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

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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model

Understanding the dynamics of circulating tumor cell (CTC) behavior within the vasculature has remained an elusive goal in cancer biology. To elucidate the contribution of hydrodynamics in determining sites of CTC vascular colonization, the physical forces affecting these cells must be evaluated in a highly controlled manner. To this end, we have bioprinted endothelialized vascular beds and perfused these constructs with metastatic mammary gland cells under physiological flow rates. By pairing these in vitro devices with an advanced computational flow model, we found that the bioprinted analog was readily capable of evaluating the accuracy and integrated complexity of a computational flow model, while also highlighting the discrete contribution of hydrodynamics in vascular colonization. This intersection of these two technologies, bioprinting and computational simulation, is a key demonstration in the establishment of an experimentation pipeline for the understanding of complex biophysical events.

42 ENGINEERING↗
DOE OSTI2020

Impact Modeling and Testing of Pouch and Prismatic Cells

Understanding battery response under impact is critical to improve the safety of electrified vehicles. Nevertheless, predicting the impact behaviors of batteries is not straightforward since a battery cell usually contains hundreds of thin layers with dramatically different material properties and multiple physical processes occur simultaneously during cell deformation. Here we utilized both empirical tests and numerical models to capture the failure process of pouch and prismatic cells in various impact scenarios. In each test, a cell was hit once by an indenter dropped from a certain height. During which the cell penetration, loading force, voltage and temperature were monitored to characterize the cell’s response. Meanwhile, numerical models were developed to capture the coupled mechanical, electrical, electrochemical and thermal responses of batteries. In these models, the cell bulk was treated as a homogeneous part to achieve computational efficiency required by large-scale simulations, and it was represented by the geologic cap model that allows both shear and compaction deformation. Simulation results showed agreement with experimental data in essential features of cell behaviors during impact. Details of the test setup, model development and cell failure behaviors are presented in this paper. Additionally, capabilities, limitations and future improvement of the battery safety modeling are discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

TRUST Contact Thermal Conductance (TRUST-CTC) Report: FY24

The objective of the Delivery Environments (DE) Testbeds to Reduce Uncertainties in Simulations and Tests (TRUST) project is to quantify and help increase confidence in specific areas of computational and experimental capabilities that are applicable to current and future delivery environments. More complete quantification of confidence in experimental and computational capabilities and the sufficient increase of confidence in those capabilities is critical to improving weapons engineering design, qualification, and assessment efforts that are critical to the current and future stockpile. Staff development will include cross-discipline collaboration to provide engineers with experience in both numerical simulations and experimental methods. This work uses and provides feedback on analysis tools and experimental results databases for efficient and responsive engineering which are currently under development. TRUST currently includes five testbeds and their associated engineering analysis baseline models (EABMs): 1. contact thermal conductivity, (CTC) 2. nonlinear dynamics, (ND) 3. sensors in environments for accelerometers, (SEA) 4. sensors in environments for fiber optic displacement gages, and (SEFOD) 5. sensors in environments for thermocouples (SETC). The TRUST project uses single-feature testbeds to quantify uncertainties in specific models and experiments and to identify capability development needs that can help to reduce these uncertainties. Each testbed is designed, configured, and tested in collaboration with groups with design and experimental capability: E-14 and MPA-CINT. The complementary simulations are conducted using W-13 analysis tools and stored in model repositories with plans for incremental progress toward EABM requirements. W-13 extends and exercises the testbed simulations in collaboration with experimentalists for uncertainty quantification of current and future materials, geometries, and environments. Additionally, TRUST is intended to provide engineers in W-13 and E-14 with experience in both numerical simulations and experimental methods through cross-discipline collaborations. Following the introduction to the TRUST project, the remainder of this report focuses on experimental and analytical efforts conducted in Fiscal Year (FY) 2024 relevant to the TRUST Contact Thermal Conductance (CTC) testbed.

42 ENGINEERING↗
DOE OSTI2021

Flow and heat transfer in the wake of a triangular arrangement of spheres

This research work seeks to investigate the influence of spacing and heat transfer on the wake behavior of a triangular arrangement of spheres. Four experimental configurations have been investigated at three Reynolds numbers, Re 1 = 350, Re 2 = 700, and Re 3 = 1050. Two isothermal cases were investigated with spacing between the spheres of zero and one sphere diameter, and two cases were investigated with an applied heat flux at the same spacing conditions. The time resolved particle image velocimetry results revealed various flow phenomena including flow separations, von Karman vortex shedding, and Kelvin–Helmholtz instabilities. The turbulent statistics reveal the effect of proximity and heat transfer on the time averaged values of the wake size, turbulent strengths, and Reynolds shear stress in the wake of each sphere, namely, the laminarization effects from the addition of heat and the suppression of the lead sphere wake from the proximity of the trailing spheres. These results are complemented by the application of proper orthogonal decomposition (POD) to the flow fields, which extracts the coherent structures from the flow. Here the modes that describe the coherent structures are extracted and described in detail, which provide further insight into effects of the experimental conditions on the temporal behavior of the flow. Many of the low order modes are found to be associated in pairs, corresponding to asymmetric structures or advection of a given structure downstream. The capability of POD to produce reduced order models of the flow is then utilized to facilitate vortex identification analysis. A turbulent kinetic energy based mode truncation criteria, which has been found to enhance vortex identification capability, is applied to select the POD modes and temporal coefficients to be used in the reduced order modeling. The reconstructed velocity fields are then analyzed with vortex identification algorithms to extract the vortex cores and boundaries. The combination of these approaches allows the study of the effect of proximity and heat transfer on the vortex characteristics, such as size, strength, and distribution.

42 ENGINEERING↗
DOE OSTI2024

Sensitivity Analyses of Natural Convection in the AP1000 Passive Containment Cooling System Following LBLOCA Using CFD

The AP1000 power plant has implemented multiple layers of passive safety systems to enhance reactor safety and ensure system integrity during postulated scenarios, such as loss of coolant accident (LOCA) and main steam line break (MSLB). Among these, the passive containment cooling system (PCCS) is a safety-related system designed to prevent the containment from exceeding the design limits of both pressure and temperature following a postulated design basis accident, by transferring heat from the steel containment vessel to the atmosphere. During LOCA, natural air convection plays a vital role in removing heat from the steel containment vessel to the atmosphere. This is integrated with the condensate film from the Passive Containment Cooling Water Storage Tank (PCCWST). Air natural convection is also the only heat removal mechanism after the dry-out of the PCCWST and during the loss of shutdown decay heat removal (LOSDHR) event. Therefore, it is essential to investigate the thermal hydraulics behavior of the containment under transient conditions to ensure its safety and integrity. This paper presents a simplified CFD/ANSYS FLUENT model developed to analyze the impact of different parameters, such as air relative humidity, air temperature, and steel containment temperature, on the natural convection capability to remove the decay heat following LOCA. The model aims to examine the thermal behavior of the containment and provides a comprehensive understanding of the system's natural convection capability during postulated accidents. The results obtained from the model can be used to improve the safety and integrity of the containment system and provide valuable insights for future research in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Predicting extreme anisotropy and shape variations in impact testing of tantalum single crystals

We report recent Taylor cylinder impact tests carried out for Ta single crystals showed strong variations in dimensional changes for different crystallographic directions aligned with the cylindrical axis. In order to capture the effect of crystallography on the deformation characteristics and final shapes of the impacted cylinders, a single crystal material subroutine is adapted and embedded in the solid mechanics/dynamics Finite Element solver Abaqus to simulate the aforementioned single crystal Ta Taylor impact experiments. Details of the coupled model implementation, and insights on the role played by single crystal anisotropic flow on the deformation behavior across a broad range of strain rates and temperatures for different single crystal orientations are presented and discussed. We demonstrate the predictive capability of the adopted crystal plasticity model to capture the significant role played by crystal orientation-induced anisotropy, as well as strain hardening and adiabatic heating, on the dynamic deformation response of crystalline materials. This re-emphasizes the need of microstructure-aware models to improve the accuracy of simulations for high-consequence engineering design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗
DOE OSTI2024

Light Output Fitting Software

Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

organic↗
DOE OSTI2020

Estimating Sediment Settling Velocities from a Theoretically Guided Data-Driven Approach

Sediment settling velocities are commonly estimated from analytical or process-based approaches. These approaches have theoretical constraints due to the incompletely resolved settling physics. A parametric data-driven approach was recently proposed without theoretical constraints, but it is limited by its mathematical assumptions. To overcome these limitations, here we apply a machine learning algorithm to an aggregated sediment settling experimental database and develops a nonparametric data-driven model to estimate the noncohesive sediment settling velocity in water. A cross-comparison against five process-based equations and a parametric data-driven equation demonstrates the higher accuracy and better consistency of the new model in estimating sediment settling velocities under various physical regimes. The new model also shows an easily implemented self-update capability by assimilating theoretical data derived from the process-based equations. The updated model, incorporating experimental and theoretical data of sediment settling processes, further improves the accuracy and reduces the uncertainty in estimating sediment settling velocities. This study demonstrates the capability of machine learning in sediment transport study and illustrates an alternative framework for other hydraulic engineering challenges.

42 ENGINEERING↗
DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗
DOE OSTI2024

Impact of Hydrological Data on Power System Operational Studies: Preprint

Hydropower is expected to play an important role in maintaining grid reliability and flexibility as the share of of variable renewable energy increases. While the current hydropower operational models have been studied and used widely, they haven't been updated for decades to meet new performance standards. For example, current steady state and dynamic models often neglect hydrological conditions, which may lead to unrealistic expectations when relying on hydropower for energy and ancillary services. To study this impact, a multi-timescale hydrological model was created by leveraging the National Renewable Energy Laboratory-developed Multi-timescale Integrated Dynamics and Scheduling (MIDAS) tool. Using MIDAS, we compare the impact of considering hydrological conditions in a day-ahead unit commitment (DAUC) schedule on the reduced 240-bus Western Interconnect (WI) test system under winter and summer case studies. We show that neglecting current hydrological conditions of hydropower plants in power system models can lead to an overestimation of hydropower capabilities, which could lead to power balancing issues. For example, power system DAUC simulation results of our reduced test system show that in the case where hydrological conditions are not considered in the model, an approximate 31% overestimation of hydropower capabilities occurs in the summer case and approximately 60% occurs in the winter case compared to what is available. Additionally, results show an underestimation of WI day-ahead power system generation costs by approximately $54M - $80M in the weekly summer scenario and $116M - $126M in the weekly winter scenario. This analysis helps to underscore the importance of considering hydrological data in power system operational studies.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗
DOE OSTI2024

The Impact of the Halden Reactor Project on the Development of the Bison Fuel Performance Code

The Bison fuel performance code has been under development at Idaho National Laboratory by various programs within the U.S. Department of Energy since 2009. As part of any fuel performance code development, validation is necessary. To this end, several Halden experiments have been used. Early on, the focus was on validating the thermal mechanical behaviour, including pellet cladding mechanical interaction and fission gas release models implemented in Bison. Support for design-basis accident capabilities began in approximately 2014. Also in 2014, the development of models for accident tolerant fuel (ATF) concepts also began. This includes dopedUO2 fuel for which Halden has some experiments with temperature and fission gas release measurements. The Bison team has been fortunate to have two senior developers be secondees to Halden for a year: the late Dr. Giovanni Pastore from May 2015 to April 2016 and the now retired Dr. Richard Williamson in 2018. The paper will provide examples of Bison validation using Halden data for standard UO2 fuel under normal operation and loss-of-coolant accident condition, and ATF concepts. A tribute to the legacy that both the late Giovanni and retired Richard left on the Bison code is provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Modeling inter‐reader variability in clinical target volume delineation for soft tissue sarcomas using diffusion model

Abstract Background Accurate delineation of the clinical target volume (CTV) is essential in the radiotherapy treatment of soft tissue sarcomas. However, this process is subject to inter‐reader variability due to the need for clinical assessment of risk and extent of potential microscopic spread. This can lead to inconsistencies in treatment planning, potentially impacting treatment outcomes. Most existing automatic CTV delineation methods do not account for this variability and can only generate a single CTV for each case. Purpose This study aims to develop a deep learning‐based technique to generate multiple CTV contours for each case, simulating the inter‐reader variability in the clinical practice. Methods We employed a publicly available dataset consisting of fluorodeoxyglucose positron emission tomography (FDG‐PET), x‐ray computed tomography (CT), and pre‐contrast T1‐weighted magnetic resonance imaging (MRI) scans from 51 patients with soft tissue sarcoma, along with an independent validation set containing five additional patients. An experienced reader drew a contour of the gross tumor volume (GTV) for each patient based on multi‐modality images. Subsequently, two additional readers, together with the first one, were responsible for contouring three CTVs in total based on the GTV. We developed a diffusion model‐based deep learning method that is capable of generating arbitrary number of different and plausible CTVs to mimic the inter‐reader variability in CTV delineation. The proposed model incorporates a separate encoder to extract features from the GTV masks, leveraging the critical role of GTV information in accurate CTV delineation. Results The proposed diffusion model demonstrated superior performance with the highest Dice Index (0.902 compared to values below 0.881 for state‐of‐the‐art models) and the best generalized energy distance (GED) (0.209 compared to values exceeding 0.221 for state‐of‐the‐art models). It also achieved the second‐highest recall and precision metrics among the compared ambiguous image segmentation models. Results from both datasets exhibited consistent trends, reinforcing the reliability of our findings. Additionally, ablation studies exploring different model structures and input configurations highlighted the significance of incorporating prior GTV information for accurate CTV delineation. Conclusions The proposed diffusion model successfully generates multiple plausible CTV contours for soft tissue sarcomas, effectively capturing inter‐reader variability in CTV delineation.

Dong, Yafei [Yale Biomedical Imaging Institute Yal↗
DOE OSTI2024

A variational phase-field framework for thermal softening and dynamic ductile fracture

A variational phase field model for dynamic ductile fracture is presented. Here, the model is designed for elasto-viscoplastic materials subjected to rapid deformations in which the effects of heat generation and material softening are dominant. The variational framework allows for the consistent inclusion of plastic dissipation in the heat equation as well as thermal softening. It employs a coalescence function to degrade fracture energy during regimes of high plastic flow. A variationally consistent form of the Johnson–Cook model is developed for use with the framework. Results from various benchmark problems in dynamic ductile fracture are presented to demonstrate capabilities. In particular, the ability of the model to regularize shear band formation and subsequent damage evolution in two- and three-dimensional problems is demonstrated. Importantly, these phenomena are naturally captured through the underlying physics without the need for phenomenological criteria such as stability thresholds for the onset of shear band formation.

42 ENGINEERING↗
DOE OSTI2020

MPACT Verification With Magnox Reactor Neutronics Progression Problems

MPACT is a state-of-the-art core simulator designed to perform high-fidelity analysis using whole-core, three-dimensional, pin-resolved neutron transport calculations on modern parallel computing hardware. MPACT was originally developed to model light water reactors, and its capabilities are being extended to simulate gas-cooled, graphite-moderated cores such as Magnox reactors. To verify MPACT’s performance in this new application, the code is being formally benchmarked using representative problems. Progression problems are a series of example models that increase in complexity designed to test a code’s performance. The progression problems include both beginning-of-cycle and depletion calculations. Reference solutions for each progression problem have been generated using Serpent 2, a continuous-energy Monte Carlo reactor physics burnup calculation code.Using the neutron multiplication eigenvalue ke as a metric, MPACT’s performance is assessed on each of the progression problems. Initial results showed that MPACT’s multi-group cross section libraries, originally developed for pressurized water reactor problems, were not sufficient to accurately solve Magnox problems. MPACT’s improved performance on the progression problems is demonstrated using this new optimized cross section library.

Luciano, Nicholas↗
DOE OSTI2025

The Atacama Cosmology Telescope: DR6 power spectrum foreground model and validation

We discuss the model of astrophysical emission at millimeter wavelengths used to characterize foregrounds in the multi-frequency power spectra of the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6), expanding on Louis et al. (2025) (2503.14452). We detail several tests to validate the capability of the DR6 parametric foreground model to describe current observations and complex simulations, and show that cosmological parameter constraints are robust against model extensions and variations. We demonstrate consistency of the model with pre-DR6 ACT data and observations from Planck and the South Pole Telescope. We evaluate the implications of using different foreground templates and extending the model with new components and/or free parameters. In all scenarios, the DR6 ΛCDM and ΛCDM+N eff cosmological parameters shift by less than 0.5σ relative to the baseline constraints. Some foreground parameters shift more; we estimate their systematic uncertainties associated with modeling choices. From our constraint on the kinematic Sunyaev-Zel'dovich power, we obtain a conservative limit on the duration of reionization of Δz rei < 4.4, assuming a reionization midpoint consistent with optical depth measurements and a minimal low-redshift contribution, with varying assumptions for this component leading to tighter limits. Finally, we analyze realistic non-Gaussian, correlated microwave sky simulations containing Galactic and extragalactic foreground fields, built independently of the DR6 parametric foreground model. Processing these simulations through the DR6 power spectrum and likelihood pipeline, we recover the input cosmological parameters of the underlying cosmic microwave background field, a new demonstration for small-scale CMB analysis. These tests validate the robustness of the ACT DR6 foreground model and cosmological parameter constraints.

CMBR experiments↗
DOE OSTI2020

Reduced Models for Chemical Kinetics derived from Parallel Ensemble Simulations of Stirred Reactors

Detailed chemical kinetic models are essential to improve the predictive capability of computational fluid dynamics (CFD) simulations and to impact the development of next generation combustion technologies. However, such detailed kinetic models are beyond the reach of CFD codes due to the computational cost of transporting additional chemical degrees of freedom. Reduced order models for combustion kinetics exploit the presence of lower dimensional manifolds in the state space and require far fewer number of scalars to be transported as part of CFD simulations. In this paper, we describe the software infrastructure developed to allow large ensembles of stirred reactor configurations with detailed chemical kinetics. The canonical reactor simulations are incorporated in a workflow with sparse grid and data reduction methodologies aimed at development of reduced order models. We present results from analyzing the results of the stirred reactor calculations for syngas mixture using principal component analysis (PCA) and neural network methodologies. The results show that nonlinear autoencoder model can surpass PCA in representing the original dataset with far fewer degrees of freedom.

Zhang, Pei↗