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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

On the quantum Landau collision operator and electron collisions in dense plasmas

The quantum Landau collision operator, which extends the widely used Landau/Fokker-Planck collision operator to include quantum statistical effects, is discussed. The quantum extension can serve as a reference model for including electron collisions in non-equilibrium dense plasmas, in which the quantum nature of electrons cannot be neglected. In this paper, the properties of the Landau collision operator that have been useful in traditional plasma kinetic theory and plasma transport theory are extended to the quantum case. We outline basic properties in connection with the conservation laws, the H-theorem, and the global and local equilibrium distributions. We discuss the Fokker-Planck form of the operator in terms of three potentials that extend the usual two Rosenbluth potentials. We establish practical closed-form expressions for these potentials under local thermal equilibrium conditions in terms of Fermi-Dirac and Bose-Einstein integrals. We study the properties of linearized quantum Landau operator, and extend two popular approximations used in plasma physics to include collisions in kinetic simulations. We apply the quantum Landau operator to the classic test-particle problem to illustrate the physical effects embodied in the quantum extension. We present useful closed-form expressions for the electron-ion momentum and energy transfer rates. Throughout the paper, similarities and differences between the quantum and classical Landau collision operators are emphasized.

Daligault, Jérôme↗
DOE OSTI2023

Keeping it simple: simplified frameworks for long-lived particles at neutrino facilities

Modern-day accelerator neutrino facilities are excellent venues for searches for new-physics particles. Many distinct new-physics models predict overlapping signatures and phenomenology in these experiments. In this work, we advocate for the adoption of simplified frameworks when studying these types of new-physics signatures, which are characterized by a small number of primary variables, including particle masses, lifetimes, and production and decay modes/rates that most directly control signal event rates and kinematics. In particular, taking the example of long-lived particles that decay inside a neutrino detector as a test case, we study formulate and study simplified frameworks in the context of light scalars/fermions produced in kaon decays which then decay into final states containing an electron-positron pair. We show that using these simplified frameworks can allow for individual experimental analyses to be applicable to a wide variety of specific model scenarios. As a side benefit, we demonstrate that using this approach can allow for the T2K collaboration, by reinterpreting its search for Heavy Neutral Leptons, to be capable of setting world-leading limits on the Higgs-Portal Scalar model. Furthermore, we argue the simplified framework interpretation can serve as a bridge to model identification in the hopeful detection of a new-physics signal. As an illustration, we perform a first determination of the likelihood that, in the presence of a new-physics signal in a detector like the DUNE ND-GAr, multiple different new-physics hypotheses (such as the Higgs-Portal Scalar and Heavy Neutral Lepton ones) can be disentangled. We demonstrate that this model discrimination is favorable for some portions of detectable new-physics parameter space but for others, it is more challenging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Scalar QED as a toy model for higher-order effects in classical gravitational scattering

Quantum Electrodynamics (QED) serves as a useful toy model for classical observables in gravitational two-body systems with reduced complexity due to the linearity of QED. We investigate scattering observables in scalar QED at the sixth order in the charges (two-loop order) in a classical regime analogous to the post-Minkowskian expansion in General Relativity. We employ modern scattering amplitude tools and extract classical observables by both eikonal methods and the formalism of Kosower, Maybee, and O’Connell (KMOC). In addition, we provide a simplified approach to extracting the radial action beyond the conservative sector.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

The Common Occurrence of Highly Supercooled Drizzle and Rain near the Coastal Regions of the Western United States

The formation of highly supercooled rain was documented by aircraft observations in clouds at a wide range of conditions near the coastal region of the western United States. Several case studies are described in detail using combined cloud and aerosol measurements to document both the highly super-cooled condition and the relatively pristine aerosol conditions under which it forms. The case studies include: (1) Marine convective clouds over the coastal waters of northern California, as measured by cloud physics probes flown on a Gulfstream-1 aircraft during the CALWATER campaign in February and early March 2011. The clouds had extensive drizzle in their tops, which extended downward to the 0°C isotherm as supercooled rain. Ice multiplication was observed only in mature parts of the clouds where cloud water was already depleted. (2) Orographically triggered convective clouds in marine air mass over the foothills of the Sierra Nevada to the east of Sacramento, as measured in CALWATER. Supercooled rain was observed down to -21°C. No indications for ice multiplication were evident. (3) Orographic layer clouds over Yosemite National Park, also measured in CALWATER. The clouds had extensive drizzle at -21°C, which intensified with little freezing lower in the cloud, and (4) Supercooled drizzle drops in layer clouds near Juneau, Alaska, as measured by the Wyoming King Air as part of a FAA project to study aircraft icing in this region. Low concentrations of CCN was a common observation in all these clouds, allowing for the formation of clouds with small concentration of large drops that coalesced into supercooled drizzle and raindrops. Another common observation was the absence of ice nuclei and/or ice crystals in measurable concentrations was associated with the persistent supercooled drizzle and rain. Average ice crystal concentrations were 0.007 l-1 at the top of convective clouds at -12°C and 0.03 l-1 in the case of layer clouds at -21°C. In combination these two conditions provide ideal conditions for the formation of highly supercooled drizzle and rain. These results help explain the anomalously high incidences of aircraft icing at cold temperatures in U.S. west coast clouds (Bernstein et al., 2004) and highlight the need to include aerosol effects when simulating aircraft icing with cloud models. These case studies can also serve as benchmarks for explicit cloud microphysics models attempting to simulate the formation of precipitation in these types of pristine conditions.

coastal↗
DOE OSTI2016

Coherent states, quantum gravity, and the Born-Oppenheimer approximation. I. General considerations

This article, as the first of three, aims at establishing the (time-dependent) Born-Oppenheimer approximation, in the sense of space adiabatic perturbation theory, for quantum systems constructed by techniques of the loop quantum gravity framework, especially the canonical formulation of the latter. The analysis presented here fits into a rather general framework and offers a solution to the problem of applying the usual Born-Oppenheimer ansatz for molecular (or structurally analogous) systems to more general quantum systems (e.g., spin-orbit models) by means of space adiabatic perturbation theory. The proposed solution is applied to a simple, finite dimensional model of interacting spin systems, which serves as a non-trivial, minimal model of the aforesaid problem. Furthermore, it is explained how the content of this article and its companion affect the possible extraction of quantum field theory on curved spacetime from loop quantum gravity (including matter fields).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Ensemble cell-wide kinetic modeling of anaerobic organisms to support fuels and chemicals production

The goal of this project is to understand the metabolism and underlying regulatory networks of two anaerobic microorganisms, Moorella thermoacetica and Clostridium thermocellum by making use of Ensemble Modeling (EM) paradigm through integration of multiple omic information (transcriptomic, proteomic, metabolomic & fluxomic). Kinetic models have the potential to provide a mechanistic description of cellular metabolism by combining several layers of regulatory events into an integrated framework. The foundation of the kinetic model is a stoichiometric network description. To this end, we have developed and curated genome-scale metabolic (GSM) models of the organisms that will serve as scaffold for building the kinetic models. For M. thermoacetica, the GSM model was constructed and tested by the Stephanopoulos group with experimental growth rate. The GSM elucidated the energy conservation mechanism under autotrophic growth conditions. For C. thermocellum, the Maranas group has developed the second-generation GSM model through integration of experimental data. The Maranas group has also constructed a core metabolic kinetic model of the organism which includes the central metabolism and all relevant pathways for biofuels production. The stoichiometric and kinetic model predictions were tested extensively using experimental data for wild-type and 20 mutant strains. We have outlined below in detail the progress related to specific individual aims. Below are listed publications of research carried out based on this research grant support.

09 BIOMASS FUELS↗
DOE OSTI2021

Initial use of Nek5000/Cardinal to improve closure models in Pronghorn

Heat transfer coefficient closure models for pebble bed reactors are built using a data-driven approach by leveraging the capabilities of an Evolutionary Algorithm entitled Particle Swarm Optimization (PSO). In the present work, the Computational Fluid Dynamics code nekRS was used in order to collect the high-fidelity flow data for a core with 1,568 pebbles. To characterize the heat transfer, multiple concentric regions were considered to extract the physical quantities of interest, e.g./ the Reynolds number. The PSO algorithm is employed as part of an inverse problem targeting determine what are the coefficients for a Nusselt number correlation to match the collected data. Such correlation should follow any given format that is defined a priori. Finally, two correlations are proposed, one with an implicit dependence on the pebbles’ wall temperatures and another expressed as a fully explicit correlation depending on the flow conditions and the position within the core. Anyway, given the generic nature of the proposed approach, correlations following different formats could be tested. Preliminary results for the high-fidelity simulation of a fast MSR core are presented. The target Reynolds number is currently 20K, with the expectation that this will increase, pending the availability of further computational resources. These simulations will be used to inform lower fidelity models, including a coarse CFD turbulence model in Pronghorn. Additionally, they will serve as a reference for the RANS models in Nek5000/NekRS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

A generative graph model for electrical infrastructure networks

We propose a generative graph model for electrical infrastructure networks that accounts for heterogeneity in both node and edge type. To inform the design of this model, we analyze the properties of power grid graphs derived from the U.S. Eastern Interconnection, Texas Interconnection, and Poland transmission system power grids. Across these datasets, we find subgraphs induced by nodes of the same voltage level exhibit shared structural properties atypical to small-world networks, including low local clustering, large diameter and large average distance. On the other hand, we find subgraphs induced by transformer edges linking nodes of different voltage types contain a more limited structure, consisting mainly of small, disjoint star graphs. The goal of our proposed model is to match both these inter and intra-network properties by proceeding in two phases: we first generate subgraphs for each voltage level and then generate transformer edges that connect these subgraphs. The first phase of the model adapts the Chung-Lu random graph model, taking desired vertex degrees and desired diameter as inputs, while the second phase of the model is based on a simpler random star graph generation process. We test the performance of our model by comparing its output across many runs to the aforementioned real data. In nearly all categories tested, we find our model is more accurate in reproducing the unusual mixture of properties apparent in the data than the Chung-Lu model. We also include graph visualization comparisons, as well as a brief analysis of edge-deletion resiliency. Ultimately, our model may be used to generate synthetic graph data, test hypotheses and algorithms at different scales, and serve as a baseline model on top of which further electrical network properties, device models, and interdependencies to other networks, may be appended.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Continuum Microkinetic Rate Theory of Lattice Systems: Formalization, Current Limitations, and a Possible Basis for Continuum Rate Theory

A broad class of reaction networks are controlled not by any thermodynamic ensemble but by rates and are referred to as “kinetically constrained”. Here in this paper, microkinetic rate theory is extended into a model of arbitrarily complex reaction networks. This microkinetic rate theory takes a form where the constraints imposed by kinetically constrained systems apply naturally. This model is compared to numerically complete results and is shown to be quantitatively predictive. Model deviations are shown to emerge for systems with incomplete mixing. Finally, this model is analytical and may serve as a basis for device and continuum scale rate modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Integrative analysis of genes and miRNA alterations in human embryonic stem cells-derived neural cells after exposure to silver nanoparticles

Given the rapid growth of engineered and customer products made of silver nanoparticles (Ag NPs), understanding their biological and toxicological effects on humans is critically important. The molecular developmental neurotoxic effects associated with exposure to Ag NPs were analyzed at the physiological and molecular levels, using an alternative cell model: human embryonic stem cell (hESC)-derived neural stem/progenitor cells (NPCs). In this study, the cytotoxic effects of Ag NPs (10–200 μg/ml) were examined in these hESC-derived NPCs, which have a capacity for neurogenesis in vitro, at 6 and 24 h. The results showed that Ag NPs evoked significant toxicity in hESC-derived NPCs at 24 h in a dose-dependent manner. In addition, Ag NPs induced cell cycle arrest and apoptosis following a significant increase in oxidative stress in these cells. To further clarify the molecular mechanisms of the toxicological effects of Ag NPs at the transcriptional and post-transcriptional levels, the global expression profiles of genes and miRNAs were analyzed in hESC-derived NPCs after Ag NP exposure. The results showed that Ag NPs induced oxidative stress and dysfunctional neurogenesis at the molecular level in hESC-derived NPCs. Based on this hESC-derived neural cell model, these findings have increased our understanding of the molecular events underlying developmental neurotoxicity induced by Ag NPs in humans. - Highlights: • This system served as a suitable model for developmental neurotoxicity testing. • Ag NPs induce the apoptosis in human neural cells by ROS generation. • Genes for development of neurons were dysregulated in response to Ag NPs. • Molecular events during early developmental neurotoxicity were proposed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Yttrium solid phase equation of state with uncertainty quantification

We discuss the development of an equation of state (EOS) for solid yttrium up to roughly 80 GPa. The EOS makes use of different experimental datasets, including measurements of the isobaric density, isobaric specific heat, room temperature isotherm, and principal shock Hugoniot. The fitting procedure is carried out using Markov Chain Monte Carlo (MCMC), where we fit model parameters for both the cold curve and ion thermal models simultaneously. The results show close agreement with experimental data and provide meaningful uncertainty estimates on the model parameters. This work serves as a first step towards a multiphase EOS for yttrium, which will include higher pressure solid phases (> 80 GPa), as well as modeling of the liquid phase.

36 MATERIALS SCIENCE↗
DOE OSTI2027

Nodal capacity expansion planning with flexible large-scale load siting

We propose explicitly incorporating large-scale load siting into a stochastic nodal power system capacity expansion planning model that concurrently co-optimizes generation, transmission, and storage expansion. The potential operational flexibility of some of these large loads is also taken into account by considering them as consisting of a set of tranches with different reliability requirements, which are modeled as a constraint on expected served energy across operational scenarios. We implement our model as a two-stage stochastic mixed-integer optimization problem with cross-scenario expectation constraints. To overcome the challenge of scalability, we build upon existing work to implement this model on a high performance computing platform and exploit scenario parallelization using an augmented Progressive Hedging Algorithm. The algorithm is implemented using the bounding features of mpisppy, which have shown to provide satisfactory provable optimality gaps despite the absence of theoretical guarantees of convergence. We test our approach and assess the value of this proactive planning framework on total system cost and reliability metrics using realistic testcases geographically assigned to San Diego and South Carolina, with datacenter and direct air capture facilities as large loads.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

DES Y1 results: Splitting growth and geometry to test $\Lambda$CDM

We analyze Dark Energy Survey (DES) data to constrain a cosmological model where a subset of parameters—focusing on Ωm—are split into versions associated with structure growth (e.g., Ωmgrow) and expansion history (e.g., Ωmgeo). Once the parameters have been specified for the ΛCDM cosmological model, which includes general relativity as a theory of gravity, it uniquely predicts the evolution of both geometry (distances) and the growth of structure over cosmic time. Any inconsistency between measurements of geometry and growth could therefore indicate a breakdown of that model. Our growth-geometry split approach therefore serves both as a (largely) model-independent test for beyond-ΛCDM physics, and as a means to characterize how DES observables provide cosmological information. We analyze the same multiprobe DES data as [Phys. Rev. Lett. 122, 171301 (2019)PRLTAO0031-900710.1103/PhysRevLett.122.171301] : DES Year 1 (Y1) galaxy clustering and weak lensing, which are sensitive to both growth and geometry, as well as Y1 BAO and Y3 supernovae, which probe geometry. We additionally include external geometric information from BOSS DR12 BAO and a compressed Planck 2015 likelihood, and external growth information from BOSS DR12 RSD. We find no significant disagreement with Ωmgrow=Ωmgeo. When DES and external data are analyzed separately, degeneracies with neutrino mass and intrinsic alignments limit our ability to measure Ωmgrow, but combining DES with external data allows us to constrain both growth and geometric quantities. We also consider a parametrization where we split both Ωm and w, but find that even our most constraining data combination is unable to separately constrain Ωmgrow and wgrow. Relative to ΛCDM, splitting growth and geometry weakens bounds on σ8 but does not alter constraints on h.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Comprehensive compartmental model and calibration algorithm for the study of clinical implications of the population-level spread of COVID-19: a study protocol

The complex dynamics of the coronavirus disease 2019 (COVID-19) pandemic has made obtaining reliable long-term forecasts of the disease progression difficult. Simple mechanistic models with deterministic parameters are useful for short-term predictions but have ultimately been unsuccessful in extrapolating the trajectory of the pandemic because of unmodelled dynamics and the unrealistic level of certainty that is assumed in the predictions. We propose a 22-compartment epidemiological model that includes compartments not previously considered concurrently, to account for the effects of vaccination, asymptomatic individuals, inadequate access to hospital care, post-acute COVID-19 and recovery with long-term health complications. Additionally, new connections between compartments introduce new dynamics to the system and provide a framework to study the sensitivity of model outputs to several concurrent effects, including temporary immunity, vaccination rate and vaccine effectiveness. Subject to data availability for a given region, we discuss a means by which population demographics (age, comorbidity, socioeconomic status, sex and geographical location) and clinically relevant information (different variants, different vaccines) can be incorporated within the 22-compartment framework. Considering a probabilistic interpretation of the parameters allows the model's predictions to reflect the current state of uncertainty about the model parameters and model states. We propose the use of a sparse Bayesian learning algorithm for parameter calibration and model selection. This methodology considers a combination of prescribed parameter prior distributions for parameters that are known to be essential to the modelled dynamics and automatic relevance determination priors for parameters whose relevance is questionable. This is useful as it helps prevent overfitting the available epidemiological data when calibrating the parameters of the proposed model. Population-level administrative health data will serve as partial observations of the model states.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Neutron Diffusion Model for Prompt Burst Simulation in Fissile Solutions

A simulation model, based on neutron diffusion, has been developed to predict prompt burst excursions in fissile solutions. This model combines neutron diffusion with simple expressions for radiolytic gas generation and solution temperature change through a neutron cross-section model that accounts for reactivity changes. The simulation model tracks neutron flux as a function of time and position during transients. The model is intended to serve as a general survey tool for the behavior of unreflected aqueous fissile solutions commonly encountered at a variety of nuclear materials processing facilities. The model has been tested against experimental data from the KEWB and Silene research reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Advancing set-conditional set generation: Diffusion models for fast simulation of reconstructed particles

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve as efficient surrogate models. We propose a fast emulation approach that combines simulation and reconstruction. In other words, a neural network generates a set of reconstructed objects conditioned on input particle sets. To make this possible, we advance set-conditional set generation with diffusion models. Using a realistic, generic, and public detector simulation and reconstruction package (COCOA), we show how diffusion models can accurately model the complex spectrum of reconstructed particles inside jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Endoluminal ultrasound applicators for MR-guided thermal ablation of pancreatic tumors: Preliminary design and evaluation in a porcine pancreas model

Purpose: Endoluminal ultrasound may serve as a minimally invasive option for delivering thermal ablation to pancreatic tumors adjacent to the stomach or duodenum. The objective of this study was to explore the basic feasibility of this treatment strategy through the design, characterization, and evaluation of proof-of-concept endoluminal ultrasound applicators capable of placement in the gastrointestinal (GI) lumen for volumetric pancreas ablation under MR guidance. Methods: Two variants of the endoluminal applicator, each containing a distinct array of two independently powered transducers (10 × 10 mm 3.2 MHz planar; or 8 × 10 × 20 mm radius of curvature 3.3 MHz curvilinear geometries) at the distal end of a meter long flexible catheter assembly, were designed and fabricated. Transducers and circulatory water flow for acoustic coupling and luminal cooling were contained by a low-profile polyester balloon covering the transducer assembly fixture. Each applicator incorporated miniature spiral MR coils and mechanical features (guiding tips and hinges) to facilitate tracking and insertion through the GI tract under MRI guidance. Acoustic characterization of each device was performed using radiation force balance and hydrophone measurements. Device delivery into the upper GI tract, adjacent to the pancreas, and heating characteristics for treatment of pancreatic tissue were evaluated in MR-guided ex vivo and in vivo porcine experiments. MR guidance was utilized for anatomical target identification, tracking/positioning of the applicator, and MR temperature imaging (MRTI) for PRF-based multislice thermometry, implemented in the real-time RTHawk software environment. Results: Force balance and hydrophone measurements indicated efficiencies of 48.8% and 47.8% and −3 dB intensity beam-widths of 3.2 and 1.2 mm for the planar and curvilinear transducers, respectively. Ex vivo studies on whole-porcine carcasses revealed capabilities of producing ablative temperature rise (ΔT > 15 °C) contours in pancreatic tissue 4–40 mm long and 4–28 mm wide for the planar transducer applicator (1–13 min sonication duration, ∼4 W/cm{sup 2} applied acoustic intensity). Curvilinear transducers produced more selective heating, with a narrower ΔT > 15 °C contour length and width of up to 1–24 mm and 2–7 mm, respectively (1–7 min sonication duration, ∼4 W/cm{sup 2} applied acoustic intensity). Active tracking of the miniature spiral coils was achieved using a Hadamard encoding tracking sequence, enabling real-time determination of each coil’s coordinates and automated prescription of imaging planes for thermometry. In vivo MRTI-guided heating trials in three pigs demonstrated capability of ∼20 °C temperature elevation in pancreatic tissue at 2 cm depths from the applicator, with 5–7 W/cm{sup 2} applied intensity and 6–16 min sonication duration. Dimensions of thermal lesions in the pancreas ranged from 12 to 28 mm, 3 to 10 mm, and 5 to 10 mm in length, width, and depth, respectively, as verified through histological analysis of tissue sections. Multiple-baseline reconstruction and respiratory-gated acquisition were demonstrated to be effective strategies in suppressing motion artifacts for clear evolution of temperature profiles during MRTI in the in vivo studies. Conclusions: This study demonstrates the technical feasibility of generating volumetric ablation in pancreatic tissue using endoluminal ultrasound applicators positioned in the stomach lumen. MR guidance facilitates target identification, device tracking/positioning, and treatment monitoring through real-time multislice PRF-based thermometry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Plant-Level Modeling and Simulation of Used Nuclear Fuel Dissolution

Plant-level modeling and simulation of a used nuclear fuel prototype dissolver is presented. Emphasis is given in developing a modeling and simulation approach to be explored by other processes involved in the recycle of used fuel. The commonality concepts presented in a previous communication were used to create a model and realize its software module. An initial model was established based on a theory of chemical thermomechanical network transport outlined previously. A software module prototype was developed with the required external behavior and internal mathematical structure. Results obtained demonstrate the generality of the design approach and establish an extensible mathematical model with its corresponding software module for a wide range of dissolvers. Scale up numerical tests were made varying the type of used fuel (breeder and light-water reactors) and the capacity of dissolution (0.5 t/d to 1.7 t/d). These tests were motivated by user requirements in the area of nuclear materials safeguards. A computer module written in high-level programing languages (MATLAB and Octave) was developed, tested, and provided as open-source code (MATLAB) for integration into the Separations and Safeguards Performance Model application in development at Sandia National Laboratories. The modeling approach presented here is intended to serve as a template for a rational modeling of all plant-level modules. This will facilitate the practical application of the commonality features underlying the unifying network transport theory proposed recently. In addition, by example, this model describes, explicitly, the needed data from sub-scale models, and logical extensions for future model development. For example, from thermodynamics, an off-line simulation of molecular dynamics could quantify partial molar volumes for the species in the liquid phase; this simulation is currently at reach for high-performance computing. From fluid mechanics, a hold-up capacity function is needed for the dissolver device; this simulation is currently at reach for computational fluid mechanics given the existing CAD geometry. From chemical transport phenomena, a simulation of the particle-scale dissolution front is needed to derive an improved solid dissolution kinetics law by predicting the local surface area change; an example was provided in this report. In addition, the associated reaction mechanisms for dissolution are presently largely untested and simplified, hence even a parallel experimental program in reaction kinetics is needed to support modeling and simulation efforts. Last but not least, a simple account of finite rates of solid feed and transfer can be readily introduced via a coupled delayed model. These are some of the theoretical benefits of a rational plant-level modeling approach which guides the development of smaller length and time scale modeling. Practical, and other theoretical benefits have been presented on a previous report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗