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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of Predictive Models for Advanced Reactor Autonomous Control

Advanced reactor designs including microreactors and small modular reactors will contribute to the clean production of cheap energy, and autonomous control for advanced reactors is an appealing option for reducing cost. However, there is a lack of industry experience applying autonomous control for advanced nuclear reactors. To accelerate the development and industry acceptance of autonomous control software for nuclear reactors, we aim to demonstrate autonomous control of the Purdue University research reactor (PUR-1) using INL-developed model predictive control (MPC) methods. To prepare for this demonstration, data-driven predictive models based on process data collected from PUR-1 have been developed and integrated with MPC and used to control a physics-based model of PUR-1. A data-driven dynamics model and a gated recurrent unit (GRU) network were both trained on process data from PUR-1. The dynamics model was shown to effectively control the reactor model with MPC when provided reactivity as a control variable but failed to control the model through the control rod positions. The GRU network produced more accurate predictions than the dynamics model when evaluated on operational data, and future work will include the evaluation of the GRU network in the controller.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Highlights: • We assessed how well a year-2006 satellite-based LUR model captures historical NO{sub 2}. • We used three methods to estimate annual mean NO{sub 2} during 1990–2005. • We measured their performance using standard LUR validation techniques. • Back-extrapolated 2006 levels captured up to 76% of spatial variability (90–05). Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006–2011, could capture nitrogen dioxide (NO{sub 2}) concentrations during 1990–2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO{sub 2} predictions: (1) ‘do nothing’ (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable ‘year’ in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006–2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R{sup 2} and mean-square error R{sup 2} (MSE-R{sup 2}), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R{sup 2} = 31%) and 80% (2003; MSE-R{sup 2} = 78%) of spatial variability in NO{sub 2} in a given year, and 76% (MSE-R{sup 2} = 72%) averaged over 1990–2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO{sub 2} estimates for Australia during 1990–2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Pre-Shot Simulations of Near-Field and Far-Field Ground Motion for the Source Physics Experiment (SPE) Explosions at the Climax Stock, Nevada National Security Site: SPE3

The Source Physics Experiment (SPE) is designed to improve ground motion prediction capabilities for underground nuclear testing through the development of new physics-based models together with validation via new chemical explosion data. The SPE shots consist of different size and depth chemical explosions in a granite borehole at the Climax Stock on the Nevada National Security Site (NNSS). The third shot in the series (SPE3) will be the same size (1000 kg TNT equivalent) and depth (45 m canister centroid) as the previous shot (SPE2). The main purpose of the SPE3 is to investigate the effects of the damage zone caused by the SPE2 chemical explosion on the SPE3 explosion, keeping all other parameters (e.g. size, depth, explosive type) the same. This SPE3 pre-shot report addresses progress made in simulation of the expected wavefield in the both the near- and the far-field. In this report we consider the far-field to be where seismic wave motion can be characterized by elastic or visco-elastic approximations, in contrast to the near-field where other, often non-linear effects, play a major role. The current simulations build on previous work from the SPE1 and SPE2 shots. The simulations are intended to highlight differences between SPE2 and SPE3 using a variety to approximations intended to mimic the expected effect of the damage zone. The simplest physics of damage zone effects suggest changes in the source region material properties (density, velocity), and in the yield strength (coupling) of the rock, should both impact the far-field signal. We can illustrate this in a simplistic way for P-waves in Figure A using the empirical Mueller-Muphy (1971) far-field explosion model (MM). First if the SPE2 explosion-induced damage reduces velocities and densities without affecting the yield strength or coupling, then by energy conservation the amplitudes for SPE 3 would increase as shown in the top of Figure A. Similarly if the explosion induced damage increased density (say by compacting pore space) the amplitude would decrease. This impedance effect, where the product of the density, wave-velocity, and ground-velocity squared is conserved, is well known. The second and harder effect to quantify, are changes in the energy coupling or yield strength. In the simple MM model this is represented by the coupling factor between cavity radius and yield. If we assume the granite was slightly weakened by SPE2 such that the cavity radius increases slightly then the overall amplitudes of SPE3 decrease relative to SPE2 as shown in the bottom of Figure A. In reality both effects, may exist due to the damage zone, and furthermore we know they are interrelated in complex ways. For S-waves we expect even more complex effects due to motion on joints and faults. For this reason we are developing physics-based near field simulation models coupled to far-field propagation codes to predict the overall effects of changes in the source emplacement region on observed signals. Near-field simulations have been performed using the Eulerian code (GEODYN). An ensemble model, calibrated to match records from past large-scale nuclear events is used. Both 2D axisymmetric (no faults) and fully three-dimensional GEODYN calculations (with /without faults) were conducted in which the damage zones sustained by SPE2 are retained as the initial condition for the SPE3 calculations. The results indicate that the waveform peak amplitudes are slightly decayed since the ensemble model includes no softening mechanism. The pulse widths are reduced, indicating the energy is dissipated more by the damage zone as expected. The dominant motions calculated in all the scenarios are spherically symmetric and non-spherical components are small as seen for the SPE2 calculations. The arrival time of the SPE3 waveform at the boreholes is shorter than SPE2 due to compaction during the previous explosion. As shown in the SPE 1 and 2 observations shear waves are generated, indicating the potential contributions from the induced motion on distributed joints. The effects of joints are evaluated using the Lagrangian hydrocode (GEODYN-L), which was benchmarked against the GEODYN calculations for the ensemble model. A new granite model calibrated for samples from the SPE site is used. The new model includes effects of poroelasticity and water saturation and may be scaled allowing application to both small and large scales. Parametric tests were performed to evaluate the effect of joints within the granite and the results demonstrate that in situ joints introduce anisotropy and significant tangential motions.

58 GEOSCIENCES↗
DOE OSTI2024

Decision-making based on Markov decision process in integrated artificial reasoning framework—Part I: Theory

This paper presents a decision-making framework based on an integrated artificial reasoning framework and Markov decision process (MDP). The integrated artificial reasoning framework provides a physics-based approach that converts system information into state transition models, and the analysis result will be represented by the transition probabilities that can be used with an MDP to find a traceable and explainable optimal pathway. A dynamic Bayesian network (DBN) is well suited for representing the structure of an MDP. The causality information among process variables (or among subsystems) is mathematically represented in a DBN by the conditional probabilities of the node’s states provided different probabilities of the parent node’s states. To define node states in a physically understandable manner, we used multilevel flow modeling (MFM). An MFM follows the fundamental energy and mass conservation laws and supports the selection of process variables that represent the system of interest so that causal relations among process variables are properly captured. An MFM-based DBN supports developing state transition models in an MDP to capture the effect of process variables of system having physical relations. The operators of the target system can capture stochastic system dynamics as multiple subsystem state transitions based on their physical relations and uncertainties coming from component degradation or random failures. We analyzed a simplified exemplary system to illustrate an optimal operational policy using the suggested approach.

Markov decision process↗
DOE OSTI2019

Branching of hydraulic cracks enabling permeability of gas or oil shale with closed natural fractures

While hydraulic fracturing technology, aka fracking (or fraccing, frac), has become highly developed and astonishingly successful, a consistent formulation of the associated fracture mechanics that would not conflict with some observations is still unavailable. It is attempted here. Classical fracture mechanics, as well as current commercial software, predict vertical cracks to propagate without branching from the perforations of the horizontal well casing, which are typically spaced at 10 m or more. However, to explain the gas production rate at the wellhead, the crack spacing would have to be only about 0.1 m, which would increase the overall gas permeability of shale mass about 10,000×. This permeability increase has generally been attributed to a preexisting system of orthogonal natural cracks, whose spacing is about 0.1 m. However, their average age is about 100 million years, and a recent analysis indicated that these cracks must have been completely closed by secondary creep of shale in less than a million years. Here it is considered that the tectonic events that produced the natural cracks in shale must have also created weak layers with nanocracking or microcracking damage. It is numerically demonstrated that seepage forces and a greatly enhanced permeability along the weak layers, with a greatly increased transverse Biot coefficient, must cause the fracking to engender lateral branching and the opening of hydraulic cracks along the weak layers, even if these cracks are initially almost closed. A finite element crack band model, based on a recently developed anisotropic spherocylindrical microplane constitutive law, demonstrates these findings [Rahimi-Aghdam S, et al. (2018) arXiv:1212.11023].

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2013

A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

We develop a parallel EM algorithm for multivariate Gaussian mixture models and use it to perform model-based clustering of a large climate data set. Three variants of the EM algorithm are reformulated in parallel and a new variant that is faster is presented. All are implemented using the single program, multiple data (SPMD) programming model, which is able to take advantage of the combined collective memory of large distributed computer architectures to process larger data sets. Displays of the estimated mixture model rather than the data allow us to explore multivariate relationships in a way that scales to arbitrary size data. We study the performance of our methodology on simulated data and apply our methodology to a high resolution climate dataset produced by the community atmosphere model (CAM5). This article has supplementary material online.

Chen, Wei-Chen↗
DOE OSTI2019

Verification of Reduced Order Modeling Based Uncertainty/Sensitivity Estimator (ROMUSE)

This article presents a number of verification case studies for a recently developed sensitivity/uncertainty code package. The code package, ROMUSE (Reduced Order Modeling based Uncertainty/Sensitivity Estimator) is an effort to provide an analysis tool to be used in conjunction with reactor core simulators, in particular the Virtual Environment for Reactor Applications (VERA) core simulator. ROMUSE has been written in C++ and is currently capable of performing various types of parameter perturbations and associated sensitivity analysis, uncertainty quantification, surrogate model construction and subspace analysis. The current version 2.0 has the capability to interface with the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA) code, which gives ROMUSE access to the various algorithms implemented within DAKOTA, most importantly model calibration. The verification study is performed via two basic problems and two reactor physics models. The first problem is used to verify the ROMUSE single physics gradient-based range finding algorithm capability using an abstract quadratic model. The second problem is the Brusselator problem, which is a coupled problem representative of multi-physics problems. This problem is used to test the capability of constructing surrogates via ROMUSE-DAKOTA. Finally, light water reactor pin cell and sodium-cooled fast reactor fuel assembly problems are simulated via SCALE 6.1 to test ROMUSE capability for uncertainty quantification and sensitivity analysis purposes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient Contingency Analysis in Power Systems via Network Trigger Nodes

Modeling failure dynamics within a power system is a complex and challenging process due to multiple inter-dependencies and convoluted inter-domain relationships. Subject matter experts (SMEs) are interested in understanding these failure dynamics for reducing the impact from future disasters (i.e., losses or failures of power system components, such as transmission lines). Contingency analysis (CA) tools enable such ’what-if’ scenario analyses to evaluate the impacts on the power system. Analyzing all possible contingencies among N system components can be computationally expensive. An important step for performing CA is identifying a set of k ‘trigger’ components, which when failed initially can significantly impact the overall system by causing multiple failures. Currently SMEs focus on identifying these trigger components by running expensive simulations on all possible subsets, which quickly becomes infeasible. Hence finding a relevant set of trigger components (contingencies) rapidly to enable efficient and useful CA is crucial.In a collaboration between computer scientists and power system experts, we propose an efficient method for performing CA by exploiting network inter-dependencies in power system components. First, we construct a network with multiple electric grid infrastructure components and dependencies as connections among them. We reformulate the problem of finding a set of trigger components as a problem of identifying critical nodes in the network, which can cascade power failures through connected nodes and cause significant damage to the network. To guide the practical CA tools, we develop a network-based model with a probabilistic edge-weights setup using intricate domain rules. Then we conduct an empirical study on real power system data in the US for both regional and national levels. Firstly, we use power system datasets for the US to create a national-scale domain-driven model. Secondly, we demonstrate that network-based model outperforms the outputs from a real CA tool and show on average 25 × improved selection of contingencies, thereby showcasing practical benefits to the power experts.

Tabassum, Anika↗
DOE OSTI2020

A general mechanism for gel layer formation on borosilicate glass under aqueous corrosion

Mineral and glass dissolution is a scientific topic deeply investigated but incompletely understood and of a great interest for the geochemical and materials science communities. If the interfacial dissolution/reprecipitation mechanism seems to be applicable to most of silicate minerals, the debate remains open concerning glass. Here we studied two model glasses, a ternary borosilicate (CJ1) and the same glass doped with 4.1 mol% of Al2O3 (CJ2). The two glasses were altered at 90°C, pH 9, and in conditions far and close to saturation with respect to amorphous silica, to determine the initial and residual rates. Moreover, a specific experiment was conducted for a short duration with a solution highly enriched with 18O and 29Si isotopes to understand how passivating gels form. SEM, TEM and ToF-SIMS characterization, along with Monte Carlo simulations were used to understand the rate limiting reactions at play and infer the role of Al. We show that Al yields a slower matrix dissolution in dilute conditions. However, it slows down he formation and the maturation of the passivating gel and favors alteration by partial hydrolysis of Si and Al entities followed by in-situ reorganization/relaxation into a porous network. Unexpectedly, CJ1 experienced both interfacial dissolution/reprecipitation and partial hydrolysis followed by in situ reorganization of the silicate network during the course of a single experiment. This study offers a unified concept that can pave the way for the future development of a predictive kinetic model based on a detailed description of bond breaking and bond forming as a function of glass composition and alteration conditions.

Gin, Stephane↗
DOE OSTI2019

Rational Design of Capacitive Pressure Sensors Based on Pyramidal Microstructures for Specialized Monitoring of Biosignals

Abstract There is an increasing demand for specialized pressure sensors in various applications. Previously, capacitive pressure sensors have been shown to have wide versatility in use, with a high degree of potential tuning possible through manipulating the geometry or material selection of the dielectric layer. However, in order to make sensors that are tunable and predictable, the design and fabrication method first needs to be developed. Presented here is an improved fabrication method to achieve tunable, consistent, and reproducible pressure sensors by using a pyramid microstructured dielectric layer along with a lamination layer. The as‐produced sensor performance is able to match predicted trends. Further, a simple model based on this system is developed and its efficacy is experimentally confirmed. Then, the model to predict a wide range of material and microstructure geometric properties prior to device fabrication is used to provide trends on sensor performance. Finally, it is demonstrated that the new method can be used to targetedly design a pressure sensor for a specific application—in vitro pulse sensing.

Ruth, Sara Rachel Arussy↗
DOE OSTI2017

Behavior of Americium in Simulated Wounds in Nonhuman Primates

An americium solution injected intramuscularly into several nonhuman primates (NHPs) was found to behave differently than predicted by the wound models described in the NCRP Report 156. This was because the injection was made along with a citrate solution, which is known to be more soluble than chlorides, oxides, or nitrates on which the NCRP Report was based. We developed a multi-exponential wound model specific to the injected americium solution based on the retention in the intramuscular sites. The model was coupled with the americium systemic model to interpret the urinary excretion data and assess the intake, and it was determined that the models were adequate to predict early urinary excretion in most cases but unable to predict late urinary excretion. This was attributed to the differences in the systemic handling of americium between humans and nonhuman primates. Furthermore, information on the type of wounds, solubility, particle size, mass, chemical form, etc., should always be considered when performing wound dosimetry.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2021

Machine Learning and Economic Models to Enable Risk-Informed Condition Based Maintenance of a Nuclear Plant Asset

The primary objective of this research is to address challenges in the implementation of risk-informed, condition-based predictive maintenance (PdM), which reduces operating costs while still maintaining the safety and reliability of commercial nuclear power plants (NPPs). To achieve the objective, risk models are being developed by taking advantage of advancements in data analytics, deep learning, machine learning (ML), and artificial intelligence (AI). The notable outcomes presented in the report include ? Development of a ML models using heterogeneous plant process and vibration data collected at different spatial and temporal resolutions from the Salem?s CWS to diagnose a circulating water pump (CWP) failure based on salient fault signatures. The developed diagnostic models are extendable to other faults associated with CWPs and CWP motors given associated fault signatures. ? Development of a natural language processing (NLP) technique to automatically classify the WO data into different categories. The developed NLP technique was validated on independent WO data. This automates the tedious and time-consuming activity of mining and classifying WOs by subject matter experts. ? Estimation of mean time between downtime (i.e., time duration between time instances when 1 or more CWPs are not available) and developed an approach to establish reliability of CWS components using unstructured WO data along with CWS plant process data. ? Formulation of economic model based on Markov chain models. The parameters of associated with the transition rate between different states of Markov chain models were estimated using WO data. The economic model formulation and discussion captures both time-independent and time-dependent parameter variation, leading to risk-informed decision-making.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Turbulence radiation coupling in boundary layers of heavy-duty diesel engines

The lack of accurate submodels for in-cylinder radiation and heat transfer has been identified as a key shortcoming in developing truly predictive, physics-based computational fluid dynamics (CFD) models that can be used to develop combustion systems for advanced high-efficiency, low-emissions engines. Recent measurements of wall layers in engines show discrepancies of up to 100% with respect to standard CFD boundary-layer models. And recent analysis of in-cylinder radiation based on the most recent spectral property databases and high-fidelity radiative transfer equation (RTE) solvers has shown that at operating pressures and exhaust-gas recirculation levels typical of modern heavy-duty compression-ignition engines, radiative emission can be as high as 40% of the wall heat losses, that molecular gas radiation (mainly CO2 and H2O) can be more important than soot radiation, and that a significant fraction of the emitted radiation can be reabsorbed before reaching the walls. That is, radiation not only contributes to heat losses, but also changes the in-cylinder temperature distribution, which in turn affects combustion and emissions. The goal of this research is to develop models that explicitly account for the potentially strong coupling between radiative and turbulent boundary layer heat transfer. For example, for optically thick conditions, a simple diffusion model might be formulated in terms of an absorption-coefficient-dependent turbulent Prandtl number.

Sircar, Arpan↗
DOE OSTI2013

Modeling of contact theories for the manipulation of biological micro/nanoparticles in the form of circular crowned rollers based on the atomic force microscope

This article has dealt with the development and modeling of various contact theories for biological nanoparticles shaped as cylinders and circular crowned rollers for application in the manipulation of different biological micro/nanoparticles based on Atomic Force Microscope. First, the effective contact forces were simulated, and their impact on contact mechanics simulation was investigated. In the next step, the Hertz contact model was simulated and compared for gold and DNA nanoparticles with the three types of spherical, cylindrical, and circular crowned roller type contact geometries. Then by reducing the length of the cylindrical section in the circular crowned roller geometry, the geometry of the body was made to approach that of a sphere, and the results were compared for DNA nanoparticles. To anticipatory validate the developed theories, the results of the cylindrical and the circular crowned roller contacts were compared with the results of the existing spherical contact simulations. Following the development of these contact models for the manipulation of various biological micro/nanoparticles, the cylindrical and the circular crowned roller type contact theories were modeled based on the theories of Lundberg, Dowson, Nikpur, Heoprich, and Hertz for the manipulation of biological micro/nanoparticles. Then, for a more accurate validation, the results obtained from the simulations were compared with those obtained by the finite element method and with the experimental results available in previous articles. The previous research works on the simulation of nanomanipulation have mainly investigated the contact theories used in the manipulation of spherical micro/nanoparticles. However since in real biomanipulation situations, biological micro/nanoparticles of more complex shapes need to be displaced in biological environments, this article therefore has modeled and compared, for the first time, different contact theories for use in the biomanipulation of cylindrical and circular crowned roller shaped micro/nanoparticles. The results of models indicate that the contact model of Hertz achieves the largest amount of deformation for the DNA nanoparticle in cylindrical form and the contact model of Heoprich achieves the largest deformation for the circular crowned roller shaped DNA. Of course, this finding is not always true for the other nanoparticles; and considering the mechanical and environmental characteristics, different results can be obtained. Also, by comparing the deformations of different types of nanoparticles, it was determined that the platelet type nanoparticles display the highest degree of deformation in all the considered models, due to their particular mechanical characteristics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Quantifying and Modeling the Impact of Phase State on the Ice Nucleation Abilities of 2-Methyltetrols as a Key Component of Secondary Organic Aerosol Derived from Isoprene Epoxydiols

Organic aerosols (OAs) may serve as ice-nucleating particles (INPs), impacting the formation and properties of cirrus clouds when their phase state and viscosity are in the semisolid to glassy range. However, there is a lack of direct parameterization between aerosol viscosity and their ice nucleation capabilities. In this study, we experimentally measured the ice nucleation rate of 2-methyltetrols (2-MT) aerosols, a key component of isoprene-epoxydiol-derived secondary organic aerosols (IEPOX-SOA), at different viscosities. These results demonstrate that the phase state has a significant impact on the ice nucleation abilities of OA under typical cirrus cloud conditions, with the ice nucleation rate increasing by 2 to 3 orders of magnitude when the phase state changes from liquid to semisolid. An innovative parametric model based on classical nucleation theory was developed to directly quantify the impact of viscosity on the heterogeneous nucleation rate. This model accurately represents our laboratory measurement and can be implemented into climate models due to its simple, equation-based form. Based on data collected from the ACRIDICON-CHUVA field campaign, our model predicts that the INP concentration from IEPOX-SOA can reach the magnitude of 1 to tens per liter in the cirrus cloud region impacted by the Amazon rainforest, consistent with recent field observations and estimations. This novel parameterization framework can also be applied in regional and global climate models to further improve representations of cirrus cloud formation and associated climate impacts.

2-methyltetrol↗
DOE OSTI2022

Modeling deformation and failure in AlSi-polyester abradable sealcoating material using microstructure-based finite element simulation

A plasma-sprayed aluminum-silicon (AlSi)/polyester coating is applied in modern gas turbine engines as an abradable sealcoating to maintain tight clearances between the rotating blades and the static casing. While running the engine, the rotating blades “rub” with the abradable coating, which results in extreme strain rate (up to 10 6 ) dynamics and a high-temperature environment. Due to the difficulty of collecting direct measurements, predictive computational models are important for analyzing the deformation and failure of the abradable material, to help meet the design target of avoiding damage to the blade tip and maintaining high fuel efficiency. In this research, a microstructure-based finite element (FE) computational model was developed to capture the complex mechanical behavior of the AlSi/polyester microstructure. The model is based on a virtual representative-volume-element (RVE) of a metal-polymer microstructure, reconstructed from x-ray computed tomography. It models the plastic deformation of, and damage to, each AlSi and polyester constituents, as well as the failure at their interface. The model was calibrated and validated with uniaxial tension and compression experiments, conducted at two temperatures (298 K and 533 K) at an applied strain rate of 10 3 - 10 4 s -1 . The material exhibited strongly asymmetric tension-compression behavior and a sensitivity to temperature, which was well captured by the model. The model was further applied to investigate changes in mechanical behavior due to variations in constituents’ volume fractions, which provides guidance to the microstructural design of AlSi/polyester abradable materials. The model is expected to facilitate the development of improved abradable materials by bypassing the conventional trial-and-error approach and extensive testing requirements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Simplified calculation of the area specific impedance for solid-state battery design

Simplified algebraic area specific impedance (ASI) correlations have been developed for solid-state composite battery electrodes made of a single ion conducting electrolyte, conductive additive, and intercalation active material. Two ASI expressions were developed, one for short times ( i.e. , pulsed power operation) and another for the pseudo steady state operation (i.e., sustained discharge for energy estimation). A full electrochemical model based on porous electrode theory was developed to examine the accuracy of the simplified ASI expressions. The simplified expressions agree favorably with full model results over a wide range of parameters (i.e. , electrode thicknesses, electrolyte conductivities, solid-state diffusion coefficients, specific surface areas, etc.) and conditions (i.e. , C-rates, states of charge, and pulse times). Under most conditions, the error between the full model and the correlations is well below 7 %. Higher errors were observed for the pseudo steady state expression at high/ low states of charge where the assumption of uniform reaction distributions loses validity. Here, the short time ASI has higher error at low states of charge due to the nonlinearity of the open circuit voltage equation, which is assumed linear in the formulation of the simplified algebraic expression.

25 ENERGY STORAGE↗
DOE OSTI2017

Electric Field Tuning Molecular Packing and Electrical Properties of Solution-Shearing Coated Organic Semiconducting Thin Films

Recent improvements in solution-coated organic semiconductors (OSCs) evidence their high potential for cost-efficient organic electronics and sensors. Molecular packing structure determines the charge transport property of molecular solids. However, it remains challenging to control the molecular packing structure for a given OSC. Here, the application of alternating electric fields is reported to fine-tune the crystal packing of OSC solution-shearing coated at ambient conditions. First, a theoretical model based on dielectrophoresis is developed to guide the selection of the optimal conditions (frequency and amplitude) of the electric field applied through the solution-shearing blade during coating of OSC thin films. Next, electric field-induced polymorphism is demonstrated for OSCs with both herringbone and 2D brick-wall packing motifs in 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene and 6,13-bis(triisopropylsilylethynyl) pentacene, respectively. Favorable molecular packing can be accessible in some cases, resulting in higher charge carrier mobilities. In conclusion, this work provides a new approach to tune the properties of solution-coated OSCs in functional devices for high-performance printed electronics.

36 MATERIALS SCIENCE↗