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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Californium-252 production at the High Flux Isotope Reactor - II: Comparison between the highly enriched uranium and a proposed low-enriched uranium core

This is the second paper on a 252 Cf production study performed in support of efforts to convert the High Flux Isotope Reactor (HFIR) from highly enriched uranium (HEU) to low-enriched uranium (LEU) fuel. The first paper primarily focuses on validating computational tools and nuclear data. This companion paper evaluates another critical aspect: the 252 Cf production capability with a proposed LEU core. HFIR must maintain its world-class performance and missions following conversion and because 252 Cf is a vital, multipurpose neutron-emitting radioisotope, the ability to efficiently produce 252 Cf must be preserved. In this study, the HFIRCON transport and depletion tool, several nuclear data libraries, and Campaign 78 data were used to compute 252 Cf production, sensitivity, and safety metrics. Further, results indicate the 252 Cf production and production rates are slightly higher with a 95MW th LEU core compared with those obtained with the 85MW th HEU core. Additionally, the target peak fission rate densities, discharge cumulative fission densities, and heat deposition rates with the LEU core are within a few percent of those calculated with the HEU core. The findings suggest HFIR’s 252 Cf production capability can be effectively maintained with an LEU core without adversely affecting the safety metrics.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Benchmarking Cu/BEA and HBEA catalysts for high-octane gasoline synthesis

We distinguish rates at which carbon deposition occurs during initiation, rates at which catalytic centers are lost during deactivation, and paraffin-to-olefin ratio during propagation as benchmarks that distinguish 5 wt% Cu/H-BEA and H-BEA (Si/Al = 13.5) catalysts during dimethyl ether (DME) homologation in the presence of hydrogen. Studies that systematically vary initial DME contact time (210, 94, and 45 mol H+, initial s (mol C ) -1 ), DME pressure (4 and 22 kPa), and H 2 pressure (1, 24, and 48 kPa) reveal that Cu enables lower carbon deposition rates (on a per proton basis) in the induction period, increases the effluent paraffin-to-olefin ratio during propagation, and decreases the instantaneous site-loss yields by a factor of ~ 1.5-2x (moles of active sites lost per mole of DME) during termination sequences thus affecting the degree of product saturation and catalyst stability during DME homologation. Furthermore, these results provide mechanistic insights revealing the critical role of Cu in facilitating DME homologation to high value, high-octane gasoline-range hydrocarbons with higher cumulative turnovers than proton form H-BEA.

09 BIOMASS FUELS↗
DOE OSTI2020

The origin, transport, and evolution of ash in engine particulate filters

Engine particulate filters have been widely applied across the world to control engine exhaust particulate matter (or particulate number) emissions. With increasing vehicle mileage, ash accumulation deteriorates vehicle fuel economy and complicates on-board control. Extending filter service life with ash loading has significant economic and environmental impacts. Many studies have been conducted in characterizing ash accumulation and evaluating its impacts on filter performance. However, comprehensive reviews covering all the key issues in the field are rather rare. This paper reviews the extensive prior research on filter ash, and not only summarizes the experimental observations but also elucidates the fundamental mechanisms. The review covers the areas of ash origin, accumulation, transport, evolution, and artificial acceleration methods. The previously reported data of ash properties is compiled and analyzed. Furthermore, the advantages and disadvantages of ash acceleration approaches are also discussed in detail. Based on the cumulative understanding, a few potential ways to improve ash management are discussed in this paper. In summary, the present work systematically reviews the previous observations and understanding of ash aging in particulate filters and identifies areas that need further research, which can be useful guidance for future studies.

42 ENGINEERING↗
DOE OSTI2022

Sizing battery energy storage and PV system in an extreme fast charging station considering uncertainties and battery degradation

In this paper, we present mixed integer linear programming (MILP) formulations to obtain optimal sizing for a battery energy storage system (BESS) and solar generation system in an extreme fast charging station (XFCS) to reduce the annualized total cost. The proposed model characterizes a typical year with eight representative scenarios and obtains the optimal energy management for the station and BESS operation to exploit the energy arbitrage for each scenario. Contrasting extant literature, this paper proposes a constant power constant voltage (CPCV) based improved probabilistic approach to model the XFCS charging demand for weekdays and weekends. This paper also accounts for the monthly and annual demand charges based on realistic utility tariffs. Furthermore, BESS life degradation is considered in the model to ensure no replacement is needed during the considered planning horizon. Different from the literature, this paper offers pragmatic MILP formulations to tally BESS charge/discharge cycles using the cumulative charge/discharge energy concept. McCormick relaxations and the Big-M method are utilized to relax the bi-linear terms in the BESS operational constraints. Finally, a robust optimization-based MILP model is proposed and leveraged to account for uncertainties in electricity price, solar generation, and XFCS demand. Case studies were performed to signify the efficacy of the proposed formulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2025

Sustainable aviation fuel from ethanol: Techno-economic analysis and life cycle analysis

Sustainable aviation fuel (SAF) is crucial for improving energy security, enhancing domestic production, and reducing carbon emissions in the aviation sector. Among various SAF production technologies, the ethanol-to-jet (ETJ) pathway is a promising option due to its economic viability and technological maturity. This study integrates a techno-economic analysis (TEA) and a life cycle analysis (LCA) to evaluate emissions reduction strategies for SAF production via the ETJ pathway, considering use of ethanol derived from both corn grain and corn stover. Conventional corn grain-derived ETJ fuel reduces greenhouse gas (GHG) emissions by 22 % compared to fossil jet fuel, with potential reductions of 26 %–96 % when incorporating renewable energy sources, with a 6 %–32 % increase in the minimum fuel selling price (MFSP). Corn stover-derived ETJ achieves a 77 % GHG reduction but with higher MFSPs compared to corn grain ETJ. Carbon capture and storage (CCS without considering the cost for piping and sequestration, only compression) reduces the emissions of corn grain-derived ETJ by up to 32 gCO 2 e/MJ and enables negative emissions for corn stover-derived ETJ, with MFSP increases ranging from 1 % to 22 %. While carbon capture and utilization (CCU) increase ethanol yield by 47 %, it raises MFSPs by 54 % due to high electricity demand. Sustainable farming practices provide only limited carbon intensity (CI) reductions individually but do offer cumulative benefits when combined. These findings highlight the trade-offs between cost and environmental impact, providing insights to optimize SAF production strategies and support aviation sector goals for emissions reduction.

09 BIOMASS FUELS↗
DOE OSTI2025

Evaluating advanced nuclear fission technologies for future decarbonized power grids

Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $\$5.7$–$\$7.3$/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $\$6.0$/W–$\$7.7$/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall ~32 % at 100 GW of cumulative capacity and ~51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. In conclusion, these findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.

Capacity expansion↗
DOE OSTI2026

A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings

Model-free reinforcement learning (RL) provides a data-driven and adaptive approach to optimize building energy use while satisfying occupant comfort. This powerful tool does not need any prior knowledge about the environment and system it is optimizing and can adapt its policy based on the changes in captures. Like any other data-driven tool, it faces high training costs due to the extensive agent-environment interactions required to capture long-term building dynamics and user comfort. Transfer learning, particularly policy distillation, offers a promising way to accelerate training by leveraging pretrained RL agents in different building and system types. Here, this study investigates online student distillation, in which the student model updates its neural network weights using outputs from teacher models. The work introduces a student distillation strategy designed for efficient knowledge transfer, along with a teacher selection method that ensures high-quality guidance. The approach is validated using a highly calibrated whole building energy model for a small/medium commercial building test facility. Results show substantial reductions in training time and data requirements while surpassing the performance of ASHRAE Guideline 36, an advanced rule-based control strategy. The distilled RL model required 45% less data and achieved 20% higher cumulative rewards than a state-of-the-art RL model, with faster convergence and lower energy consumption. These outcomes demonstrate that effective transfer learning enables a scalable and data-efficient energy management solution for commercial buildings.

ASHRAE guideline 36↗
DOE OSTI2021

Modeling airborne pathogen transport and transmission risks of SARS-CoV-2

An integrated modeling approach has been developed to better understand the relative impacts of different expiratory and environmental factors on airborne pathogen transport and transmission, motivated by the recent COVID-19 pandemic. Computational fluid dynamics (CFD) modeling was used to simulate spatial-temporal aerosol concentrations and quantified risks of exposure as a function of separation distance, exposure duration, environmental conditions (e.g., airflow/ventilation), and face coverings. The CFD results were combined with infectivity models to determine probability of infection, which is a function of the spatial-temporal aerosol concentrations, viral load, infectivity rate, viral viability, lung-deposition probability, and inhalation rate. Uncertainty distributions were determined for these parameters from the literature. Probabilistic analyses were performed to determine cumulative distributions of infection probabilities and to determine the most important parameters impacting transmission. This modeling approach has relevance to both pathogen and pollutant dispersion from expelled aerosol plumes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Interspecific oral rabies vaccine bait competition in the Southeast United States

Here, the United States Department of Agriculture’s National Rabies Management Program (NRMP) has coordinated the use of oral rabies vaccination (ORV) to control the spread of raccoon rabies virus variant west of the Appalachian Mountains since 1997. Working with state and local partners, the NRMP deploys ORV baits containing a rabies vaccine, primarily targeting raccoon populations (Procyon lotor). Bait competition between raccoons and non-target species may limit the effectiveness of ORV programs, but the extent of bait competition remains poorly quantified, particularly in the southeastern United States. We placed placebo ORV baits in bottomland hardwood (n = 637 baits) and upland pine (n = 681 baits) habitats in South Carolina, USA during August-December 2019 and used remote cameras to examine bait competition between raccoons and non-target species. The estimated proportion of bait consumed by raccoons was 18.8 ± 2.1% in bottomland hardwood and 11.6 ± 2.1% in upland pine habitats. Vertebrate competition appeared to have a minimal effect on raccoon uptake as estimated consumption did not exceed 5% for any species or 8% of bait uptake events cumulatively. We estimated that raccoons were the primary consumer of baits in bottomland hardwood, whereas invertebrates were the primary consumer in upland pine (26.7 ± 1.3% of baits). Our results indicate a need to closely consider the effects of invertebrates on bait consumption to minimize their potential impact on ORV bait uptake by target species. Uptake probabilities by raccoons were relatively low but not primarily driven by competition with vertebrates. As such, strategies to increase the specificity of raccoon uptake may be needed to enhance the effectiveness of ORV baiting programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Parametric and sensitivity analysis of a PCM-integrated wall for optimal thermal load modulation in lightweight buildings

Load modulation in buildings is becoming increasingly important due to growing disparity in energy demand during peak and off-peak hours. Integrating phase change material (PCM) in building envelopes and using a controlled precooling strategy can provide substantial thermal load modulation; however, it may greatly increase the total energy use. Previous studies have employed PCM in building envelopes primarily for energy savings and, to some extent, peak load shedding and shifting. However, the load modulation capacity of a PCM-integrated envelope has not been well explored in the literature. In this study, we perform an extensive parametric and sensitivity analysis on PCM-integrated lightweight building walls and examine the combinatory effects of various PCM parameters on thermal load modulation and wall-related heat gains in buildings. Using numerical simulations, we investigate eight PCM parameters: PCM location in the wall, transition temperature, thickness, latent heat, transition range, density, specific heat, and thermal conductivity. Here, we evaluate their impact and relative importance to achieve maximum load modulation in buildings without compromising occupants’ thermal comfort or total energy use. The results show that the optimized PCM proposed in this study can completely invert the transient heat gain profile of the wall, providing up to 70% reduction of wall-related heat gain during peak hours without a major increase in the cumulative heat gain.

42 ENGINEERING↗
DOE OSTI2021

Uranium integral fission product yields for a spectrally-shaped 14.1 MeV neutron source at the National Ignition Facility

This paper describes the experimental results for an energy tuning assembly created to modify the National Ignition Facility deuterium–tritium fusion neutron source into a notional thermonuclear and prompt fission neutron spectrum, which has applications in integral measurements, nuclear data benchmarks, and radiation effects on microelectronics. The Monte Carlo neutron transport utilized MCNP5 to estimate the ETA-modified fluence using the ENDF-B/VIII.0 and IRDFF-II continuous energy nuclear data libraries, and SCALE Sampler was used to estimate the systematic nuclear data covariance using ENDF-B/VII.1 and IRDFF-II in a 252-group structure. The experiment fielded eight activation foils and a highly enriched uranium sample. This provided fifteen reaction channels that were used in a forward-fit comparison to the modeled results and to unfold the neutron spectrum using STAYSL. Gamma-ray spectrometry was performed on the activation and highly enriched uranium foils, and the reduced Χ 2 between the modeled and experimental values was 1.21. The results from the STAYSL unfold, reduced Χ 2 = 1.62, indicated that the modeled neutron spectrum was achieved and the systematic nuclear data uncertainty associated with the neutron transport and activation product cross sections was representative of the experiment. Integral cumulative fission product yield data were collected for 37 mass chains with a combination of gamma-ray spectrometry and radiochemical analysis. Fission product analysis was generally in agreement with two models using a semi-empirical fit and the General Observables of Fission code, with the exception of mass chains 88, 109, 111, 112, 113, 129, 139, 142, 144, 151, and 156.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Manganese concentration influences nitrogen cycling in agricultural soil

Manganese (Mn) can modulate nitrogen (N) transformations in soil, yet its role in agroecosystems remains understudied. We conducted a 51-day microcosm incubation with agricultural soils differing in long-term N history (N 0 , no added N; N 1 , added 225 kg N ha −1 for a duration of 27 years) and amended with soluble Mn at 0 (M 0 ), 50 (M 1 ), or 250 (M 2 ) mg kg −1 and Glyceria striata (Lam.) residue. In N 1 soils, Mn additions (both M 1 and M 2 ) lowered total mineral N by 25 % relative to N 1 M 0 at day 51 and reduced 51-day cumulative N₂O by 32 % (N 1 M 1 ) and 42 % (N 1 M 2 ) vs. N 1 M 0 , whereas effects in N 0 were negligible. Mn also depressed ammonia-oxidizing bacterial amoA gene transcripts at day 15 in N 1 M 2 vs. N 1 M 0 (2.5 fold change). This reduction was likely due to increased N loss via complete denitrification to N₂ through microbial pathways such as nitrate/nitrite-dependent manganese oxidation (NDMO), where bacteria directly used the added Mn 2+ to reduce nitrate (NO 3 − ) and nitrite (NO 2 − ) to N 2 or as Mn dependent-ammonia oxidation (Mnammox) where bacteria oxidized ammonium (NH 4 + ) to N 2 , using Mn oxides as electron acceptors that formed from the oxidation of the added Mn. Other contributing mechanisms may include Mn-induced N immobilization, toxicity, and changes in the microbial community. These mechanistic results indicate that background Mn availability and redox dynamics can shape nitrification–denitrification pathways under N-rich conditions. In conclusion, we highlight how native Mn pools and redox state may help explain observed variability in N losses and greenhouse gas production across agricultural soils.

Decomposition↗
DOE OSTI2020

Combined Cr and S poisoning behaviors of La 1-x Sr x MnO 3±δ and La 1-x Sr x Co 1-y FeyO 3-δ cathodes in solid oxide fuel cells

Although the individual effects of airborne Cr and S contaminants on SOFC cathode performance degradation have been extensively studied, the combined effects of Cr and S contaminants remain largely unexplored. Under the real SOFC operating condition where the Cr and S species coexist, their effects may compete, affecting the poisoning behavior. Here, our investigation reveals that the combined Cr and S poisoning behavior of LSCF remains different from those of individual Cr and S poisonings, while the combined poisoning mechanism of LSM is equivalent to the sum of those of individual Cr and S effects. For LSCF electrode, gaseous Cr species are deposited mainly at the LSCF/GDC interface by electrochemical reduction, rather than forming SrCrO 4 on LSCF surfaces (as in the case of Cr-only poisoning), indicating no reaction between Cr vapors and SrO on the LSCF surface. Thermodynamic analysis demonstrates that the SrO on LSCF surface absorbs SO 2 (g) and thereby loses the Cr-gettering effect, allowing Cr vapors to flow through LSCF and to reach the LSCF/GDC interface where the Cr deposition occurs. Unlike LSCF, LSM electrode shows cumulative effects of Cr and S, as Cr accumulation occurs at the triple-phase boundary and S absorption takes place at localized Sr-rich regions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Global Biodiversity Implications of Alternative Electrification Strategies Under the Shared Socioeconomic Pathways

Addressing climate mitigation while meeting global electrification goals will require major transitions from fossil-fuel dependence to large-scale renewable energy deployment. However, renewables require significant land assets per unit energy and could come at high cost to ecosystems, creating potential conflicts between global climate mitigation and biodiversity conservation. Here, we explore the potential biodiversity implications of alternative future global electrification pathways as depicted under the Shared Socioeconomic Scenarios (SSPs), i.e., alternative trends in societal development. We examined the intersection of high-resolution estimates of global energy densities for ten renewable and conventional technologies with global richness data to estimate technology-specific biodiversity footprints (species per GWh), whereas a Cumulative Biodiversity Impact (CBI) score was used to assess land and biodiversity outcomes of alternative scenarios. Downscaled electricity generation scenarios (2020-2100) were also constrained by alternative land conservation and energy development policies. Unexpectedly, variation among SSPs did not exhibit a clear tradeoff between global climate mitigation and CBI. Rather, CBIs were an outcome of total infrastructure development to meet electricity demand (from population growth and GDP) and the total magnitude of renewable energy development and storage technologies. Renewables assembled along a spectrum from land sharing to sparing. At the land sharing end, biomass-powered electricity from dedicated crops contributed the most to biodiversity impacts due to low energy density, whereas land-sparing technologies (solar) caused more-intense land degradation, but in smaller areas. Our results suggest that local land conservation practices and strategies promoting energy diversification could have greater implications for future biodiversity conflicts than global socioeconomic drivers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Evapotranspiration of advanced perennial bioenergy grasses produced on marginal land in the U.S. Midwest

The production of dedicated energy crops for biofuel and co-products can help develop a sustainable and viable bioeconomy under a changing climate. The co-production of perennial bioenergy and commodity crops in an agricultural landscape has the potential to reduce greenhouse gas emissions and provide numerous ecosystem services. However, as production of advanced, higher-yielding cultivars occurs, their impact on water resources needs to be considered. Estimates of evapotranspiration (ET) of perennial grasses compared to commodity crops are not always consistent in the literature. In this study, ET was estimated using an energy-balance model with Landsat satellite imagery and ground-based weather data to compare the large-scale production of advanced switchgrass cultivars (‘Independence’, ‘Liberty’, and ‘Carthage’), predecessor cultivars (‘Shawnee’ and ‘Sunburst’), and other perennial grasses (big bluestem and a low-diversity mixture) to corn grown continuously on marginal soils in the U.S. Midwest. Results showed significant differences in ET between corn and perennial grass treatments at four of the five field sites. However, differences between crop treatments were not consistent across sites, nor were the differences between advanced and predecessor switchgrass varieties. Production year played a large role in daily and cumulative ET across all sites. Differences in crop biomass yield between establishment and post-establishment years may contribute to these interannual differences. Site and interannual variations in precipitation and temperature may also be major contributing factors. Overall, the results of this study indicate that differences in ET between perennial bioenergy grasses and corn are likely dependent upon location of production and weather conditions.

09 BIOMASS FUELS↗
DOE OSTI2021

Validation of the TOPAS Monte Carlo toolkit for HDR brachytherapy simulations

The goal of this work is to validate the user-friendly Geant4-based Monte Carlo toolkit TOol for PArticle Simulation (TOPAS) for brachytherapy applications. Brachytherapy simulations performed with TOPAS were systematically compared with published TG-186 reference data. The photon emission energy spectrum, the air-kerma strength, and the dose-rate constant of the model-based dose calculation algorithm (MBDCA)-WG generic Ir-192 source were extracted. For dose calculations, a track-length estimator was implemented. Additionally, the four Joint AAPM/ESTRO/ABG MBDCA-WG test cases were evaluated through histograms of the local and global dose difference volumes. A prostate, a palliative lung, and a breast case were simulated. For each case, the dose ratio map, the histogram of the global dose difference volume, and cumulative dose-volume histograms were calculated. The air-kerma strength was (9.772 ± 0.001) × 10 -8 U Bq -1 (within 0.3% of the reference value). The dose-rate constant was 1.1107 ± 0.0005 cGy h -1 U -1 (within 0.01% of the reference value). For all cases, at least 96.9% of voxels had a local dose difference within [-1%, 1%] and at least 99.9% of voxels had a global dose difference within [-0.1%, 0.1%]. The implemented track-length estimator scorer was more efficient than the default analog dose scorer by a factor of 237. For all clinical cases, at least 97.5% of voxels had a global dose difference within [-1%, 1%]. Dose-volume histograms were consistent with the reference data. TOPAS was validated for high-dose-rate brachytherapy simulations following the TG-186 recommended approach for MBDCAs. Built on top of Geant4, TOPAS provides broad access to a state-of-the-art Monte Carlo code for brachytherapy simulations.

59 BASIC BIOLOGICAL SCIENCES↗