Monte Carlo Simulations Research Articles

Development of a Coupled Depletion Perturbation Theory Methodology in Continuous-Energy Monte Carlo Depletion Simulations

This work develops a depletion perturbation theory methodology coupled with generalized perturbation theory to produce fully coupled nuclide number density sensitivity coefficients for cross sections, initial nuclide number densities, and decay constants. The method is implemented into OpenMC, a high-fidelity Monte Carlo simulation code, utilizing continuous-energy cross sections to produced high resolution sensitivity coefficients over the energy spectrum.

Design and optimization of a dedicated Faraday cup for UHDR proton dosimetry: Implementation in a UHDR irradiation station

We describe in detail an Ultra High Dose Rate (UHDR) setup on a 2 MV tandem accelerator for investigating the FLASH effect in radiotherapy. Addressing UHDR dosimetry challenges, we have developed a specialized Faraday Cup (FC) as a dose rate-independent solution. Utilizing Monte Carlo simulations, the geometry and materials used for the FC's design has been optimized for a 4 MeV proton beam. According to the simulations, the virtual error on the charge collection is negligible. This three-part UHDR setup also integrates a beam profiler to ensure the beam homogeneity and an electrostatic chopper to irradiate for precise short times (down to 10 μs), replicating the macro beam structure of typical clinical proton accelerator structures such as the S2C2 and C230 (IBA, Louvain-la-Neuve). The integrated setup ensures dose deposition with an error ≤ 5% for dose rate from 140 to 1000 Gy/s.

Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions.

The computational demand posed by applying multi-Slater determinant trials in phaseless auxiliary-field quantum Monte Carlo methods (MSD-AFQMC) is particularly significant for molecules exhibiting strong correlations. Here, we propose using direct-product wave functions as trials for MSD-AFQMC, aiming to reduce computational overhead by leveraging the compactness of multi-Slater determinant trials in direct-product form (DP-MSD). This efficiency arises when the active space can be divided into noncoupling subspaces, a condition we term "decomposable active space". By employing localized-active space self-consistent field wave functions as an example of such trials, we demonstrate our proposed approach across a range of molecular systems, each exhibiting varying degrees of complexity in their electronic structures. Our findings indicate that the compact DP-MSD trials can reduce computational costs substantially, by up to 36 times for the C2H6N4 molecule where the two double bonds between nitrogen N=N are clearly separated by a C-C single bond, while maintaining accuracy when active spaces are decomposable. In the case of larger systems such as the benzene dimer, characterized by weak coupling between the two monomers, we observed a decrease in computational cost compared to using a complete active space trial, yet we retained the same level of accuracy. However, for systems where these active subspaces strongly couple, a scenario we refer to as "strong subspace coupling", the method's accuracy decreases compared to that achieved with a complete active space approach. We anticipate that our method will be beneficial for systems with noncoupling to weakly coupling subspaces that require local multireference treatments.

Optimal energy storage system selection for future cost-effective green hydrogen production: Integrating techno-economic, risk-based decision making, and cost projections

This study conducts technical, economic, and safety analysis of a green hydrogen production system consisting of a 1000 kWp photovoltaic cell, 3 options of energy storage namely lead carbon (PbC), lithium-ion (Li-ion), and repurposed lithium-ion (2nd Life Li-ion) battery, and an electrolyzer. Firstly, the system is optimized to maximum hydrogen production by adjusting equipment capacity and capacity factors, informed by a 1-h resolution solar energy generation profile. This optimization yields insights into the system's yearly operational dynamics. Techno-economic analysis was conducted to estimate the levelized cost of hydrogen (LCOH) from 2020 to 2050, additionally, uncertainty analysis using Monte-Carlo simulation to estimate the range of uncertainty in LCOH projection from 2020 to 2050 was performed. Furthermore, a quantitative risk analysis, utilizing the process route index metric, evaluates the risk of battery explosions. Finally, this study employs multi-criteria decision-making to choose the best energy storage technology to produce green hydrogen from economic and safety factors. The result indicates that in 2020, the PbC battery emerged as the most cost-effective option. However, over time, both lithium batteries became the cheapest option with almost similar trends. In terms of explosion hazard, the analysis shows that the PbC battery is a safer option for stationary energy storage. When both aspects are considered, the preferred battery option is almost certainly PbC over the years, but in extreme cases where the decision-maker places significant emphasis on economic factors both Li-ion options may become preferable.

A deep-learning-based scatter correction with water equivalent path length map for digital radiography.

We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed U-Net model comprises two concatenated modules: one for generating a WEPL map and the other for predicting scatter using the WEPL map as auxiliary information. First, 3D CT images were used as numerical phantoms for training and validation, generating observed and scattered images by Monte Carlo simulation, and WEPL maps using Siddon's algorithm. Then, we optimised the model without overfitting. Next, we validated the proposed model's performance by comparing it with other DL models. The proposed model obtained scatter-corrected images with a peak signal-to-noise ratio of 44.24 ± 2.89dB and a structural similarity index measure of 0.9987 ± 0.0004, which were higher than other DL models. Finally, scatter fractions (SFs) were compared with other DL models using an actual phantom to confirm practicality. Among DL models, the proposed model showed the smallest deviation from measured SF values. Furthermore, using an actual radiograph containing an acrylic object, the contrast-to-noise ratio (CNR) of the proposed model and the anti-scatter grid were compared. The CNR of the images corrected using the proposed model are 16% and 82% higher than those of the raw and grid-applied images, respectively. The advantage of the proposed method is that no actual radiography system is required for collecting training dataset, as the dataset is created from CT images using Monte Carlo simulation.

Impact of the reconfigurable optical add-drop multiplexer architecture on the design of multi-band C+L+S optical networks

The main advantage of multi-band (MB) networks is to provide more capacity than C-band networks by using other unused bands like the L- and S-bands and, in this way, postpone the announced optical network capacity crunch. However, MB nodes have a more complex structure than C-band nodes, impacting their cost and enhancing their induced physical layer impairments (PLIs). The main goal of this paper is to analyze the impact of several MB node architectures (namely baseline, common-band and compact MB node architectures) on the total network capacity and total network cost-per-bit, using a routing, modulation format, and spectrum assignment (RMSA) network planning tool based on a Monte–Carlo simulation that also incorporates the impact of network PLIs. When the PLIs are neglected, the common-band architecture presents the lowest cost-per-bit compared to the remaining MB architectures, since only lower cost C-band components are used. However, with the PLIs impact, the common-band architecture leads to the lowest total network capacity and highest cost-per-bit due to additional noise coming from all-optical wavelength converters. In particular, the common-band total network capacity is less than half the baseline and compact total network capacities for a blocking probability of 1%, considering the best channel launch power. Also, the common-band cost-per-bit is almost twice the baseline and compact cost-per-bit due to the PLIs-induced degradation.

Statistical Analysis of the Inverse Power Zeghdoudi Model: Estimation, Simulation and Modeling to Engineering and Environmental Data

Abstract In this research, we investigate a brand-new two-parameter distribution as an extension of the
power Zeghdoudi distribution (PZD). Using the inverse transformation technique on the PZD, the
produced distribution is called the inverted PZD (IPZD). Its usefulness in producing symmetric and
asymmetric probability density functions makes it the perfect choice for lifetime phenomenon mod-
eling. It is also appropriate for a range of real data since the relevant hazard rate function has one of
the following shapes: increasing, decreasing, reverse j-shape or upside-down shape. Mode, quantiles,
moments, geometric mean, inverse moments, incomplete moments, distribution of order statistics,
Lorenz, Bonferroni, and Zenga curves are a few of the significant characteristics and aspects explored
in our study along with some graphical representations. Twelve effective estimating techniques are
used to determine the distribution parameters of the IPZD. These include the Kolmogorov, least
squares (LS), a maximum product of spacing, Anderson-Darling (AD), maximum likelihood, mini-
mum absolute spacing distance, right-tail AD, minimum absolute spacing-log distance, weighted LS,
left-tailed AD, Cram ́er-von Mises, AD left-tail second-order. A Monte Carlo simulation is used to
examine the effectiveness of the obtained estimates. The visual representation and numerical results
show that the maximum likelihood estimation strategy regularly beats the other methods in terms of
accuracy when estimating the relevant parameters. The usefulness of the recommended distribution
for modelling data is illustrated and displayed visually using two real data sets and comparisons with
other distributions.

Geographical distribution of nitrate pollution and its risk assessment using GIS and Monte Carlo simulation in drinking water in urban areas of Fars province-Iran during 2017-2021.

Pollution of water resources with nitrate is currently one of the major challenges at the global level. In order to make macro-policy decisions in water safety plans, it is necessary to carry out nitrate risk assessment in underground water, which has not been done in Fars province for all urban areas. In the current study, 9494 drinking water samples were collected in four seasons in 32 urban areas of Fars province in Iran, between 2017 and 2021 to investigate the non-carcinogenic health risk assessment. Geographical distribution maps of hazard quotient were drawn using geographical information system software. The results showed that the maximum amount of nitrate in water samples in 4% of the samples in 2021, 2.5% of the samples in 2020 and 3% of the samples in 2019 were more than the standard declared by World Health Organization guidelines (50mg/L). In these cases, the maximum amount of nitrate was reported between 82 and 123mg/L. The HQ values for infants did not exceed 1 in any year, but for children (44% ± 10.8), teenagers (10.8% ± 8.4), and adults (3.2% ± 1.7) exceeded 1 in cities, years, and seasons, indicating that three age groups in the studied area are at noticeably significant non-carcinogenic risk. The results of the Monte Carlo simulation showed that the average value of non-carcinogenic risk was less than 1 for all age groups. Moreover, the maximum HQ values (95%) were higher than 1 for both children and teenager, indicating a significant non-carcinogenic risk for the two age groups.

Bootstrap Confidence Intervals for the Poisson-Pranav Distribution Parameter with an Application to COVID-19 Data

The Poisson distribution is commonly applied in statistical modeling to represent the count of events, assuming that the events are independent and occur at a constant rate. This assumption, however, may not always hold true in real-life situations. The Poisson distribution may be inadequate if the underlying rate of occurrence is not constant. The mixed Poisson distribution is proposed to solve this limitation because it permits the rate parameter of the Poisson distribution to be random rather than fixed. The Poisson-Pranav distribution, which is classified as a type of mixed Poisson distribution, has been commonly utilized to analyze count data that exhibits over-dispersion over many domains. However, no research has been done into building confidence intervals for the parameter of the Poisson-Pranav distribution using the bootstrap method. Using Monte Carlo simulation, the coverage probabilities and average lengths of the percentile bootstrap (PB), basic bootstrap (BB), and biased-corrected and accelerated (BCa) bootstrap’s interval-estimation performances were compared. The bootstrap method was not appropriate for achieving the desired nominal confidence level with small sample sizes. In addition, the performance of the bootstrap confidence intervals did not differ significantly when the sample size was increased considerably. In each case studied, the BCa bootstrap confidence intervals performed better than the others. The effectiveness of bootstrap confidence intervals was demonstrated by applying them to the number of COVID-19 deaths in Belgium. The computations substantially supported the proposed bootstrap confidence intervals.

Population pharmacokinetics of intravenous daptomycin in critically ill patients: implications for selection of dosage regimens.

Daptomycin is gaining prominence for the treatment of methicillin-resistant Staphylococcus aureus infections. However, the dosage selection for daptomycin in critically ill patients remains uncertain, especially in Chinese patients. This study aimed to establish the population pharmacokinetics of daptomycin in critically ill patients, optimize clinical administration plans, and recommend appropriate dosage for critically ill patients in China. The study included 64 critically ill patients. Blood samples were collected at the designated times. The blood daptomycin concentration was determined using validated liquid chromatography-tandem mass spectrometry. A nonlinear mixed-effects model was applied for the population pharmacokinetic analysis and Monte Carlo simulations of daptomycin. The results showed a two-compartment population pharmacokinetic model of daptomycin in critically ill adult Han Chinese patients. Monte Carlo simulations revealed that a daily dose of 400mg of daptomycin was insufficient for the majority of critically ill adult patients to achieve the anti-infective target. For critically ill adult patients with normal renal function (creatinine clearance rate >90mL/min), the probability of achieving the target only reached 90% when the daily dose was increased to 700mg. For patients undergoing continuous renal replacement therapy (CRRT), 24h administration of 500mg met the pharmacodynamic goals and did not exceed the safety threshold in most patients. Therefore, considering its efficacy and safety, intravenous daptomycin doses are best scaled according to creatinine clearance, and an increased dose is recommended for critically ill patients with hyperrenalism. For patients receiving CRRT, medication is recommended at 24h intervals.

Abstract PO5-18-05: Assessment of the Potential of Photon mini-GRID Therapy for Pre-operative Partial Breast Cancer Treatment

Abstract Purpose We propose a new spatially fractionated radiation therapy (SFRT) technique, called mini-GRID therapy, to reduce normal tissue toxicities in pre-operative partial breast cancer radiotherapy. Mini-GRID therapy, an optimized implementation of GRID therapy, utilizes very narrow beams (widths ~ 1-2 mm) which significantly increase normal tissue tolerances. The concept, proposed by our team in 2017 [1], has been successfully implemented at Hospital de Santiago de Compostela (Spain) showing excellent results in terms of brain toxicity reduction [2]. Profiting from this remarkable increase in tolerance, this study aims to evaluate the feasibility and efficacy of photon mini-GRID therapy for pre-operative partial breast cancer treatment. Methods Ten unbiased clinical cases were selected for the study, comparing photon mini-GRID therapy with conventional broad beam radiotherapy. Monte Carlo simulations (TOPAS v.3.6) were performed based on the mini-GRID therapy implementation (megavoltage X-rays, Varian flattening-filter-free LINAC) realized at the University Hospital in Santiago de Compostela. A mean dose of 24 Gy was delivered in the PTV using three photon mini-GRID arrays with mini beams of approximately 1.8x1.8 mm² at the isocenter. The study aimed to achieve better tumor coverage than conventional pre-operative radiotherapy while ensuring comparable skin toxicity. Valley doses, determining tissue-sparing effects, were compared against organs-at-risk (OARs) tolerance limits. Results The mini-GRID therapy treatments ensured partial tumor coverage and met the dose tolerance limits for all the considered OARs, the most challenging of all being the skin [3]. Peak-to-valley dose ratio (PVDR) in the skin was higher than 3.5 for all the considered patients. Compared to standard radiotherapy (SRT) plans, mini-GRID therapy significantly reduced mean doses to the lungs, heart, and chest wall in the studied cases. Conclusion In this proof-of-concept study, we evaluated photon mini-GRID therapy with megavoltage X-rays as an alternative treatment approach for pre-operative partial breast cancer irradiation, demonstrating its feasibility and straightforward implementation. Because of its high skin and normal tissue-sparing capacities, photon mini-GRID therapy has great potential to improve pre-operative treatment outcomes. Complementary studies and clinical validation are needed to fully establish the optimal protocols and assess long-term effects, but the results encourage the integration of photon mini-GRID therapy as a promising modality for breast cancer therapy.

On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading

The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power.

A new dynamic Monte Carlo method satisfying n-particle diffusion equation with position-dependent diffusion coefficient, free energy, and intermolecular interactions.

A dynamic Monte Carlo (MC) method recently proposed by us [Nagai et al., J. Chem. Phys. 156, 154506 (2022)] to describe single-particle diffusion of a molecule in a heterogeneous space with position-dependent diffusion coefficient and free energy is generalized here to n-particle dynamics, where n molecules diffuse in heterogeneous media interacting via their intermolecular potential. Starting from the master equation, we give an algebraic proof that the dynamic MC transition probabilities proposed here produce particle trajectories that satisfy the n-particle diffusion equation with position-dependent diffusion coefficient D0i(ri), free energy F1i(ri), and intermolecular interactions Vij(ri, rj). The MC calculations based on this method are compared to molecular dynamics (MD) calculations for two-dimensional heterogeneous Lennard-Jones test systems, showing excellent agreement of the long-distance global diffusion coefficient between the two cases. Thus, the particle trajectories produced by the present MC transition probabilities satisfy the n-particle diffusion equation, and the diffusion equation well describes the long-distance trajectories produced by the MD calculations. The method is also an extension of the conventional equilibrium Metropolis MC calculation for homogeneous systems with a constant diffusion coefficient to the dynamics in heterogeneous systems with a position-dependent diffusion coefficient and potential. In the present method, interactions and dynamics of the real systems are coarse-grained such that the calculation cost is drastically reduced. This provides an approach for the investigation of particle dynamics in very complex and large systems, where the diffusing length is of sub-micrometer order and the diffusion time is of the order of milliseconds or more.

Calculation method for novel upright CT scanner isodose curves.

A computational method based on Monte-Carlo calculations is presented and used to calculate isodose curves for a new upright and tilting CT scanner useful for radiation protection purposes. The TOPAS code platform with imported CAD files for key components was used to construct a calculation space for the scanner. A sphere of water acts as the patient would by creating scatter out of the bore. Maximum intensity dose maps are calculated for various possible tilt angles to make sure radiation protection for site planning uses the maximum possible dose everywhere. The resulting maximum intensity isodose lines are more rounded than ones for just a single tilt angle and so closer to isotropic. These maximum intensity curves are closer to the isotropic assumption used in CTDI or DLP based methods of site planning and radiation protection. The isodose lines are similar to those of a standard CT scanner, just tilted upwards. There is more metal above the beam that lessens the dose above versus below isocenter. Aside from the orientation, this upright scanner is very similar to a typical CT scanner, and nothing different for shielding needs to be done for this new upright tilting CT scanner, because an isotropic scatter source is often assumed for any CT scanner.

An advanced fast method for the evaluation of multiple immunolabelling using gold nanoparticles based on low-energy STEM

We present a powerful method for the simultaneous detection of Au nanoparticles located on both sides of ultrathin sections. The method employs a high-resolution scanning electron microscope (HRSEM) operating in scanning transmission electron microscopy (STEM) mode in combination with the detection of backscattered electrons (BSE). The images are recorded simultaneously during STEM and BSE imaging at the precisely selected accelerating voltage. Under proper imaging conditions, the positions of Au nanoparticles on the top or bottom sides can be clearly differentiated, hence showing this method to be suitable for multiple immunolabelling using Au nanoparticles (NPs) as markers. The difference between the upper and lower Au NPs is so large that it is possible to apply common software tools (such as ImageJ) to enable their automatic differentiation. The effects of the section thickness, detector settings and accelerating voltage on the resulting image are shown. Our experimental results correspond to the results modelled in silico by Monte Carlo (MC) simulations.

A probabilistic approach to chronic effects assessments for listed species in a vernal pool case study.

Ecological risk assessments for potential pesticide impacts on species listed as threatened or endangered must ensure that decisions to grant registration or establish water quality standards will not jeopardize species or their critical habitats. Pesticides are designed to affect pest species via physiological pathways that may be shared by some nontarget species for which toxicity data are usually unavailable, creating a need for robust methods to estimate acute and chronic toxicity with minimal data. We used a unique probabilistic approach to estimate the risk of chronic effects of two organophosphate (OP) pesticides on the vernal pool fairy shrimp Branchinecta lynchi. Acute toxicity estimates were derived from Monte Carlo (MC) sampling of acute toxicity distributions developed from interspecies relationships using surrogate species. Within each MC draw, acute values were divided by an acute to chronic ratio (ACR) sampled from a distribution of ACRs for OP pesticides and invertebrates, producing a distribution of chronic effects concentrations. The estimated exposure concentrations (EECs) were sampled from distributions representing different environmental conditions. Risk was characterized using probability distributions of acute toxicity, ACRs, and EECs in a probabilistic analysis, as well as partial probabilistic variations that used only some distributions whereas some variables were used deterministically. A deterministic risk quotient (RQ) was compared with the results of probabilistic methods to compare the approaches. Risk varied across exposure scenarios and the number of variables that were handled probabilistically, increasing as the number of variables drawn from distributions increased. The magnitude of RQs was not correlated with the probability that EECs would exceed chronic thresholds, and comparison of the two approaches demonstrates the limited interpretability of RQs. Our novel probabilistic approach to estimating chronic risk with minimal data incorporates uncertainty underlying both exposure and effects assessments for listed species. Integr Environ Assess Manag 2024;00:1-13. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

Experiment and Monte Carlo simulation of gamma-ray backscattering from materials for nondestructive testing

Experiment and Monte Carlo simulation of gamma-ray backscattering from materials for nondestructive testing

Upsampling Monte Carlo Reactor Simulation Tallies in Depleted Sodium-Cooled Fast Reactor Assemblies Using a Convolutional Neural Network

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network.

A viscoelastic nonlinear energy sink with an electromagnetic energy harvester: Narrow-band random response

Abstract Nonlinear energy sink is a passive energy absorption device that surpasses linear dampers, and has gained significant attention in various fields of vibration suppression. This is owing to its capacity to offer high vibration attenuation and robustness across a wide frequency spectrum. Energy harvester is a device employed to convert kinetic energy into usable electric energy. In this paper, we propose an electromagnetic energy harvester enhanced viscoelastic nonlinear energy sink (VNES) to achieve passive vibration suppression and energy harvesting simultaneously. A critical departure from prior studies is the investigation of the stochastic P-bifurcation of the electromechanically coupled VNES system under narrow-band random excitation. Initially, approximate analytical solutions are derived using a combination of multiple-scale method and a perturbation approach. The substantial agreement between theoretical analysis solutions and numerical solutions obtained from Monte Carlo simulation underscores the method’s high degree of validity. Furthermore, the effects of system parameters on system responses are carefully examined. Additionally, we demonstrate that stochastic P-bifurcation can be induced by system parameters, which is further verified by the steady-state density functions of displacement. Lastly, we analyze the impacts of various parameters on mean square current and mean output power, crucial for selecting suitable parameters to enhance energy harvesting performance.

Modeled estimates of HIV-serodifferent couples in tuberculosis-affected households in four sub-Saharan African countries.

Household-based tuberculosis (TB) contact evaluation may be an efficient strategy to reach people who could benefit from oral pre-exposure prophylaxis (PrEP) because of the epidemiological links between HIV and TB. This study estimated the number of HIV serodifferent couples in TB-affected households and potential HIV acquisitions averted through their PrEP use in 4 TB-HIV high-burden countries. We conducted a model-based analysis set in Ethiopia, Kenya, South Africa, and Uganda using parameters from population-based household surveys, systematic literature review and meta-analyses, and estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. We parameterized the nonlinear relationship between the proportion of serodifferent couples among people living with HIV and population-level HIV prevalence using Markov chain Monte Carlo methods. We integrated all parameters in a mathematical model and propagated uncertainty using a Monte Carlo approach. We estimated the HIV prevalence among adults aged 15-49 living in TB-affected households to be higher than in the general population in all 4 countries. The proportion of serodifferent couples among all couples in TB-affected households was also higher than in the general population (South Africa: 20.7% vs. 15.7%, Kenya: 15.7% vs. 5.7%, Uganda: 14.5% vs. 6.0%, Ethiopia: 4.1% vs. 0.8%). We estimated that up to 1,799 (95% UI: 1,256-2,341) HIV acquisitions in South Africa could be prevented annually by PrEP use in serodifferent couples in TB-affected households, 918 (95% UI: 409-1,450) in Kenya, 686 (95% UI: 505-871) in Uganda, and 408 (95% UI: 298-522) in Ethiopia. As couples in TB-affected households are more likely to be serodifferent than couples in the general population, offering PrEP during household TB contact evaluation may prevent a substantial number of HIV acquisitions.