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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 19 records

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NASA NTRS1994

A statistical analysis of the dependency of closure assumptions in cumulus parameterization on the horizontal resolution

Simulated data from the UCLA cumulus ensemble model are used to investigate the quasi-universal validity of closure assumptions used in existing cumulus parameterizations. A closure assumption is quasi-universally valid if it is sensitive neither to convective cloud regimes nor to horizontal resolutions of large-scale/mesoscale models. The dependency of three types of closure assumptions, as classified by Arakawa and Chen, on the horizontal resolution is addressed in this study. Type I is the constraint on the coupling of the time tendencies of large-scale temperature and water vapor mixing ratio. Type II is the constraint on the coupling of cumulus heating and cumulus drying. Type III is a direct constraint on the intensity of a cumulus ensemble. The macroscopic behavior of simulated cumulus convection is first compared with the observed behavior in view of Type I and Type II closure assumptions using 'quick-look' and canonical correlation analyses. It is found that they are statistically similar to each other. The three types of closure assumptions are further examined with simulated data averaged over selected subdomain sizes ranging from 64 to 512 km. It is found that the dependency of Type I and Type II closure assumptions on the horizontal resolution is very weak and that Type III closure assumption is somewhat dependent upon the horizontal resolution. The influences of convective and mesoscale processes on the closure assumptions are also addressed by comparing the structures of canonical components with the corresponding vertical profiles in the convective and stratiform regions of cumulus ensembles analyzed directly from simulated data. The implication of these results for cumulus parameterization is discussed.

Xu, Kuan-Man↗

DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗

NASA NTRS1993

The helical decomposition and the instability assumption

Direct numerical simulations show that the triadic transfer function T(k,p,q) peaks sharply when q (or p) is much smaller than k. The triadic transfer function T(k,p,q) gives the rate of energy input into wave number k from all interactions with modes of wave number p and q, where k, p, q form a triangle. This observation was thought to suggest that energy is cascaded downscale through non-local interactions with local transfer and that there was a strong connection between large and small scales. Both suggestions were in contradiction with the classical Kolmogorov picture of the energy cascade. The helical decomposition was found useful in distinguishing between kinematically independent interactions. That analysis has gone beyond the question of non-local interaction with local transfer. In particular, an assumption about the statistical direction of triadic energy transfer in any kinematically independent interaction was introduced (the instability assumption). That assumption is not necessary for the conclusions about non-local interactions with local transfer recalled above. In the case of turbulence under rapid rotation, the instability assumption leads to the prediction that energy is transferred in spectral space from the poles of the rotation axis toward the equator. The instability assumption is thought to be of general validity for any type of triad interactions (e.g. internal waves). The helical decomposition and the instability assumption offer detailed information about the homogeneous statistical dynamics of the Navier-Stokes equations. The objective was to explore the validity of the instability assumption and to study the contributions of the various types of helical interactions to the energy cascade and the subgrid-scale eddy-viscosity. This was done in the context of spectral closures of the Direct Interaction or Quasi-Normal type.

Waleffe, Fabian A.↗

DOE OSTI2022

Evaluation of assumptions made by Hygroscopic Tandem Differential Mobility Analyzer inversion routines

The Hygroscopic Tandem Differential Mobility Analyzer (H-TDMA) measures the hygroscopicity of atmospheric particles, and many atmospheric processes that change this hygroscopicity also change the atmospheric size distribution. Two assumptions made during H-TDMA inversion create spurious hygroscopic trends as a function of the changing inlet size distribution. These two assumptions—that the particles exiting the first Differential Mobility Analyzer (DMA1) are singly charged and that the inlet size distribution has a slope of zero (flat)— generate Multi-Charge Dispersion (MCD) bias and Slope bias, respectively. First, we use a model, named TAO, to show that the inlet size distribution could theoretically change the measured ammonium sulfate hygroscopicity by 10%–20% as a function of diameter or experimental time with no change in relative humidity. Secondly, we show experimentally that aerosol emitted from the flaming combustion of grass creates MCD bias. Here, in this experiment, we measure the CPC response of the first three charges and invert these responses using a new routine named Junior. Junior's inversion of each charge shows that one growth factor distribution describes all measured diameters (no growth dependence on diameter). As in the modeling study above, previous publications of this aerosol system, using traditional inversion assumptions, report a decrease in hygroscopicity as DMA1 diameter increases. Unlike traditional inversions, Junior's inversion does not assume the particles are singly charged nor does it make the flat inlet size distribution assumption. Instead, both the inlet size distribution and each charge's CPC response are measured quantities. Thus, the discrepancy between our inversion results and previous publications is likely due to the traditional inversion routine assumptions. This underscores the importance for accounting for Slope and MCD bias during inversions. Experimental results should be carefully analyzed when reporting hygroscopic trends with respect to diameter or experimental time when using the traditional inversion assumptions.

42 ENGINEERING↗

NASA NTRS1990

Effects of simplifying assumptions on optimal trajectory estimation for a high-performance aircraft

When analyzing the performance of an aircraft, certain simplifying assumptions, which decrease the complexity of the problem, can often be made. The degree of accuracy required in the solution may determine the extent to which these simplifying assumptions are incorporated. A complex model may yield more accurate results if it describes the real situation more thoroughly. However, a complex model usually involves more computation time, makes the analysis more difficult, and often requires more information to do the analysis. Therefore, to choose the simplifying assumptions intelligently, it is important to know what effects the assumptions may have on the calculated performance of a vehicle. Several simplifying assumptions are examined, the effects of simplified models to those of the more complex ones are compared, and conclusions are drawn about the impact of these assumptions on flight envelope generation and optimal trajectory calculation. Models which affect an aircraft are analyzed, but the implications of simplifying the model of the aircraft itself are not studied. The examples are atmospheric models, gravitational models, different models for equations of motion, and constraint conditions.

Kern, Lura E.↗

NASA NTRS2011

Sensitivity of C-Band Polarimetric Radar-Based Drop Size Distribution Measurements to Maximum Diameter Assumptions

The estimation of rain drop size distribution (DSD) parameters from polarimetric radar observations is accomplished by first establishing a relationship between differential reflectivity (Z(sub dr)) and the central tendency of the rain DSD such as the median volume diameter (D0). Since Z(sub dr) does not provide a direct measurement of DSD central tendency, the relationship is typically derived empirically from rain drop and radar scattering models (e.g., D0 = F[Z (sub dr)] ). Past studies have explored the general sensitivity of these models to temperature, radar wavelength, the drop shape vs. size relation, and DSD variability. Much progress has been made in recent years in measuring the drop shape and DSD variability using surface-based disdrometers, such as the 2D Video disdrometer (2DVD), and documenting their impact on polarimetric radar techniques. In addition to measuring drop shape, another advantage of the 2DVD over earlier impact type disdrometers is its ability to resolve drop diameters in excess of 5 mm. Despite this improvement, the sampling limitations of a disdrometer, including the 2DVD, make it very difficult to adequately measure the maximum drop diameter (D(sub max)) present in a typical radar resolution volume. As a result, D(sub max) must still be assumed in the drop and radar models from which D0 = F[Z(sub dr)] is derived. Since scattering resonance at C-band wavelengths begins to occur in drop diameters larger than about 5 mm, modeled C-band radar parameters, particularly Z(sub dr), can be sensitive to D(sub max) assumptions. In past C-band radar studies, a variety of D(sub max) assumptions have been made, including the actual disdrometer estimate of D(sub max) during a typical sampling period (e.g., 1-3 minutes), D(sub max) = C (where C is constant at values from 5 to 8 mm), and D(sub max) = M*D0 (where the constant multiple, M, is fixed at values ranging from 2.5 to 3.5). The overall objective of this NASA Global Precipitation Measurement Mission (GPM/PMM Science Team)-funded study is to document the sensitivity of DSD measurements, including estimates of D0, from C-band Z(sub dr) and reflectivity to this range of D(sub max) assumptions. For this study, GPM Ground Validation 2DVD's were operated under the scanning domain of the UAHuntsville ARMOR C-band dual-polarimetric radar. Approximately 7500 minutes of DSD data were collected and processed to create gamma size distribution parameters using a truncated method of moments approach. After creating the gamma parameter datasets the DSD's were then used as input to a T-matrix model for computation of polarimetric radar moments at C-band. All necessary model parameterizations, such as temperature, drop shape, and drop fall mode, were fixed at typically accepted values while the D(sub max) assumption was allowed to vary in sensitivity tests. By hypothesizing a DSD model with D(sub max) (fit) from which the empirical fit to D0 = F[Z(sub dr)] was derived via non-linear least squares regression and a separate reference DSD model with D(sub max) (truth), bias and standard error in D0 retrievals were estimated in the presence of Z(sub dr) measurement error and hypothesized mismatch in D(sub max) assumptions. Although the normalized standard error for D0 = F[Z(sub dr)r] can increase slightly (as much as from 11% to 16% for all 7500 DSDs) when the D(sub max) (fit) does not match D(sub max) (truth), the primary impact of uncertainty in D(sub max) is a potential increase in normalized bias error in D0 (from 0% to as much as 10% over all 7500 DSDs, depending on the extent of the mismatch between D(sub max) (fit) and D(sub max) (truth)). For DSDs characterized by large Z(sub dr) (Z(sub dr) > 1.5 to 2.0 dB), the normalized bias error for D0 estimation at C-band is sometimes unacceptably large (> 10%), again depending on the extent of the hypothesized D(sub max) mismatch. Modeled errors in D0 retrievals from Z(sub dr) at C-band are demonstrated in detail and comparedo similar modeled retrieval errors at S-band and X-band where the sensitivity to D(sub max) is expected to be less. The impact of D(sub max) assumptions to the retrieval of other DSD parameters such as Nw, the liquid water content normalized intercept parameter, are also explored. Likely implications for DSD retrievals using C-band polarimetric radar for GPM are assessed by considering current community knowledge regarding D(sub max) and quantifying the statistical distribution of Z(sub dr) from ARMOR over a large variety of meteorological conditions. Based on these results and the prevalence of C-band polarimetric radars worldwide, a call for more emphasis on constraining our observational estimate of D(sub max) within a typical radar resolution volume is made

Carey, Lawrence D.↗

DOE OSTI2023

Generalizing the Gurson model using symbolic regression and transfer learning to relax inherent assumptions

Abstract To generate material models with fewer limiting assumptions while maintaining closed-form, interpretable solutions, we propose using genetic programming based symbolic regression (GPSR), a machine learning (ML) approach that describes data using free-form symbolic expressions. To maximize interpretability, we start from an analytical, derived material model, the Gurson model for porous ductile metals, and systematically relax inherent assumptions made in its derivation to understand each assumption’s contribution to the GPSR model forms. We incorporate transfer learning methods into the GPSR training process to increase GPSR efficiency and generate models that abide by known mechanics of the system. The results show that regularizing the GPSR fitness function is critical for generating physically valid models and illustrate how GPSR allows a high level of interpretability compared with other ML approaches. The method of systematic assumption relaxation allows the generation of models that address limiting assumptions found in the Gurson model, and the symbolic forms allow conjecture of decreased material strength due to void interaction and non-symmetric void shapes.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Why is it Doing That? - Assumptions about the FMS

In the glass cockpit, it's not uncommon to hear exclamations such as "why is it doing that?". Sometimes pilots ask "what were they thinking when they set it this way?" or "why doesn't it tell me what it's going to do next?". Pilots may hold a conceptual model of the automation that is the result of fleet lore, which may or may not be consistent with what the engineers had in mind. But what did the engineers have in mind? In this study, we present some of the underlying assumptions surrounding the glass cockpit. Engineers and designers make assumptions about the nature of the flight task; at the other end, instructor and line pilots make assumptions about how the automation works and how it was intended to be used. These underlying assumptions are seldom recognized or acknowledged, This study is an attempt to explicitly arti culate such assumptions to better inform design and training developments. This work is part of a larger project to support training strategies for automation.

Feary, Michael↗

DOE OSTI2024

Different model assumptions about plant hydraulics and photosynthetic temperature acclimation yield diverging implications for tropical forest gross primary production under warming

Tropical forest photosynthesis can decline at high temperatures due to (1) biochemical responses to increasing temperature and (2) stomatal responses to increasing vapor pressure deficit (VPD), which is associated with increasing temperature. It is challenging to disentangle the influence of these two mechanisms on photosynthesis in observations, because temperature and VPD are tightly correlated in tropical forests. Nonetheless, quantifying the relative strength of these two mechanisms is essential for understanding how tropical gross primary production (GPP) will respond to climate change, because increasing atmospheric CO 2 concentration may partially offset VPD-driven stomatal responses, but is not expected to mitigate the effects of temperature-driven biochemical responses. We used two terrestrial biosphere models to quantify how physiological process assumptions (photosynthetic temperature acclimation and plant hydraulic stress) and functional traits (e.g., maximum xylem conductivity) influence the relative strength of modeled temperature versus VPD effects on light-saturated GPP at an Amazonian forest site, a seasonally dry tropical forest site, and an experimental tropical forest mesocosm. By simulating idealized climate change scenarios, we quantified the divergence in GPP predictions under model configurations with stronger VPD effects compared with stronger direct temperature effects. Assumptions consistent with stronger direct temperature effects resulted in larger GPP declines under warming, while assumptions consistent with stronger VPD effects resulted in more resilient GPP under warming. Furthermore, our findings underscore the importance of quantifying the role of direct temperature and indirect VPD effects for projecting the resilience of tropical forests in the future, and demonstrate that the relative strength of temperature versus VPD effects in models is highly sensitive to plant functional parameters and structural assumptions about photosynthetic temperature acclimation and plant hydraulics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact

As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.

Prakash, Anand Krishnan↗

DOE OSTI2021

Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models

Background: Models can help guide colorectal cancer screening policy. Although models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance. Methods: We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6 mm), 0.85 for small adenomas (6–10 mm), 0.95 for large adenomas (≥10 mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. In the study, we examined how well the model predicted outcomes measured over a three-year follow-up period, including the number of adenomas detected, the size of the largest adenoma detected, and incident colorectal cancer. Results: Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%–50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%–17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10- to 20-mm adenomas, and 0.98 for adenomas 20 mm and larger. Conclusions: Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations. Impact: Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may affect the predicted effectiveness of colorectal cancer screening. When possible, decision analyses used to inform policy should address these uncertainties.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Site 300 B-851 Permit: Radiological Assumptions and Source Term - Air Dispersion Modeling for Radiological Dose Assessment

The following modeling assumptions and source term that is provided in the attachments to this document are meant to serve as a guide to the presentation delivered on April 23rd, 2020, to the San Joaquin Air District Permit Services slides. This analysis is in response to the District’s “Project Incompleteness Letter” (Facility Number: N-472 and Project Number: N-1173492) regarding the potential for depleted uranium (DU) to be resuspended from areas of contaminated soil at Site 300 as a result of high explosive (HE) experiments increased to a requested permitted maximum of 1,000 pound detonations with an annual HE throughput not to exceed 7,500 pounds per year. LLNL has taken every step in the radiological health risk concerns in the modeling assumptions and final source term to be at a level of maximum conservativeness. As an example, forty soil samples were taken in the B-851 area (LLNL-AR-738708 – 2018) and assayed for uranium-234, uranium-235, and uranium-238 concentrations. The maximum uranium soil concentrations were used in the assumptions (potential for resuspended dust occurring from detonation experiments) and not the use of median uranium soil concentrations – a more realistic outcome. Additionally, these uranium soil concentrations were considered to be at 100% uptake in the vegetation for prescribed burns; vegetation uptake factors were not used as that would lower the final source term. This level of conservativeness was maintained throughout the modeling assumptions.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

NASA NTRS1994

Further evidence for the EPNT assumption

We recently proved a theorem extending the Greenberger-Horne-Zeilinger (GHZ) Theorem from multi-particle systems to two-particle systems. This proof depended upon an auxiliary assumption, the EPNT assumption (Emptiness of Paths Not Taken). According to this assumption, if there exists an Einstein-Rosen-Podolsky (EPR) element of reality that determines that a path is empty, then there can be no entity associated with the wave that travels this path (pilot-waves, empty waves, etc.) and reports information to the amplitude, when the paths recombine. We produce some further evidence in support of this assumption, which is certainly true in quantum theory. The alternative is that such a pilot-wave theory would have to violate EPR locality.

Greenberger, Daniel M.↗

NASA NTRSDate not supplied

Sensitivity of Hybrid NEP-Chemical Vehicle Mass to Assumptions for Crewed Opposition-Class Mars Missions

This research illustrates the impact of different technology choices and their associated performance on overall sizing for a hybrid Nuclear Electric Propulsion (NEP) and chemical crewed Mars transportation vehicle. A set of models for the trajectory and vehicle mass, power system specific mass(α), and power conversion performance enable the analysis. Vehicle mass required to close the mission is calculated as a function of α and specific impulse (Isp) for multiple choices of high-level assumptions including system alpha vs. power scaling and electric thruster type. System alpha is built up and compared for multiple cases of technology assumptions. A Brayton cycle model is used to illustrate the alpha sensitivities to assumptions in cycle design such as peak operating temperature and component performance. The analysis illustrates how technology performance sensitivities affect the alpha and thus vehicle sizing for a range of mission and technology assumptions.

Matthew Duchek↗

NASA NTRSDate not supplied

Logistics Rates and Assumptions for Future Human Spaceflight Missions Beyond LEO

As NASA prepares for future human spaceflight missions with extended crew duration in destinations beyond low Earth orbit (LEO), the Agency has focused itself on understanding the drivers to sustainably support human life beyond Earth’s atmosphere. Future missions to deep space, the lunar surface, and eventually the Martian surface pose new challenges in ensuring the crew is sufficiently supplied with all necessary materials. To mitigate the risk of not delivering sufficient consumables and logistics for human spaceflight missions, NASA has examined past human space mission data and developed metabolic modeling to determine estimates for the crew consumption rates of fluids, solid consumables, and additional equipment needed. This paper is a compilation of guidelines, rates, and assumptions necessary to evaluate the logistics needs for future human exploration conceptual missions beyond LEO, providing a starting point and resource of information regarding usage rates and overall logistics supply planning for crewed exploration missions. Logistics represent all equipment and supplies not installed as part of the vehicle that are needed to support mission activities. Logistics can be further divided into specific categories, including consumables, maintenance items, spares, utilization, outfitting, as well as any packaging required. This paper will also provide use case examples of logistics needs to support human missions in deep space, including a conceptual lunar surface mission. The paper provides information necessary to calculate the mass and volume of known logistics for conceptual future human exploration missions beyond LEO. The assumptions in the paper are updated versions of previous assumptions made by the Agency and were derived from a number of sources, including International Space Station (ISS) historical usage and resupply rates, the Life Support Baseline Values and Assumptions Document (BVAD) 2022, Human Integration Design Handbook (HIDH) 2014, and data gathered from NASA human spaceflight programs and projects. The primary goal of the paper is to establish a set of consistent reference rates that multiple teams and groups can utilize to conduct logistics analysis and compare cases. This methodology is for initial estimates of conceptual human missions and does not take the place of detailed analysis for programs, nor does it provide requirements for programs.

Human Spaceflight↗

NASA NTRSDate not supplied

Logistics Rates and Assumptions for Future Human Spaceflight Missions Beyond LEO

Human Spaceflight↗

NASA NTRSDate not supplied

Assumption Generation for the Verification of Learning-Enabled Autonomous Systems

Providing safety guarantees for autonomous systems is difficultas these systems operate in complex environments that require the use of learning-enabled components, such as deep neural networks (DNNs) for visual perception. DNNs are hard to analyze due to their size (they can have thousands or millions of parameters), lack of formal specifications (DNNs are typically learnt from labeled data, in the absence of any formal or informal requirements), and sensitivity to small changes in the environment. We present an assume-guarantee style compositional approach for the formal verification of system-level safety properties of such autonomous systems. Our insight is that we can analyze the system in the absence of the DNN perception components by automatically synthesizing assumptions on the DNN behaviour that guarantee the satisfaction of the required safety properties. The synthesized assumptions are the weakest in the sense that they characterize the output sequences of all the possible DNNs that, plugged into the autonomous system, guarantee the required safety properties. The assumptions can be leveraged as run-time monitors over a deployed DNN to guarantee the safety of the overall system; they can also be mined to extract local specifications for use during training and testing of DNNs. We illustrate our approach on a case study taken from the autonomous airplanes domain that uses a complex DNN for perception

Autonomous systems↗

DOE OSTI2024

Consequences of plane-strain and plane-stress assumptions in fully coupled chemo-mechanical Li-ion battery models

In Li-ion battery research, it is common to simulate chemo-mechanical phenomena in reduced dimensions (e.g., 2-D) as opposed to fully resolve these complex physics in 3-D. It is common to assume either (1) the out-of-plane strain is negligible (commonly referred to as plane-strain), or (2) the out-of-plane stress is negligible (commonly referred to as plane-stress). However, there is typically little consideration as to the quantitative consequences of these approximations. Furthermore, the influence of these out-of-plane assumptions can be compounded and convoluted when chemo-mechanics models implement so-called “fully coupled” formulations, where the local species concentrations influence the stress-state and the stress-state influences the local species fluxes. Here, the present manuscript explores the implications of using plane-stress and plane-strain assumptions in 2-D as compared to simulating a full 3-D electrode particle. This comparative study includes simulating both isotropic and anisotropic particle intercalation where the particles can be surrounded by either a liquid or solid electrolyte. Additionally, common Li-ion battery-model metrics such as the state-of-stress, intercalation fraction distribution, and specific capacity are compared, while also considering the effects of particle size and C-rate. As alternatives to the pure plane-strain and plane stress approximations, two modified plane-strain assumptions are found to better approximate the fully coupled chemo-mechanical 3-D behavior.

25 ENERGY STORAGE↗