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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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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Knock Mitigation Effectiveness of EGR across the Pressure-Temperature Domain

Exhaust gas recirculation (EGR) has been shown to enable efficiency improvements in SI engines through multiple different mechanisms, including decreasing the knock propensity at high load, which allows higher compression ratio. While many of the benefits of EGR are applicable to both low and high power density engines, including reductions in pumping work and improved specific heat ratio, the knock benefits and corresponding compression ratio increases have been limited to low power density naturally aspirated engines primarily intended for hybrid vehicle architectures. An earlier study [1] indicated that there may be a kinetic limitation for the ability of EGR to mitigate knock under these conditions, but that study only considered a small number of conditions. In this investigation, we expand on that study while also providing data for model validation for the new light-duty combustion consortium from the U.S. Department of Energy: Partnership for Advancing Combustion Engines (PACE). In this investigation, the effectiveness of EGR to mitigate knock is studied with regards to the effect of engine speed (1,500 and 3,000 rpm), changing trajectory in the pressure-temperature domain by varying the intake manifold temperature (35, 60, and 90 deg C), and by considering the effect of minor species by studying the effect of untreated EGR vs. EGR that has been treated by an automotive three-way catalyst. Additionally, to increase the relevance of these data for future modeling studies, the performance of the full boiling range gasoline was compared relative to a surrogate formulation. The study found that the fuel surrogate performs well, confirmed the kinetic limitations of EGR to mitigate knock under boost, and showed improvements in EGR performance with catalyzed EGR.

42 ENGINEERING↗
DOE OSTI2023

Autoignition of cyclohexane at low-to-intermediate temperatures: Rapid compression machine experiments and improved comprehensive chemical kinetic model

Cycloalkanes are an important class of hydrocarbons found in conventional and alternative transportation fuels. Being one of the simplest practical cycloalkanes, cyclohexane is often used as a surrogate in studies to understand the oxidation, ignition, and sooting characteristics of cycloalkanes more broadly. Although cyclohexane has been extensively studied, very limited datasets for cyclohexane oxidation at low-to-intermediate temperatures are available in the literature. Moreover, the literature autoignition data from rapid compression machines (RCMs) did not provide information on volume histories or experimental heat transfer characteristics which can significantly impact model predictions, thereby hindering the detailed validation of chemical kinetic models. It has also been observed that the cyclohexane kinetic models developed so far are not able to predict the literature data across a wide range of operating and mixture conditions. Hence, further studies are recommended to understand and improve model predictions for cyclohexane oxidation. In this study, RCM experiments were conducted using cyclohexane/air mixtures at varying compressed pressures (P C =15, 20, and 30 bar), equivalence ratios (ϕ=0.5, 1.0, and 2.0), and compressed temperatures (T C =670–960 K). The current RCM results of cyclohexane were found to complement well with the literature data, with the first-stage IDTs at high pressures and volume histories at all test conditions being first reported for better development and validation of chemical kinetic models. Additionally, a detailed chemical kinetic model for the cyclohexane oxidation has been developed and validated using both newly acquired experimental IDTs in this study and literature experimental studies (IDTs in RCMs and STs, species profiles in jet-stirred reactors, and laminar burning velocities). Unlike literature models, the proposed model was observed to provide an overall better prediction for experimental results over a wide range of conditions. Chemical kinetic analysis of the developed model was further conducted to identify the reaction pathways controlling the autoignition of cyclohexane.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In-cylinder spray evolution in a motored central-injection gasoline engine: Imaging and simulating the effects of flash-boiling and intake crossflow

Accurate predictions of fuel spray behavior and mixture formation in simulations of direct-injection spark-ignition (DISI) engines are fundamental to ensure proper description of all subsequent processes including ignition, combustion, and emissions. In this work, the spray evolution in a single-cylinder optical DISI engine was studied experimentally and numerically with the goal of enabling predictive computational fluid dynamics (CFD) modeling of in-cylinder sprays. The authors explored a wide range of operating conditions characterized by several fuel injection temperatures and engine speeds, using a well-characterized nine-component gasoline surrogate known as PACE-20. The effect of flash boiling and intake crossflow on the spray is discussed, with a focus on evaluating the ability of the spray models to capture highly transient spray behavior. In the experiments, the fuel temperature was varied between 20°C and 80°C, allowing for non-flash- to flash-boiling transition to emerge with enhanced flashing intensity at the highest temperatures. Spray collapse resulted in vapor-rich regions, owing to the locally lower inertia of the fluid. Varying the engine speed from 650 to 1950 rpm promoted increasingly more turbulent in-cylinder crossflow which interacted with the spray during the injection event and resulted in enhanced spray dispersion. The CFD model was able to capture the spray morphology transition at different fuel temperatures and engine speeds adequately. Further, it is shown that the spray breakup model could capture the transitional spray behavior induced by flash boiling atomization and intake flow via proper initialization of the spray cone angle and calibration of the spray models’ constants.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Development and Validation of Physics-Based Sub-Models of High-Pressure Supercritical Fuel Injection at Diesel Conditions Insert

The project sought to develop and validate accurate, physics-based, numerical submodels and implement them in computational fluid dynamics (CFD) software codes to enable accurate predictions of diesel sprays at pressures higher than the current systems. The combined experimental-computational effort has four main objectives: (1) acquire spatially and temporally resolved scalar measurements of fuel-oxidizer mixing in the near field of the fuel jet, (2) develop software modules to calculate thermo-physical properties of diesel surrogates and their mixtures with oxidizer of interest in real-gas operating regime, (3) integrate the real-fluid model into a commercial and an open-source CFD code to simulate fuel-air mixing in high-pressure diesel sprays, and (4) assess the robustness, accuracy, and uncertainty of the integrated CFD solvers in computing diesel sprays. Project accomplishments are consistent with the original goals of the project. Although we acquired spatially and temporally resolved scalar measurements, they were limited to refractive index difference and not to thermodynamic properties such as temperature and equivalence ratio. However, we successfully developed the theoretical background needed to acquire such measurements at diesel conditions and demonstrated the capability of the RSD for scalar measurements in a canonical turbulent jet flow replicating features of the diesel jet. Additional work will be needed to implement these procedures at high-pressure, high-temperature engine-like conditions of the CPFR. Mainly, we did not anticipate the need to find the liquid boundary and its statistical features or the state relationships between refractive index and thermodynamic properties, assuming the widely known Dale-Gladstone equation would suffice. Still, efforts devoted to these tasks were essential to fulfil the goals of the project and serves as the foundation on which many of the conclusions and key contributions of this project are based. Further assessment and application of the developed CFD codes is recommended to fully realize the benefits of this research.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Solving multiphysics-based inverse problems with learned surrogates and constraints

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.

Yin, Ziyi (ORCID:0000000250248771)↗
DOE OSTI2021

Evaluation of Tritium Behavior in Forest Vegetation for the Purpose of Determining Appropriate Non-Zero Deposition Velocities for Tritium Oxide

Modeling and field measurements have been conducted between 2019 and 2021 to assess the practicality of using a non-zero deposition velocity for safety basis estimates of tritium oxide fate and transport modeling. A model was developed which used a complex deposition algorithm designed to assess how tritium oxide would mix based on the turbulent motions and wind speed effects that the forest canopy has on the atmosphere. The model was driven based on measurements of wind and turbulence taken from the Aiken AmeriFlux Tower which measures these properties at five levels located within and just above the forest. The model was then validated against a series of field experiments which were designed to test the model predictions and estimate the deposition velocity occurring over the forest environment at the Savannah River Site. The field releases used deuterium oxide as a surrogate for tritium oxide and was released as a fine mist which rapidly evaporated, creating a gaseous tracer in the atmosphere. Using air samplers, the elevations in deuterium concentration in the air relative to background measurements was assessed and then modeled. Generally, the numerical model tended to underpredict the amount of deuterium being mixed from above the canopy to the forest floor, indicating that the predictions it provides are still conservative relative to what was measured during the field experiments. Across a suite of modeling runs, the 95 th and 99 th percentile deposition velocities were estimated to be 1.2 and 0.7 cm s -1 , respectively. Estimated deposition velocities in the 2021 field experiments, which specifically assessed a release above the forest canopy and its mixing to the surface, predicted deposition velocities ranging from 1.75 to 6.61 cm s -1 . While these field releases do not cover all possible meteorological conditions, it seems appropriate to use a non-zero deposition velocity when performing safety-basis modeling of tritium oxide. The recommendation presented in this report is to use 1.0 cm s -1 . This is between the 95 th and 99 th percentile value estimated from the modeling study, suggesting it should be appropriate for the majority of release scenarios given the model’s apparent conservatism relative to field measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Pretest Modeling A Spent Nuclear Fuel Seismic Shake Test

The U.S. Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is planning to conduct a series of full-scale shake table tests to simulate hypothetical earthquake conditions and record the response of surrogate spent nuclear fuel (SNF) assemblies in a dry canister storage system mockup. The shake table motions will represent a range of hypothetical earthquake conditions at hypothetical locations in the continental U.S. to generally define the range of mechanical loads that SNF can be expected to experience during extended dry storage periods. This paper describes the pretest predictions made with LS-DYNA models of the mockup storage systems. The test will use two dry storage system configurations, a mockup vertical concrete cask (VCC) and a mockup horizontal storage module (HSM). The test will use a production-quality canister and basket. Within the canister will be four instrumented fuel assemblies with fuel rods containing surrogate mass and 28 instrumented dummy assemblies that are intended to match the mass and outer dimension of a fuel assembly. The finite element models include models of the VCC and HSM on the shake table to calculate the system level dynamic responses and separate single fuel assembly models to calculate stress and strain in fuel assembly components. Both types of models include nonlinear behavior like rod-to-rod contact and the ability for VCC’s to rock and slide. This paper presents the expected response of the VCC, HSM, and fuel assemblies to the shake table testing that is planned to start in April of 2024. The earthquake conditions represent seismic hazards in the 2,000-to-20,000-year return period range. The test data is expected to confirm the expectation that fuel rod cladding will remain intact, fuel assembly structural components like guide tubes will remain intact, and no significant VCC sliding or tipping will occur in the range of conditions to be tested with the shake table.

Klymyshyn, Nicholas A.↗
DOE OSTI2022

Investigating the kinetic effect of prenol on iso -octane auto-ignition by means of an experimental and modeling study

A detailed experimental and kinetic modeling study was dedicated to understand the reported octane hyperboosting effect of prenol, by means of the measurement of the ignition delay times of its blends with iso -octane, and measurement of the mole fraction profiles of the fuels and intermediates inside the ULille rapid compression machine. Here, these results show that prenol addition leads to a reduction of the first-stage ignition phenomena and negative temperature coefficient behavior, which is only qualitatively captured by the model and is consistent with knock resistance improvement. It is suggested that this behavior is caused by two different factors. The first originates from gas-phase reactivity of prenol, and spans from the formation of unreactive unsaturated species through resonance-stabilized radicals, thereby constituting a competitive pathway for the radical pool generated by iso -octane. The second is of catalytic nature and cannot be captured by means of gas-phase kinetic modeling, but could also play an important role in the behavior of prenol in internal combustion engines.

42 ENGINEERING↗
DOE OSTI2024

Computational study on the impact of gasoline-ethanol blending on autoignition and soot/NO x emissions under low-load gasoline compression ignition conditions

Here, in the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NO x ), and soot emissions under low-load conditions. In order to represent the test gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF)+ethanol (ETPRF) surrogate (with 10% ethanol by volume; E10) is employed. A three-dimensional (3D) engine CFD model employing finite-rate chemistry with a skeletal kinetic mechanism (including NO x sub-mechanism), adaptive mesh refinement (AMR), and hybrid method of moments (HMOM) is adopted to capture the in-cylinder combustion phenomena and soot/NO x emissions. The engine CFD model is validated against experimental data for three gasoline-ethanol blends: E10, E30 and E100, with varying ethanol content by volume. Model validation is carried out for a broad range of start-of-injection (SOI) timings (−21, −27, −36, and −45 crank angle degrees (°CA) after top-dead-center (aTDC)) with respect to in-cylinder pressure, heat release rate, combustion phasing, NO x and soot emissions. For relatively later injection timings (−21 and −27 °CA aTDC), E30 yields higher amount of soot than E10; while the trend reverses for early injection cases (−36 and −45 °CA aTDC ). On the other hand, E100 yields the lowest amount of soot among all fuels irrespective of SOI timing. Further, E10 shows a non-monotonic trend in soot emissions with SOI timing: SOI-36>SOI-45>SOI-21>SOI-27, while soot emissions from E30 exhibit monotonic decrease with advancing SOI timing. NO x emissions from various fuels follow a trend of E10>E30>E100. On the other hand, NO x emissions increase as SOI timing is advanced for all fuels, with an anomaly for E10 and E100 where NO x decreases when SOI is advanced beyond −36 °CA aTDC. Detailed analysis of the numerical results is performed to investigate the soot/NO x emission trends and elucidate the impact of chemical composition and physical properties on autoignition and emissions characteristics.

Computational fluid dynamics↗
DOE OSTI2026

CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.

Jiang, Cheng [Edinburgh U.]↗
DOE OSTI2021

Evaluation of ceria as a surrogate material for UO 2 in experiments on fuel cracking driven by resistive heating

A variety of normal operation and accident scenarios can generate thermal stresses large enough to cause cracking in light-water reactor (LWR) fuel pellets. Cracking of fuel pellets can lead to reduced heat removal, larger centerline temperatures, and localized stress in cladding all of which impact fuel performance. Furthermore, pellet cracking also contributes to a temperature reduction in the pellet since the pellet fragments tend to move towards the heat sink (cladding), and the heat flow remains predominantly radial despite the presence of cracks. It is important to understand the temperature profile on the pellet before and after cracking to improve cracking models in fuel performance codes However, in-reactor observation and measurement of cracking is very challenging owing to the harsh environment and design of fuel rods. Recently, an experimental pellet cracking test stand was developed for separate effects testing of normal operations and accident temperature conditions, using thermal imaging to capture the pellet surface temperature for evaluation of thermal stresses and optical imaging to capture the evolution of cracking in real time. Cracking experiments were initially performed using ceria (CeO 2 ) as a surrogate fuel material, which is useful for developing and demonstrating the experimental approaches but is also valuable in its own right for cracking model development and validation. A combination of induction and resistance heating was used for volumetric heat generation in the pellet creating a thermal gradient. The material properties of CeO 2 and UO 2 are reviewed and compared for use in model development. Simulations of the experiment were performed to evaluate the behavior of the surrogate (CeO 2 ) fuel in BISON. The measured temperature profiles from BISON models match reasonably well with the observed experiments for the ceria pellets before cracking. The findings from this work will help improve confidence in fracture models used for fuel pellets under similar in-reactor conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Study of alkaline carbonate cooling to mitigate Ex-Vessel molten corium accidents

To mitigate adverse effects from molten corium following a reactor pressure vessel failure (RPVF), some new reactor designs employ a core catcher and a sacrificial material (SM), such as ceramic or concrete, to stabilize the molten corium and avoid containment breach. Existing reactors cannot easily be modified to include these SMs but could be modified to allow injectable cooling materials. Current reactor designs are limited to using water to stabilize the corium, but this can create other issues such as reaction of water with the concrete forming hydrogen gas. Here the novel SM proposed is a granular carbonate mineral that can be used in existing light water reactor plants. The granular carbonate will decompose when exposed to heat, inducing an endothermic reaction to quickly solidify the corium in place and producing a mineral oxide and carbon dioxide. Corium spreading is a complex process strongly influenced by coupled chemical reactions, including decay heat from the corium, phase change, and reactions between the concrete containment and available water. A recently completed Sandia National Laboratories laboratory directed research and development (LDRD) project focused on two research areas: experiments to demonstrate the feasibility of the novel SM concept, and modeling activities to determine the potential applications of the concept to actual nuclear plants. Small-scale experiments using lead oxide (PbO) as a surrogate for molten corium demonstrate that the reaction of the SM with molten PbO results in a fast solidification of the melt due to the endothermic carbonate decomposition reaction and the formation of open pore structures in the solidified PbO from CO 2 released during the decomposition. A simplified carbonate decomposition model was developed to predict thermal decomposition of carbonate mineral in contact with corium. This model was incorporated into MELCOR, a severe accident nuclear reactor code. A full-plant MELCOR simulation suggests that by the introduction of SM to the reactor cavity prior to RPVF ex-vessel accident progression, e.g., core-concrete interaction and core spreading on the containment floor, could be delayed by at least 15 h; this may be enough for additional accident management to be implemented to alleviate the situation.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Validation of the HyRAM+ physics models for use with propane

The Hydrogen Risk Assessment Model Plus (HyRAM+) toolkit combines quantitative risk assessment with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. HyRAM+ is differentiated from HyRAM in that it includes models and leak data for other alternate fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for propane which is being used as a surrogate for autogas, which is a mixture of propane and butane and used in internal combustion engines in vehicles. For flame length comparisons, five previously published experiments from peer reviewed journals were used to validate our models. The validation looked at flame lengths and flame widths with respect to different leak diameters, mass flow rates, and source pressures. Most of the sources included more than one set of experimental data, which were collected using different methods (CCD cameras, IR visualization etc.). In general, HyRAM+ overpredicts the flame lengths by around 65%. For heat and radiation models, we compared the heat flux and radiation data reported from two different sources to the values calculated by HyRAM+. For higher mass flow rates, the HyRAM+ calculated flame length results gave a better estimate of what is found in the experiments (65% error), but a higher error (85%) is observed between the HyRAM+ calculated lengths and the experimental flame lengthsfor lower mass flows. Some differences can be attributed to outdoor environmental effects (i.e. wind speed) and uncertainties in jet flame shapes. The propane flame trajectory is predicted for a high Reynolds number case with Re = 12,500 and a low Reynolds number case where Re = 2,000. The Re=12,500 case which is momentum dominated matches well with the experimental flame trajectory, but the agreement for the bouancy driven low Reynolds number case is not as good. Dispersion modeling for unignited propane was also analyzed. We compared the mole fraction, mixture fraction, mean velocity, concentration half width, and inverse mass concentration over an axial distance from different credible journals to the values calculated by HyRAM+. The results display good agreement but generally, HyRAM+ predicts a wider profile for mole fraction and mixture fraction experiments. Overall, HyRAM+’s results are reasonable for predicting the flame length, heat flux, flame trajectory, and dispersion for propane and can be used in risk analyses

08 HYDROGEN↗
DOE OSTI2026

Neural operator transformers capture bifurcating drift-wave turbulence in fusion plasma simulations

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Decomposition and Algorithmic Approaches for Solving Large-Scale Process Family Design Problems

Our most recent work expands the water desalination case study from 76 variants to 10,897 variants using the equation-oriented model built in Pyomo as part of the PARETO project. Using the discretization formulation presented in Stinchfield (2024a), rather than solving for all 10,897 variants simultaneously, we decompose the formulation into subproblems containing subsets of variants from the process family. We solve the overall problem with Progressive Hedging (PH) deployed in parallel on a distributed HPC cluster using the open-source Python package mpi-sppy (Knueven et al., 2023). This approach allowed us to solve this process family design problem to ~1.5% relative optimality gap in about 5 hours; in comparison, Gurobi reached ~50% relative optimality gap in about 6 hours (Stinchfield et al., 2024b). However, this approach still requires discretization of the common unit module design ranges; additionally, PH acts as a heuristic for MILP’s with gap-closing capabilities. Ideally, we would not have to use ML surrogates or discretization to solve this problem, instead solving the process family design problem with the equation-oriented model directly to achieve the most accurate results. However, recall that we did not consider solving the MINLP directly due to complexity and size. In this work, we aim to decompose and solve this large-scale MINLP using a Structured Nonlinear Global Optimization algorithm presented by Cao and Zavala (2019).

Stinchfield, Georgia↗
DOE OSTI2020

Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo

Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells.These models usually involve several parameters, such as the kinetic rates of chemical reactions, that are not directly measurable and must be inferred from experimental data. Bayesian inference provides a rigorous probabilistic frame-work for identifying these parameters by finding a posterior parameter distribution that captures their uncertainty.Traditional computational methods for solving inference problems such as Markov Chain Monte Carlo methods based on classical Metropolis-Hastings algorithm involve numerous serial evaluations of the likelihood function, which in turn requires expensive forward solutions of the chemical master equation (CME). We propose an alternate approach based on a multifidelity extension of the Sequential Tempered Markov Chain Monte Carlo (ST-MCMC) sampler. This algorithm is built upon Sequential Monte Carlo and solves the Bayesian inference problem by decomposing it into a sequence of efficiently solved subproblems that gradually increase both model fidelity and the influence of the observed data. We reformulate the finite state projection (FSP) algorithm, a well-known method for solving the CME, to produce a hierarchy of surrogate master equations to be used in this multifidelity scheme. To determine the appropriate fidelity, we introduce a novel information-theoretic criteria that seeks to extract the most information about the ultimate Bayesian posterior from each model in the hierarchy without inducing significant bias. This novel sampling scheme is tested with high performance computing resources using biologically relevant problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Experimental and Computational Characterization of a Modified Sioutas Cascade Impactor for Respirable Radioactive Aerosols

Oak Ridge National Laboratory is collecting and characterizing aerosols released when spent nuclear fuel (SNF) rods are fractured in bending. An aerosol collection system was designed and tested to collect respirable sized (<10 μm aerodynamic diameter [AED]) particulates inside a hot cell facility. The setup is a modified version of the commercially available Sioutas cascade impactor, to which additional stages were added to expand the aerosol collection range from 2.5 to ~15 μm AED. To accommodate the additional stages and specific test conditions, the operating flow rate for aerosol collection was reduced, and testing was conducted by using pressure drop measurements, surrogate dust collection, and particle size characterization. The fluid flow distribution within the cascade and its stages was simulated in STAR-CCM+, and the stage-wise pressure drops obtained using the computational fluid dynamics model were then compared to experimental data. Lagrangian particle simulations were also performed, and stage-wise collection statistics were obtained from the simulation for comparison with the experimental data obtained using SNF-surrogate dust particles. The results provide valuable insights into the stage-wise particle collection characteristics of the modified cascade impactor and can also be used to improve the prediction accuracy of the manufacturer-determined analytical correlations.

aerosol modeling↗
DOE OSTI2023

Simulation Study of Monoenergetic Photon Source LiDAR System Performance for Emergency Response Applications

The following report summarizes the results of a Monte Carlo simulation and modeling study of the Lawrence Berkeley National Laboratory’s (LBL) Monoenergetic Photon Source (MPS) as part of a high-energy LiDAR system for use in emergency response applications. A set of image quality indicators (IQI) and nonsensitive phantoms or generalized surrogates for search targets have been identified for use in the planned measurement campaign at the Berkeley Lab Laser Accelerator (BELLA) Center toward the end FY2023. These items were modeled and the time-of-flight (TOF) spectral responses following irradiation were calculated for a set of varying operating conditions. The goal of this study was to both predict the LiDAR system performance as well as determine if minor modifications to the IQI and phantom designs were required before fabrication. It was found that the IQI and target designs both overlap and extend towards the limit of performance of the system and are thus suitable for the final project measurement campaign.

43 - PARTICLE ACCELERATORS↗