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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

CERESMIP: a climate modeling protocol to investigate recent trends in the Earth's Energy Imbalance

The Clouds and the Earth's Radiant Energy System (CERES) project has now produced over two decades of observed data on the Earth's Energy Imbalance (EEI) and has revealed substantive trends in both the reflected shortwave and outgoing longwave top-of-atmosphere radiation components. Available climate model simulations suggest that these trends are incompatible with purely internal variability, but that the full magnitude and breakdown of the trends are outside of the model ranges. Unfortunately, the Coupled Model Intercomparison Project (Phase 6) (CMIP6) protocol only uses observed forcings to 2014 (and Shared Socioeconomic Pathways (SSP) projections thereafter), and furthermore, many of the ‘observed' drivers have been updated substantially since the CMIP6 inputs were defined. Most notably, the sea surface temperature (SST) estimates have been revised and now show up to 50% greater trends since 1979, particularly in the southern hemisphere. Additionally, estimates of short-lived aerosol and gas-phase emissions have been substantially updated. These revisions will likely have material impacts on the model-simulated EEI. We therefore propose a new, relatively low-cost, model intercomparison, CERESMIP, that would target the CERES period (2000-present), with updated forcings to at least the end of 2021. The focus will be on atmosphere-only simulations, using updated SST, forcings and emissions from 1990 to 2021. The key metrics of interest will be the EEI and atmospheric feedbacks, and so the analysis will benefit from output from satellite cloud observation simulators. The Tier 1 request would consist only of an ensemble of AMIP-style simulations, while the Tier 2 request would encompass uncertainties in the applied forcing, atmospheric composition, single and all-but-one forcing responses. We present some preliminary results and invite participation from a wide group of models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Oxalic Acid Adsorption on Rutile: Experiments and Surface Complexation Modeling to 150 °C

Here, we characterize oxalate adsorption by rutile in NaCl media (0.03 and 0.30 m) and between pH 3 and 10 over a wide temperature range which includes the near hydrothermal regime (10–150 °C). Oxalate adsorption increases with decreasing pH (as is typical for anion binding by metal oxides), but systematic trends with respect to ionic strength or temperature are absent. Surface complexation modeling (SCM) following the CD-MUSIC formalism, and as constrained by molecular modeling simulations and IR spectroscopic results from the literature, is used to interpret the adsorption data. The molecular modeling simulations, which include molecular dynamics simulations supported by free-energy and ab initio calculations, reveal that oxalate binding is outer-sphere, albeit via strong hydrogen bonds. Conversely, previous IR spectroscopic results conclude that various types of inner-sphere complexes often predominate. SCMs constrained by both the molecular modeling results and the IR spectroscopic data were developed, and both fit the adsorption data equally well. We conjecture that the discrepancy between the molecular simulation and IR spectroscopic results is due to the nature of the rutile surfaces investigated, that is, the perfect (110) crystal faces for the molecular simulations and various rutile powders for the IR spectroscopy studies. Although the (110) surface plane is most often dominant for rutile powders, a variety of steps, kinks, and other types of surface defects are also invariably present. Hence, we speculate that surface defect sites may be primarily responsible for inner-sphere oxalate adsorption, although further study is necessary to prove or disprove this hypothesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Progress on Plant-Level Modeling and Simulation of Used Nuclear Fuel Dissolution: Startup Condition with Quasi-Developed Flow and Varying Acid Concentration

An update to a previous report is presented here describing the startup condition of a continuous dissolver. The need for a careful implementation of a startup condition is twofold. First, by varing the used fuel composition and various other parameters of the model, it becomes difficult to solve the governing equations accurately or to obtain a converged solution for an arbitrary initial condition. This is evident when coupling the dissolver module with a third-party user simulator. The second impetus for improving the modeling of the startup process is that an improved realism is obtained and the model is more complete and useful for accounting material balances rigorously. The aforementioned previous report on this dissolver module is a required reading for the understanding of this update.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

An effective online data monitoring and saving strategy for large-scale climate simulations

Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk, the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This study proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Finally, our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

SECURED: Simulator-Enhanced Control and Understanding of Reactor systems for cyber-Event Defense

The study discusses a learning approach for analyzing cyber-events in reactor systems using integrated hardware and personal computer simulator models. Key points include the rise in cyber-attacks and their sophistication in industrial control systems (ICS), the necessity for awareness, understanding, resource allocation, and preparation to combat these threats, and the digital transformation of old and new nuclear plants, increasing their exposure to cyber threats. It highlights the cyber vulnerabilities of advanced reactor systems, which rely on digital instrumentation and control for operations and safety functions, making them susceptible to cyber-attacks. The approach involves demonstrating reactor system plant ICS cyber-attacks under various operational conditions utilizing tools like simulator models and hardware-based kits. A strategic solution approach tailored to critical infrastructure is emphasized, along with community engagement for public and government support, adopting effective learning approaches, and the preparation for anticipated future challenges. The presentation concludes with a call to action to address challenges, leverage opportunities, and advance through lesson learning in cybersecurity for nuclear energy systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

Time-integrated activity coefficient estimation for radionuclide therapy using PET and a pharmacokinetic model: A simulation study on the effect of sampling schedule and noise

Purpose: The aim of this study was to investigate the accuracy of PET-based treatment planning for predicting the time-integrated activity coefficients (TIACs). Methods: The parameters of a physiologically based pharmacokinetic (PBPK) model were fitted to the biokinetic data of 15 patients to derive assumed true parameters and were used to construct true mathematical patient phantoms (MPPs). Biokinetics of 150 MBq {sup 68}Ga-DOTATATE-PET was simulated with different noise levels [fractional standard deviation (FSD) 10%, 1%, 0.1%, and 0.01%], and seven combinations of measurements at 30 min, 1 h, and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data using population-based Bayesian parameters to construct predicted MPPs. Therapy simulations were performed as 30 min infusion of {sup 90}Y-DOTATATE of 3.3 GBq in both true and predicted MPPs. Prediction accuracy was then calculated as relative variability v{sub organ} between TIACs from both MPPs. Results: Large variability values of one time-point protocols [e.g., FSD = 1%, 240 min p.i., v{sub kidneys} = (9 ± 6)%, and v{sub tumor} = (27 ± 26)%] show inaccurate prediction. Accurate TIAC prediction of the kidneys was obtained for the case of two measurements (1 and 4 h p.i.), e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 10)%, or three measurements, e.g., FSD = 1%, v{sub kidneys} = (7 ± 3)%, and v{sub tumor} = (22 ± 9)%. Conclusions: {sup 68}Ga-DOTATATE-PET measurements could possibly be used to predict the TIACs of {sup 90}Y-DOTATATE when using a PBPK model and population-based Bayesian parameters. The two time-point measurement at 1 and 4 h p.i. with a noise up to FSD = 1% allows an accurate prediction of the TIACs in kidneys.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Energy Saving Quantification on Ductless Heat Pump (DHP) in Existing Homes

In residential retrofit applications, ductless mini-split heat pumps (DHP) are often reported to have high-energy savings potential, depending on the system they are supplementing or replacing. However, recently, there have been a number of utility studies and analyses indicating these energy savings are not being achieved when the existing system is left in place for backup heating or air conditioning. The Pacific Northwest National Laboratory conducted a three-phase project to help determine which control strategies would have the most energy savings impact in various climate zones around the United States. The first phase developed the standalone simulation model for the PNNL Lab Homes and investigated the energy-saving potential from different control strategies and HVAC system configurations. The second phase focused on conducting experiments in the PNNL Lab Homes, which tested the most promising solutions that were modeled in the first phase. The third phase used the field data to calibrate the simulation model and then extrapolated the results to different climate locations and different building sizes. This report focuses on the third phase of the study, including five major parts: model calibration, parametric model setup, results, a sensitivity analysis of air leakage rate, and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Reducing warpage in a hybrid large-scale additive manufacturing and compression molding process

In recent years, a hybrid manufacturing process, developed by combining extrusion-based large-scale additive manufacturing (AM) and compression molding (CM) techniques, has shown promising outcomes for producing structurally functional parts. The process can be used with both short fiber-reinforced composites and neat polymers and hence, even multi-material parts can be manufactured easily. This process offers the advantages of structural enhancement by having a desired fiber orientation using a large-scale AM process, as well as rapid manufacturing capability using a CM process. In the large-scale AM process, the alignment of fibers in the deposition direction enables significant improvement in the mechanical properties of the manufactured parts. However, the anisotropy resulting from the directional arrangement of fibers also introduces challenges related to warpage in the produced parts. This study aims to identify the causes of warpage and propose strategies to mitigate it. The research involves the use of preforms manufactured through the large-scale AM, which are then combined with the neat resin for CM manufacturing. A finite element-based numerical simulation model is developed, employing a sequentially coupled thermomechanical approach. Through a parametric study using the simulation models, optimization of printing direction and preform geometry is performed to minimize warpage. This contributes to the advancement and wider adoption of AM/CM hybrid manufacturing to produce structurally functional parts.

Jo, Eonyeon↗
DOE OSTI2022

Avista CEF2 Shared Energy Economy: Modeling and Simulation

This research project supported Avista’s Shared Energy Economy (SEE) Project in Spokane’s University District (UDistrict), as funded by the WA Department of Commerce Clean Energy Fund (CEF) Phase 2. The SEE project comprises a microgrid with two campus buildings, two solar photovoltaic generators, and two batteries. The microgrid can operate grid-connected or off-grid. This research project included modeling the buildings and other microgrid components, simulating a transactive energy system, and supporting Avista’s testing of different use cases for the WA Department of Commerce. The main research finding is that buildings can be modeled successfully in a data-driven approach.

14 SOLAR ENERGY↗
DOE OSTI2015

Modeling and simulation of offshore wind farm O&M processes

This paper describes a holistic approach to operation and maintenance (O&M) processes in the domain of offshore wind farm power generation. The acquisition and process visualization is followed by a risk analysis of all relevant processes. Hereafter, a tool was designed, which is able to model the defined processes in a BPMN 2.0 notation, as well as connect and simulate them. Furthermore, the notation was enriched with new elements, representing other relevant factors that were, to date, only displayable with much higher effort. In that regard a variety of more complex situations were integrated, such as for example new process interactions depending on different weather influences, in which case a stochastic weather generator was combined with the business simulation or other wind farm aspects important to the smooth running of the offshore wind farms. In addition, the choices for different methodologies, such as the simulation framework or the business process notation will be presented and elaborated depending on the impact they had on the development of the approach and the software solution. - Highlights: • Analysis of operation and maintenance processes of offshore wind farms • Process modeling with BPMN 2.0 • Domain-specific simulation tool.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fluid Dynamics of Axial Turbomachinery: Blade- and Stage-Level Simulations and Models

The current generation of axial turbomachines is the culmination of decades of experience, and detailed understanding of the underlying flow physics has been a key factor for achieving high efficiency and reliability. Driven by advances in numerical methods and relentless growth in computing power, computational fluid dynamics has increasingly provided insights into the rich fluid dynamics involved and how it relates to loss generation. This article presents some of the complex flow phenomena occurring in bladed components of gas turbines and illustrates how simulations have contributed to their understanding and the challenges they pose for modeling. The interaction of key aerodynamic features with deterministic unsteadiness, caused by multiple blade rows, and stochastic unsteadiness, i.e., turbulence, is discussed. High-fidelity simulations of increasingly realistic configurations and models improved with help of machine learning promise to further grow turbomachinery performance and reliability and, thus, help fluid mechanics research have a greater industrial impact.

Mechanics↗
DOE OSTI2024

Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition

Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pilot projects at intermediate scales are costly and time-consuming. An alternative, the pilot-scale model, relies on high-fidelity numerical simulations, but even these simulations can be computationally prohibitive at larger scales. To overcome these limitations, we propose a scalable, physics-constrained reduced order model (ROM) method. The ROM identifies critical physics modes from small-scale unit components, projecting governing equations onto these modes to create a reduced model that retains essential physics details. We also employ Discontinuous Galerkin Domain Decomposition (DG-DD) to apply ROM to unit components and interfaces, enabling the construction of large-scale global systems without data at such large scales. Here this method is demonstrated on the Poisson and Stokes flow equations, showing that it can solve equations about 15–40 times faster with only ~1% relative error. Furthermore, ROM takes one order of magnitude less memory than the full order model, enabling larger scale predictions at a given memory limitation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to power use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Modeling and simulation of hydrogen behavior in Zircaloy-4 fuel cladding

As a result of corrosion during normal operation in nuclear reactors, hydrogen can enter the zirconium-alloy fuel cladding and precipitate as brittle hydride platelets, which can severely degrade the cladding ductility. Under a heterogeneous temperature distribution, hydrides tend to accumulate in the colder areas, creating local spots of degraded cladding that can favor crack initiation. Therefore, an estimation of the local hydride distribution is necessary to help predict the risk of cladding failure. The hydride distribution is governed by three competing phenomena. Hydrogen in solid solution diffuses under a concentration gradient due to Fick’s law and under a temperature gradient due to the Soret effect. Precipitation of the hydride platelets occurs once the hydrogen solubility limit is reached. A model of these phenomena was implemented in the 3D fuel performance code BISON in order to calculate the hydrogen distribution for arbitrary geometries, such as a nuclear fuel rod, and is now available for BISON users. Simulations have been performed on simple geometries to validate the model and its implementation. The simulations predict that before precipitation occurs, hydrogen tends to accumulate in the colder spots due to the Soret effect. Once the solubility limit is reached, hydrogen precipitates and forms a rim close to the outer edge of the cladding. The simulations also predict that the reactor shut down has little effect on already precipitated hydrides but causes the remaining hydrogen to precipitate homogeneously into hydrides.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Atomistic simulations, mesoscopic modeling, and theoretical analysis of thermal conductivity of bundles composed of carbon nanotubes

We report that the propensity of carbon nanotubes (CNTs) to self-organize into continuous networks of bundles has direct implications for thermal transport properties of CNT network materials and defines the importance of clear understanding of the mechanisms and scaling laws governing the heat transfer within the primary building blocks of the network structures—close-packed bundles of CNTs. A comprehensive study of the thermal conductivity of CNT bundles is performed with a combination of non-equilibrium molecular dynamics (MD) simulations of heat transfer between adjacent CNTs and the intrinsic conductivity of CNTs in a bundle with a theoretical analysis that reveals the connections between the structure and thermal transport properties of CNT bundles. The results of MD simulations of heat transfer in CNT bundles consisting of up to 7 CNTs suggest that, contrary to the widespread notion of strongly reduced conductivity of CNTs in bundles, van der Waals interactions between defect-free well-aligned CNTs in a bundle have negligible effect on the intrinsic conductivity of the CNTs. The simulations of inter-tube heat conduction performed for partially overlapping parallel CNTs indicate that the conductance through the overlap region is proportional to the length of the overlap for CNTs and CNT-CNT overlaps longer than several tens of nm. Based on the predictions of the MD simulations, a mesoscopic-level model is developed and applied for theoretical analysis and numerical modeling of heat transfer in bundles consisting of CNTs with infinitely large and finite intrinsic thermal conductivities. The general scaling laws predicting the quadratic dependence of the bundle conductivity on the length of individual CNTs in the case when the thermal transport is controlled by the inter-tube conductance and the independence of the CNT length in another limiting case when the intrinsic conductivity of CNTs plays the dominant role are derived. Lastly, an application of the scaling laws to bundles of single-walled (10,10) CNTs reveals that the transition from inter-tube-conductance-dominated to intrinsic-conductivity-dominated thermal transport in CNT bundles occurs in a practically important range of CNT length from ~20 nm to ~4 μm.

36 MATERIALS SCIENCE↗