially features a cyclic or periodic bathtub and inverted bathtub shape in chain format. The numerical behavior of the estimates of the average bias and mean square error were examined under Monte Carlo simulation approach; and an applicative simulation is experimented to underscore the parametric behavior of MLTD in data modeling. A real life flood data is used to illustrate the essence of the development.

A framework to analyse the probability of accidental hull girder failure considering advanced corrosion degradation for risk-based ship design

Ship’s hull girder failure could result from maritime accident that can cause human life loss, environmental disaster, and major economic impacts. In risk-based ship design paradigm, accounting for rare phenomena (e.g. ship-ship collision or grounding) is important to provide safe and durable structure. In-service corrosion-induced hull degradation should be considered at the design stage, as it can significantly affect structural strength. The current study presents a novel framework to estimate the probability of ship hull girder failure, accounting for novel corrosion modelling techniques and accidental damage. The associated uncertainties are considered using statistical sampling from evidence-based distributions. A state-of-the-art deterministic model for ultimate strength calculation is applied using Monte Carlo simulation approach, resulting in the probability of hull failure through a reliability assessment. Wave and still-water bending moments are considered random variables. Two case studies of tanker ships with varying sizes are executed to show the applicability of the proposed framework. The results indicate that proper consideration of corrosion is of high importance, as ageing can significantly increase the probability of failure if accidental damage happens. Therefore, whereas future research and model refinement are discussed, the presented framework can serve for risk-based ship design tool and assess existing structures’ safety.

Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters.

Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits.

Fuzzy control charts for individual observations to analyze variability in health monitoring processes

In healthcare monitoring, the quality of medical services and patient outcomes are significantly influenced by the unnatural variations emphasizing the importance of effective control and monitoring strategies. In such scenarios the quality is compromised when a shift in the medical process is not detected timely. In this study, we commenced a comprehensive analysis of the variability present in the patient's hematocrit levels. We introduced a novel approach using fuzzy control charts for individual measurements to detect shift within hematocrit levels effectively. For the initial forecasting of hematocrit level variability, we employed the exponential smoothing method by using R software. Following this, we proposed a set of fuzzy control charts intended for individual measurements, including the fuzzy moving average control chart, fuzzy weighted moving control chart, and fuzzy moving range control chart. These control charts are defined with fuzzy control rules, allowing them to analyze and interpret the small shifts in the process precisely. Also, to measure the process's capability for providing a deeper understanding of the processes, fuzzy process capability indices are introduced. A Monte Carlo simulation approach is employed to obtain the performance metrics. Finally, a case study sourced from Kaggle was conducted to evaluate the performance of the proposed fuzzy control charts for assessing hematocrit levels.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.